www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 1
IJEEE, Vol. 1, Spl. Issue 1 (March 2014) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
A Review of different Routing Protocols for Ad
Hoc Mobile Wireless Networks
1Maninder singh,
2Varun Marwaha
1,2Electronics & Communication Engineering Department,
Indo Global College of Engineering, Punjab, India [email protected], [email protected]
Abstract: In this paper we review basics of wireless
communication its features, characteristic, fundamental
,applications etc along with its area of research , scope of
improvement and prominent attributes. Wireless
communication is not aimed to just general communication
as in multimedia services but have found underlying use in
industrial application and is embedded with different
hardware equipment for robotic application . Current
wireless multimedia services , for example, may include
2G,3G and 4G cellular radios, Wi-Fi and Bluetooth
technologies. Another category of which use wireless
technique is in VANET i.e vehicular Ad hoc Networks
,iMANET i.e. internet based mobile adhoc networks
MANET i.e. mobile ad hoc network. We will also discuss
about active research topic mainly MANET in wireless such
as improving and achieving high data rates on given width
of channel, improving quality of signal by controlling and
limiting losses and errors produced as a result of scattering,
interference and multipath propagation.
Keywords: iMANET, Wi-Fi, 4G, Bluetooth, Ad hoc.
I. INTRODUCTION
Wireless communication is activity of conveying
information between two or more points or terminals
without having any physical connection between them i.e.
medium is either air or vaccum. In actual communication
takes place with help of electromagnetic waves and strength
and frequency of electromagnetic waves depends upon
number of factors as on distance between two points,
environmental factors etc.Claude chappe of france was first
to design practical system for communication purpose
called semaphore system, conveyed information through
visual singnals. Then era of wired communication came
Paul Schilling pioneer of electrical telegraph , Alexander
Graham Bell inventor of first practical telephone.
Giant leap in communication was in 1895 ,when Italian
inventor Guglielmo Marconi successful in making first radio
telegraph or wireless telegraph.in 1906 first WARC i.e.
world administrative radio conference was held in order to
coordinate different inventions in wireless technology. 1915
first wireless voice transmission between new York and san
Fransco took place. Marconi discovered short waves in
1920 , which got reflected back from ionosphere . Edwin H
Armstrong 1933 discovered frequency modulation. In 1946,
First interconnection of mobile users to public switched
telephone network (PSTN) was established. Major
reformation In wireless technology took place in 80‘s and
90‘s ,with introduction of Advanced Mobile Phone System
(AMPS) in 1983 deployed in US using duplex channels.
Followed by introduction of packet switched services i.e.
SMS and GPRS in early 90‘s along with SPREAD
SPECTRUM techniques like CDMA. Overview in this
portion we are going to take general idea of different
prominent wireless techniques.
2G
2g signify second generation wireless technology. 2g
working parameters are defined by European
Telecommunications Standards Institute (ETSI) i.e. protocol
, frequency range etc. it was followed by 2.5g which in
addition contained packet switching technology.
3G
3g signify third generation wireless technology. 3g provide
higher data rates as compare to 2g.It working technology is
spread spectrum mainly CDMA. Current 3G systems have
been established through ITU‘s project on International
MobileTelecommunications 2000 (IMT-2000).
4G
4g signify fourth generation wireless technology. 4g provide
higher data rates as compare to 3g.It is new technology and
deployed only in few counties due to backward
compatibility problems.Wimax (Worldwide Interoperability
for Microwave Access) is example of 4g technology .
These technologies are much familiar to everyone as
compare to technology like MANET , InVANET.
Let us discuss these technology briefly one by one .
MANET A mobile ad hoc network is abbreviate as
MANET. In this technology a temporary wireless network is
established which configure itself to perform particularly
task. Each device in a MANET is free to move
independently in any direction, and will therefore change its
links to other devices. Ex Bluetooth .
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InVANETIntelligent vehicular ad-hoc network (InVANET)
is another term for promoting vehicular networking.
InVANET technology is best example is Wi-Fi .
II. PERFORMANCE EVALUATION
Among given technologies MANET is most active topic for
research work considering scope of improvement in this
technology.
Clearly a MANET is wireless technique that can be
performed without having any pre-existing infrastructure in
which each node or terminal act as router. But to coordinate
or establish or rout link between two terminals we need to
follow some sort of instructions,rules i.e. protocols. A
wireless ad-hoc environment introduces many problems such
as mobility and limited bandwidth which makes routing
difficult.
Hence efficiency and accuracy of system can be determined
on basis of QoS i.e. quality of service parameters like
Throughput (It is the average rate of successful message
delivery over a communication channel), Delay ( It is a
time required for packets to reach to destination node from
source node) and Fairness (it defines channel utilization
by users).
There are three types of Ad hoc Routing protocols. They are
pro-active protocols, active protocols and hierarchical
protocols. For comparison purpose we will take the few
protocols from each type. They are Dynamic Source routing
Protocol (DSR), Destination Sequenced Distance Vector
(DSDV), Ad hoc on demand Distance vector protocol
(AODV) and Ad-hoc On-demand Multipath Distance Vector
Routing (AOMDV).
Let us view some graph showing performance of MANET
using different protocols.
Throughput using DSR i.e. Dynamic Source routing Protocol.
Comparison between DSR and AODV
III. METHODOLOGY
We can use some simulation software which can mimic real
life scenarios for performance evaluation. For example we
can use network simulator NS-2.34 for simulation
purpose.To compare different ad-hoc routing protocol, it is
best to use identical simulation environments for their
performance evaluation.
IV. CONCLUSION
In this paper we are concentrated on MANET As MANET is
active research topic and we can study it under different
conditions and various parameters to get more accurate
results. Future work will include deep study and analysis of
MANET parameters.
REFERENCES
1. Shaily Mittal Prabhjot Kaur ―Performance comparison of
AODV, DSR and ZRP Routing Protocols in Manets‖
International Conference on Advances in Computing, Control,
and Telecommunication Technologies, 2009.
2. Wireless Network Evolution: 2G to 3G Authors:Vijay Kumar
Garg Editors:Theodore S.Rappaport.
3. Data communications and networking - by Forouzan.
4. Digital Communication by Proakis, Prentice Hall.
www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 3
IJEEE, Vol. 1, Spl. Issue 1 (March 2014) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
Design of Fast Reliable Brain Hemorrhage CT
Scan Image Segmentation Technique
Abhishek Thakur1, Rajesh Kumar
2, Amandeep Bath
3, Jitender Sharma
4
1,2,3,4Electronics & Communication Engineering Department,
Indo Global College of Engineering, Punjab, India [email protected], [email protected],
[email protected], [email protected]
Abstract- The aim of this paper is to develop a fast
and reliable segmentation method to segment the
haemorrhage region from brain CT images. To calculate area
of segmented hemorrhage region that could be useful for
physicians or researchers involved in the treatment or
investigation of intracranial brain haemorrhage. Thus
improving the machine generated automated results and
reducing the human effort for better segmentation and saving
vital time for the treatment of a patient.
Keywords- Magnetic resonance imaging (MRI) and
Computed tomography (CT) scan also known as CAT
(Computer Axial Tomography), IV‘s (Intravenous therapy).
I. INTRODUCTION
Magnetic resonance imaging (MRI) and Computed
tomography (CT) scan also known as CAT (Computer Axial
Tomography) scan, are the two main ways by which
physicians take a picture of brain. In CT scan, testing is fast
and results are quick and thus making it exceptionally
valuable when prompt diagnosis and treatment are critical.
CT scan can be taken while patient is hooked up to IV‘s
(Intravenous therapy) or other medical equipment, unlike
some other scanning methods. CT scans can disclose
hematomas, hemorrhages, and skull fractures and thus
providing exact information to neurologist, necessary for
deciding whether emergency treatment is required. An MRI
process can take about 30-45 minutes to complete while a CT
scan may only take 5 to 10 minutes. So, a severe hemorrhage
could kill patient in the time consumed to take pictures in
MRI machine. Further, in some situations a CT scan can
actually detect abnormalities more easily than an MRI like a
CT scan is good at detecting acute haemorrhage and
problems in bone, for example fractures. On the other hand,
an MRI is best at detecting small or subtle lesions. CT scans
deliver a relatively high dose of radiation to a patient in
comparison to other diagnostic tests. This is not usually a
problem for a single scan, but patients who need to undergo
repeated tests can be subjected to a significant level of
radiation, hence increasing their cancer risk.
MRI makes use of powerful magnetic fields and the
magnetic reaction of the body cells to construct cross-
sectional images is similar to CT scans. MRI does not use X-
rays, so it can be safer than CT if multiple imaging
sessions are expected. The variations of MRI technology can
also examine brain functioning and identify injuries which
are not visible in CT scans. But even the detail available
using MRI cannot detect mild concussions. In acute head
injury cases, MRI is not often used. MRI has some
drawbacks, although MRI images yield finer detail than CT
scans. Some drawbacks include it takes longer to perform, it
is not as readily available as a CT scanner in most hospitals,
it is not practical for patients hooked up to medical
equipment and it cannot be used if patient has metal
embedded anywhere in the body. The greatest danger of an
MRI is to those with metal in their bodies that could be
moved around or heated up by the powerful magnetic force
created by MRI machine. MRI scans also require that a
patient stay very still for a long period of time, which may be
difficult if a patient is confused or fidgety. Each type of scan
is susceptible to different kinds of artefact i.e. blurring of the
image.
The definitive tool for accurate diagnosis of an
intracranial hemorrhage is CT scan i.e. computed
tomography as shown in fig1.1. Typically computed
tomography scanning of head is used to detect infarction,
tumors, calcifications, hemorrhage and bone trauma. Head
CT is the mainstay of diagnosis in ICH. Acute bleeding
appears hyper dense (whiter) on a CT, relative to the
surrounding tissues as shown in figure:
Fig.1 CT scan of a spontaneous intracranial hemorrhage
Image segmentation is the process of partitioning an
image into different segments. These segments often
International Journal of Electrical & Electronics Engineering 4 www.ijeee-apm.com
correspond to different tissue classes, organs, pathologies, or
other biologically relevant structures in medical imaging. In
medical image analysis, one fundamental problem is image
segmentation which identifies the boundaries of objects such
as organs or abnormal regions like tumors in images. Due to
noise, low contrast and other imaging ambiguities medical
image segmentation becomes difficult. It is possible with the
segmentation results to have shape analysis, detecting
volume change, and making a precise radiation therapy
treatment plan. Segmentation is a low-level operation, which
is necessary in order to perform high-level operations like
analysis of shape and size of the organs, 3-D volume
visualization and other such operations. In image processing,
image segmentation techniques are considered a critical
operation because further process steps have to rely on the
segmentation results. Two principal ways exists in order to
perform segmentation. First one is manual segmentation
which is performed by medical experts. In this case medical
expert has to manually outline region of interest using a
pointer device, usually mouse. Another way is to perform as
much as possible of the segmentation automatically and the
whole process is performed by means of a segmentation
algorithm with minimal user interaction.
Some of the segmentation techniques include shape
based segmentation and interactive segmentation. In shape
based segmentation many methods parameterize a template
shape for a given structure, often relying on control points
along the boundary. The entire shape is then deformed to
match a new image. Two of the most common shape-based
techniques are active shape models and active appearance
models. These methods have been very influential and have
given rise to similar models. On the other hand, interactive
methods are useful when clinicians can provide some
information like a seed region or rough outline of the region
to segment. Further, an algorithm can then iteratively refine
such segmentation with or without guidance from the
clinician. Manual segmentation, using tools such as a paint
brush to explicitly define the tissue class of each pixel,
remains the gold standard for many imaging applications
such as radio and telecommunications.
II. TECHNIQUES FOR IMAGE
SEGMENTATION
Approaches of image segmentation can be classified
according to both the features and the type of techniques
used. The features include pixel intensities, edge information,
and texture etc. There exist several common approaches on
medical image segmentation. However, multiple techniques
are often used in conjunction with one another for solving
different segmentation problems.
1. Thresholding methods:
The Segmentation algorithms are based on one of two
basic properties of intensity values discontinuity and
similarity. Discontinuity includes partitioning an image based
on abrupt changes in intensity, such as edges in an image.
Similarity includes partitioning an image into regions that are
similar according to predefined criteria. Threshold
segmentation techniques can be grouped in different classes
which includes local techniques that are based on the local
properties of the pixels and their neighbourhoods. Another
are global techniques to segment an image on the basis of
information obtain globally such as by using image
histogram; global texture properties. Last one split merge and
growing techniques use both the notions of homogeneity and
geometrical proximity in order to obtain good segmentation
results [7].
The gray levels of pixels belonging to the object are
different from the gray levels of the pixels belonging to the
background in many applications of image processing.
Therefore, thresholding becomes a simple but effective tool
to separate objects from the background. Thresholding
operation outputs a binary image whose one state will
indicate the foreground objects while the complementary
state will correspond to the background. On the basis of an
application, the foreground can be represented by gray-level
0 i.e. black and the background by the highest luminance i.e.
255 in 8-bit images, or conversely the foreground by white
and the background by black. It is one of the important
approaches to image segmentation. Often an image histogram
is used to determine the best setting for the threshold. A
thresholded image is defined as:
g x,y = 1 if f(x,y)>T
0 if f(x,y)≤T
Below is an example of image on which threshold is applied.
Fig.2: Thresholding method a. Original CT scan brain image b.
Brain image after thresholding
Thresholding is a simple yet often effective means for
obtaining segmentation in images. Many times thresholding
is used as an initial step in a sequence of image processing
operations. Some of its main limitations includes that in its
simplest form only two classes are generated and it cannot be
applied to multi-channel images. Also, thresholding typically
does not take into account the spatial characteristics of an
image. Due to this it becomes sensitive to noise and intensity
inhomogeneities, which can occur in images like MRI. Due
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to both these artifacts the histogram of image is corrupted,
making separation more difficult. There are single and
multiple thresholds as shown:
Fig. 3: Gray level histograms that can be partitioned as a. single
threshold b. multiple thresholds
Thresholding can be viewed as:
T = T[ x, y, p(x, y), f(x, y) ]
Here, T stands for the threshold. f (x, y) is the gray value of
point (x, y) and p(x, y) denotes some local property of the
point like the average gray value of the neighborhood
centered on point (x, y) . Based on above equation
thresholding techniques can be mainly divided into global,
local, and dynamic thresholding techniques. When T = T [
f(x, y) ] then threshold is global. For T = T[ p(x, y), f(x, y) ]
threshold is local and when T = T[ x, y, p(x, y), f(x, y) ] then
threshold is dynamic or adaptive. There are a number of
global thresholding techniques like minimum thresholding,
Otsu, optimal thresholding, histogram concave analysis,
iterative thresholding, entropy-based thresholding and so on.
Similarly the Main local thresholding techniques are simple
statistical thresholding, 2-D entropy-based thresholding,
histogram-transformation thresholding etc.
2. Region growing methods:
Region growing method is region based image
segmentation. It involves the selection of initial seed points
therefore also a pixel-based image segmentation method.
This method examines neighbouring pixels of initial seed
points and then determines whether the pixel neighbours
should be added to the region. Basically it involves to start
from some pixels (seeds) representing distinct image regions
and to grow them, until they cover the entire image. This
method needs a rule describing a growth mechanism and a
rule checking the homogeneity of the regions after each
growth step. This method for extracting a region of the image
that is connected based on some predefined criteria that can
be based on intensity information and/or edges in the image.
The simplest form of region growing requires a seed point
that is manually selected by an operator, and extracts all
pixels connected to the initial seed with the same intensity
value. Same as thresholding, region growing is not often used
alone but within a set of image processing operations,
particularly for the delineation of small and simple structures
such as tumors and lesions. Its primary disadvantage is that it
requires manual interaction to obtain the seed point. Hence, a
seed must be planted for each region that needs to be
extracted. Figure showing region growing method effect is as
shown.
Fig. 4: Region growing method a. Original CT scan brain image b.
Image after region growing method applied on original image
The Split and merge algorithms are related to region growing
but do not require a seed point. Region growing techniques
can also be noise sensitive that causes extracted regions to
have holes or even become disconnected. The partial volume
effects can also cause separate regions to become connected.
Classifiers:
The classifier methods are pattern recognition techniques.
These methods seek to partition a feature space derived from
the image using data with known labels. Classifiers are
known as supervised methods because of requirement of
training data that are manually segmented and then used as
references for automatically segmenting new data. Number
of ways exists in which training data can be applied in
classifier methods. The nearest-neighbor classifier is a simple
classifier, where each pixel or voxel is classified in the same
class as the training datum with the closest intensity. A
generalization of this approach is k nearest neighbor (kNN)
classifier where the pixel is classified according to the
majority vote of the k closest training data. This classifier is
considered a nonparametric classifier as it makes no
underlying assumption about the statistical structure of the
data. Maximum likelihood (ML) or Bayes classifier is a
commonly-used parametric classifier.
3. Clustering methods:
Clustering algorithms essentially perform the same function
like classifier methods without the use of training data.
Therefore, they are termed unsupervised methods. To
compensate for the lack of training data, clustering methods
iterate between segmenting the image and characterizing the
properties of the each class. In other words, clustering
methods train themselves using the available data.
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Fig. 5: Colouring of squares into three clusters representing results
of cluster analysis.
Some typical cluster models include:
1. Connectivity models: Example is hierarchical clustering
that builds models based on distance connectivity.
2. Centroid models: Example for these models includes the k-
means algorithm which represents each cluster by a single
mean vector.
3. Distribution models: In this, clusters are modelled using
statistical distributions, such as multivariate normal
distributions used by the Expectation-maximization
algorithm (EM).
The commonly used clustering algorithms are the K-means
or ISODATA algorithm, the fuzzy c-means algorithm and the
expectation-maximization algorithm. The K-means clustering
algorithm clusters data by iteratively computing a mean
intensity for each class and segmenting the image by
classifying each pixel in the class with the closest mean. The
result of applying the K-means algorithm to a slice of a MR
brain image is shown in figure below. Cerebrospinal fluid,
gray matter and white matter regions are there.
Fig. 6: Segmentation of MRI brain image a. Original brain MRI b.
Segmentation using K-means algorithm
Clustering algorithms do not require training data, but they
do require an initial segmentation or initial parameters.
4. Markov random field models:
Markov random field modelling itself is not a segmentation
method but a statistical model which can be used within
segmentation methods. In medical imaging, they are typically
used to take into account the fact that most pixels belong to
the same class as their neighbouring pixels. Markov random
field are often incorporated into clustering segmentation
algorithms like the K-means algorithm under a Bayesian
prior model. These models have difficulty of proper selection
of the parameters controlling the strength of spatial
interactions. Too high a setting can result in an excessively
smooth segmentation and a loss of important structural
details. Further, computationally intensive algorithms are
required by MRF methods. But in spite of this, MRFs are
widely used not only to model segmentation classes, but also
to model intensity in homogeneities that can occur in MRI
images and texture properties.
Fig. 7: Segmentation of MRI brain image a. Original brain MRI b.
Segmentation using K-means algorithm c. Segmentation using K-
means algorithm with MRF prior.
5. Artificial neural networks:
Artificial Neural Networks are electronic models based on
the neural structure of the brain. ANN is capable of machine
learning and pattern recognition. These are presented as
systems of interconnected neurons that can compute values
from inputs by feeding information through the network.
Neural networks have been used to solve a wide variety of
tasks that are hard to solve using ordinary rule-based
programming, including computer vision. Neural network
based image segmentation techniques include supervised
techniques such as feed-forward neural network, Multilayer
perceptron and unsupervised techniques such as pulse
coupled neural network (PCNN).
Fig. 8: Circular nodes represent artificial neurons and arrows
represent input output connections from one neuron to other neurons
6. Deformable models:
Deformable models are physically motivated, model-based
techniques. These are used for delineating region boundaries
using closed parametric curves or surfaces that deform under
the influence of internal and external forces. To delineate an
object boundary in an image, a closed curve or surface must
first be placed near the desired boundary. It is then allowed to
undergo an iterative relaxation process. The internal forces
are computed from within the curve or surface to keep it
smooth throughout the deformation and external forces are
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usually derived from the image to drive the curve or surface
towards the desired feature of interest.
Advantages of deformable include their ability to directly
generate closed parametric curves or surfaces from images
and their incorporation of a smoothness constraint that
provides robustness to noise and spurious edges.
Disadvantage include requirement of manual interaction to
place an initial model and choose appropriate parameters.
The standard deformable models can also exhibit poor
convergence to concave boundaries. This difficulty can be
reduced to some extent through the use of pressure forces and
other modified external force model.
Atlas guided approaches:
For medical image segmentation atlas-guided approaches are
a powerful tool when a standard atlas or template is
available. The atlas is generated by compiling information on
the anatomy that requires segmenting and then used as a
reference frame for segmenting new images. The atlas-
guided approaches are similar to classifiers except they are
implemented in the spatial domain of the image rather than in
a feature space. The standard atlas-guided approach treats
segmentation as a registration problem. Firstly it finds a one
to one transformation that maps a pre-segmented atlas image
to the target image that requires segmenting. This process is
often known as atlas warping. These approaches have been
applied mainly in MR brain imaging. Advantages include
that labels are transferred as well as the segmentation.
III. RESULTS AND DISCUSSION
In this paper, algorithm for segmentation and area calculation
of intracranial brain hemorrhage from CT scan images has
been implemented in matlab R2011b. All the images has
dimension of 512 x 512. Tests were performed on 11 human
brain CT scan hemorrhage images. The segmentation results
are as shown in figures below. The percentage of correct
classification (PCC) and computational time results are also
shown in table 4.
a) Original brain hemorrhage CT scan:
The original image of brain hemorrhage CT scan is
showing hemorrhage region which is appearing white in the
center. Grey matter is the brain region. The outermost white
region surrounding gray matter is skull.
b) Binary image
The binary image has pixel values in 1‘s and 0‘s. The
white region pixels correspond to 1‘s and black region pixels
correspond to 0‘s.
c) Binary image after bwlabel
Binary image after bwlabel morphological operation is
shown. bwlabel has syntax : [L,num] = bwlabel(f,conn)
which returns a matrix L, of the same size as BW, containing
labels for the connected objects in BW.
Fig. 8: a. Original brain hemorrhage CT scan, b. Binary image, c.
Binary image after bwlabel, d. Skull portion Binary image, e. Holes
filled image, f. Image after logical operations, g. Image in which
extra pixels are removed by morphological operation, g. Image in
which extra pixels are removed by morphological operation, h.
Extracted ROI image (Intracranial), i. Sobel edge operator
segmented image.
conn can have a value of either 4 or 8, where 4 specifies 4-
connected objects and 8 specifies 8-connected objects. L is
the label matrix, num gives total number of connected
components and is optional. f is input binary image and
conn parameter has default value of 8. The elements of L are
integer values greater than or equal to 0. The pixels labelled 0
are the background. The pixels labelled 1 make up one
object, the pixels labelled 2 make up a second object, and so
on.
d) Skull portion Binary image:
The skull portion comprises of white pixels i.e. 1‘s. Basically
this skull portion has to be removed in order to get only
intracranial region (ROI).
e) Holes filled image:
Holes filling is done using imfill morphological operation.
imfill fills image regions and holes. Image after holes filling
is shown in e. It has syntax BW2 = imfill(BW,'holes') which
fills holes in the binary image BW. Basically, a hole is a set
of background pixels that cannot be reached by filling in the
background from the edge of the image.
f) Image after logical operations
Image after logical operations & and ~ is shown. &
returns 1 for every element location that is true means
nonzero in both arrays and 0 for all other elements. ~
complements each element of the input array.
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g) Image in which extra pixels are removed by
morphological operation
The small objects in the image background are removed by
bwareaopen morphological operation. bwareaopen remove
small objects from binary image. It has syntax: BW2 =
bwareaopen(BW, P). It removes from a binary image all
connected components (objects) that have fewer
than P pixels, producing another binary image, BW2. This
operation is known as an area opening.
h) Extracted ROI image (Intracranial):
Extracted ROI image
The extracted ROI image is shown with removed background
pixels and skull portion. In other words, the image is left with
ROI (intracranial) portion and the segmentation is applied on
this image further.
IV. CONCLUSION AND FUTURE WORK
In this work sub-blocking rule based criteria is used for
intracranial hemorrhage segmentation in CT brain images
and also hemorrhage area is calculated. The CT scan brain
images with hemorrhage (ICH) are taken for segmentation.
This segmentation method used has main advantage of fast
the processing speed and better results for percentage of
correct classification with less noise in processed images.
Except one case, in rest all cases, the algorithm segments
hemorrhage. However, in some cases the segmentation is not
completely exact. Practically, the issue is very difficult of
avoiding and until now, a perfect automatic segmentation
algorithm does not exist. For such a reason, to analyse the
performance of algorithm, a comparison with manual
segmentation (ground truth) is done. The achievement of
better results lies in the use of sub-blocking rule based
criteria for the segmentation rather than multilevel otsu
thresholding method. The Otsu thresholding does not claim
to be the best automatic thresholding ever and can be
extended to a multi-level thresholding which results in
segmentation. Thresholding is a technique often applied to
image segmentation with a basic objective to classify the
pixels of a given image into two classes i.e. those pertaining
to an object and those pertaining to the background. In case
of an image with clear objects in the background, the bi-level
thresholding method can easily divide the object from the
background. On the other hand, to segment complex images
a multilevel threshold method is required. The multilevel
thresholding method segments the pixels into several distinct
groups in which the pixels of the same group have gray
levels within a specific range. However, when the
thresholding method is extended to multi-level thresholding,
the computation time grows exponentially with the number
of thresholds. Comparison of Sub-blocking rule based criteria
and MLSA (Multi-level local segmentation approach) in
terms of average computational time and average PCC is
shown in table 1.
Table1. Comparison of Sub-blocking rule based criteria and MLSA
(Multi-level local segmentation approach) in terms of average
computational time and average PCC
Methods Average
Computational
Time (seconds)
Average
PCC
(%)
Sub blocking rule
based ctiteria
0.10 0.986
MLSA 0.17 0.971
The Sub-blocking rule based criteria include k-means
clustering in addition to sobel edge and weighed sum
method. The MLSA (Multi-level local segmentation
approach) includes multilevel otsu thresholding method.
Table6. shows that obtained results for proposed algorithm
are better in comparison to MLSA method.
The future work will focus to further improve the results
using more image datasets of medical images and other
robust image segmentation techniques. A combination of
different methods may be applied to obtain a complete
effective and robust solution for segmentation. By using the
advantage of each method the segmentation results can be
improved.
REFERENCES
[1] A comparative performance evaluation of various approaches
for liver segmentation from SPIR images; Evgin Göçeri, Mehmet Zübeyir Ünlü and Oğuz Dicle; Available at: http://online.journals.tubitak.gov.tr/ openAcceptedDocument.htm?fileID=290786&no=63245 , pp. 1-44
[2] Cerebrovascular Disease: Revised Imaging Guidelines from the American College of Radiology; Available at: http://www.eradimaging.com/ site/article.cfm?ID=779.
[3] Evaluation of Image Segmentation; Simon K. Warfield, Ph.D.;
Computational Radiology Laboratory Harvard Medical
School; Available at:
http://www2.imm.dtu.dk/projects/sparse/iceland-warfield-eval-
segmentation.pdf , pp. 1-46
[4] Morphological Image Processing Approach On The Detection
Of Tumor And Cancer Cells; Ms. M. Parisa Beham,
Ms.A.B.Gurulakshmi; IEEE 2012
[5] Comprehensive Applying Watershed Algorithm in Segmentation
of CT Brain Images; ZHU Bing-li, Xiong Jiang, Tan Xiao-ling;
2011 IEEE, pp. 81-83
[6] Intracranial Hemorrhage Annotation for CT Brain Images; Tong
Hau Lee, Mohammad Faizal Ahmad Fauzi , Su-Cheng Haw;
Proceeding of the International Conference on Advanced
Science, Engineering and Information Technology 2011, pp.
689-693
www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 9
[7] A novel intuitionistic fuzzy approach for tumour/hemorrhage
detection in medical images; Tamalika Chaira and Sneh
Anand; Journal of Scientific & Industrial Research Vol. 70,
June 2011, pp. 427-434
[8] Hematoma volume detection and estimation from CT images; V.
SĂCELEANU, R. BRAD, A. BARGLAZAN, M. PEREANU;
AMT, vol. II, nr. 3, 2011, pp. 298-301
[9] Qualitative and Quantitative Comparisons of Haemorrhage
Intracranial Segmentation in CT Brain Images; W. Mimi Diyana
W. Zaki; Tencon 2011, pp. 367- 373
[10] An Algorithm for Automatic Segmentation of Spontaneous
Cerebral Hemorrhages; R. Rodríguez Morales; Claib 2011, pp.
1-4
[11] Comparative Study of Adaptive Network-Based Fuzzy
Inference System (ANFIS), k-Nearest Neighbors (k-NN) and
Fuzzy c-Means (FCM) for Brain Abnormalities Segmentation;
Noor Elaiza Abdul Khalid, Shafaf Ibrahim, Mazani Manaf;
INTERNATIONAL JOURNAL OF COMPUTERS Issue 4,
Volume 5, 2011, pp. 513- 524
[12] Medical Image Segmentation Based on Contourlet Transform
and Watershed Algorithm; Hongying LIU, Yi LIU, Qian LI,
Hongyan LIU, Yongan TONG; 2011 IEEE, pp. 224-227
[13] A Novel Anatomical Structure Segmentation Method of CT
Head Images; Xiaojun Zang, Jian Yang, Dongdong Weng, Vue
Liu, Yongtian Wang; The 2010 IEEE/ICME International
Conference on Complex Medical Engineering July 13-15,2010,
Gold Coast, Australia, pp. 316-320
[14] A novel method of CT brain images segmentation; Xiaojun
Zang, Yongtian Wang, Jian Yang, Yue Liu; 2010 International
Conference of Medical Image Analysis and Clinical
Application (MIACA), pp. 109-112
[15] Multi-dimensional Data Analysis of Intracerebral Hemorrhage
from CT Images; Jianmin Dong, Fangxia Shi; 2010 3rd
International Conference on Biomedical Engineering and
Informatics (BMEI 2010), pp. 406-409
[16] Fuzzy expert system for edema segmentation; Sven Lencaric;
INTERNATIONAL JOURNAL OF COMPUTERS Issue 3,
Volume 5, 2010, pp. 311- 317
[17] A Fast and Noise-Adaptive Rough-Fuzzy Hybrid Algorithm for
Medical Image Segmentation; Arpit Srivastava, Abhinav Asati,
Mahua Bhattacharya; 2010 IEEE International Conference on
Bioinformatics and Biomedicine, pp. 416-421
AUTHORS
First Author– Abhishek Thakur: M.
Tech. in Electronics and
Communication Engineering from
Punjab Technical University, MBA in
Information Technology from
Symbiosis Pune, M.H. Bachelor in
Engineering (B.E.- Electronics) from
Shivaji University Kolhapur, M.H. Five years of work
experience in teaching and one year of work experience in
industry. Area of interest: Digital Image and Speech
Processing, Antenna Design and Wireless Communication.
International Publication: 7, National Conferences and
Publication: 6, Book Published: 4 (Microprocessor and
Assembly Language Programming, Microprocessor and
Microcontroller, Digital Communication and Wireless
Communication). Working with Indo Global College of
Engineering Abhipur, Mohali, P.B. since 2011.
Email: [email protected]
Second Author – Rajesh Kumar is
working as Associate Professor at
Indo Global College of Engineering,
Mohali, Punjab. He is pursuing Ph.D
from NIT, Hamirpur, H.P. and has
completed his M.Tech from GNE,
Ludhiana, India. He completed his
B.Tech from HCTM, Kaithal, India. He has 11 years of
academic experience. He has authored many research papers
in reputed International Journals, International and National
conferences. His areas of interest are VLSI, Microelectronics
and Image & Speech Processing.
Third Author – Amandeep Batth:
M. Tech. in Electronics and
Communication Engineering from
Punjab Technical University, MBA
in Human Resource Management
from Punjab Technical University ,
Bachelor in Technology (B-Tech.)
from Punjab Technical University . Six years of work
experience in teaching. Area of interest: Antenna Design and
Wireless Communication. International Publication: 1,
National Conferences and Publication: 4. Working with Indo
Global College of Engineering Abhipur, Mohali, P.B. since
2008.
Email: [email protected]
Fourth Author – Jitender Sharma: M. Tech. in Electronics
and Communication Engineering from Mullana University,
Ambala, Bachelor in Technology (B-Tech.)from Punjab
Technical University . Five years of work experience in
teaching. Area of interest:, Antenna Design and Wireless
Communication. International Publication: 1 National
Conferences and Publication:6 and Wireless
Communication). Working with Indo Global college since
2008.
E-mail: [email protected]
International Journal of Electrical & Electronics Engineering 10 www.ijeee-apm.com
IJEEE, Vol. 1, Spl. Issue 1 (March 2014) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
Design of Lambda/2 Dipole Antenna 1Amandeep Bath,
2Abhishek Thakur,
3Jitender Sharma ,
4Prof. Basudeo Prasad
1,2,3,4Electronics & Communication Engineering Department,
Indo Global College of Engineering, Punjab, India [email protected], [email protected], [email protected]
Abstract- Ultra wideband is a wireless technology to
realize high speed communications which is performed in
wideband. In this paper the wideband dipole antenna is
designed. The simulation is done using ANSOFT HFSS
simulation software.
Index Terms- Broad band, wide beam, circular polarization,
conducting wall, micro strip antenna, Wide-Band, Omni
directional radiation pattern smart grid, Wi Max directive
antennas, UWB antennas, Biotelemetry, capsule endoscope,
dipole antenna ,planar reflector antenna.
I. INTRODUCTION
In radio and telecommunications a dipole antenna also
known as doublet is the easiest and most commonly used
class of antenna. It is made up of two similar conductive
elements such as metal wires or rods which are generally
bilaterally symmetrical. The driving current from the
transmitter is given, or for receiving antennas the output
signal to the receiver is obtained and taken, between the two
halves of the antenna. Each side of the feedline to the
transmitter or receiver is joined to one of the conductors.
This is different with a monopole antenna, which is made up
of a single rod or conductor with one side of the feed line
joined to it, and the other side connected to some type of
ground. The best example of a dipole is the "rabbit ears"
television antenna which is found on broadcast television
sets.
The most common type of dipole is two straight rods or wires
which are connected end to end on the same axis, with the
feed line connected to the two adjacent ends. This is the
easiest type of antenna from a theoretical point of view.
Dipoles are resonating antennas, meaning that the elements
serve as resonating elements, with standing waves of radio
current which flows back and forth between their ends. So
the length of the dipole elements is calculated by the
wavelength of the radio waves used. The most common type
is the one half wave dipole, in which both of the two rod
elements is approximately 1/4 wavelength long, so the
complete antenna is a half-wavelength long. Numerous
different types of the dipole are also used, such as the folded
dipole, short dipole, cage dipole, bow-tie, and batwing
antenna. Dipoles may be used as standalone antennas
themselves, but they are also used as feed antennas (driven
elements) in many more advanced antenna types, such as the
Yagi antenna, parabolic antenna, reflective array, turnstile
antenna, log periodic antenna, and phased array. The dipole
was the oldest and primitive type of antenna; it was invented
by German scientist Heinrich Hertz around 1886 in his
advanced research of radio waves
A dipole is a symmetrical antenna, as it is composed of
two symmetrical ungrounded elements. Therefore it works
best when fed by a balanced transmission line, such as a
ladder line. It happens because in that case the symmetry
(one aspect of the impedance complex, which is a complex
number) matches and therefore the power transfer is external.
When a dipole with an unbalanced feed line such as coaxial
cable which is generally used for transmitting the signal, the
shield side of the cable, in addition to the antenna, radiates.
RF currents are induced into other electronic equipment very
close to the radiating feed line, producing RF interference.
Furthermore, the efficiency of the antenna is very low
because it is radiating closer to the ground and its radiation as
well as the reception pattern may be asymmetrically
distorted. At very high frequencies, where the coax diameter
is generally more than the length of the dipole, this becomes
a more prominent problem. To remove this, dipoles fed by
coaxial cables have a balloon kind of structure between the
cable and the antenna, the unbalanced signal provided by the
coax is converted to a very balanced symmetrical signal for
the antenna.
Agile reconfigurable antennas for future communication
systems have attracted researchers around the globe.
Antenna's characteristics such as frequency, radiation pattern
and polarization are reconfigured to attain the demands for
agile radios. A lot of researches focus on frequency
reconfiguration as future communication systems such as
cognitive radio needs an antenna that can do spectrum
sensing and communication. In reconfigurable frequency
antennas development, recently a reconfigurable wide-band
to agile narrow frequencies, using a printed log periodic
dipole array antenna, was introduced. A wideband slotted
multifunctional reconfigurable frequency antenna for
WLAN, WIMAX, UWB and UMTS has been proposed in, a
frequency reconfigurable antenna, consisting of two
structures; one is an ultra-wide band (UWB) and other is a
frequency reconfigurable triangle shape antenna, is proposed
for cognitive radio communication
www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 11
Ultra-wide band antennas have already been used in
areas such as satellite communication, remote sensing, and
ultra-wide band radar and so on. Currently, the wireless area
network (WLAN) in the 2.4-GHz (2.4-2.485 GHz) and 5-
GHz (5.l5-5.875 GHz) bands is the most popular networks
for accessing the internet the antenna for an AP not only
requires dual-band operation but also needs to have an
appropriate radiation profile in both bands, namely similar
gain, wide beam width, and high front-to-back ratio. Wireless
communications continues to enjoy exponential growth in
Industrial, Scientific, and Medical (ISM) band. The future
generation wireless networks require systems with broad-
band capabilities in various environments to satisfy several
applications as smart grid, personal communications, home,
car, and office networking .On the other hand, many modern
wireless communication systems such as radar, navigation,
satellite, and mobile applications use the circular polarized
(CP) radiation pattern. For the best UWB performance, the
transmitter and receiver (T/R) antennas should have flat and
high directive gain, narrow beam, low side and back lobes
over the operational frequency band; to attain the largest
dynamic range, best focused illumination area, lowest T/R
coupling, reduced ringing and uniformly shaped impulse
radiation.UWB has promised to offer high data rates at short
distances with low power, primarily due to wide resolution
bandwidth.
II. ANTENNA DESIGN AND CONFIGURATION
All The geometry and configuration of the proposed
antenna is shown in the figure. Initially the design properties
are selected by adjusting the local variables such as the
substrate height ‗l=25cm' and the radius 'a=0.5mm' and the
position as well. As shown in the figure the proposed antenna
consists of a cylindrical radiating substrate which is duplicate
d around the X axis with a rectangular lumped port excitation
between them. The duplicated substrate cylindrical antenna
element around the X axis is shown in the figure.
Fig. 1: Duplicated cylindrical substrate around the axis
Fig. 2: Rectangular radiating element between substrates
The material of the substrate is kept as pec with a bulk
conductivity of 1e+030 Siemens/m. The rectangular element
between the cylindrical substrates provides the lumped
excitation with a position 0,-.5,-2. The the integrating line is
drawn between the cylindrical substrates through the
rectangular element.
Fig. 3: Integrating line between the substrates
The structure is then covered by a vacuum box with the
position -100,-75,-75 mm and the other dimensions as
X=200, Y=150, Z=150mm. Also the transparency is adjusted
as 0.76. Further the faces of the vacuum box are individually
selected for assigning the radiation boundary. Before the
final validation check the solution frequency is adjusted as
300 MHz for the setup. Also for the same set up the
frequency sweep is adjusted by keeping the sweep type as
fast and the start and stop frequencies as 100 and 500Mhz
respectively by keeping the count as linear. Finally the design
undergoes the validation check for the errors.
Fig. 4: Air box over the dipole
III. DIPOLE CHARACTERISTICS
International Journal of Electrical & Electronics Engineering 12 www.ijeee-apm.com
A. Frequency versus length
Dipoles that are very small even smaller than the
wavelength of the signal are called Hertz an, short, or
infinitesimal dipoles. These have a very low radiation
resistance and a high capacitive reactance, so they are not
very much efficient; though inefficient, they can be practical
antennas for long wavelengths. Dipoles whose length is half
the wavelength of the signal are called half-wave dipoles, and
are more efficient. In general radio engineering, the term
dipole usually means a half-wave dipole (center-fed).A half-
wave dipole is cut to length l for frequency f in hertz
according to the formula
Where λd is the wavelength on the dipole elements, λ0 is
the free-space wavelength, c is the speed of light in free
space (299,792,458 meters per second (983,571,060 ft/s)),
and k is called adjustment factor. The adjustment factor
completely compensates for propagation speed being
somewhat less than the speed of light. The dipole elements
will have distributed inductance and capacitance. The value
of k is around 0.95. For thin wires with the dimensions
(radius = 0.000001 wavelengths), k is approximately 0.981; for thick wires (radius = 0.01 wavelengths), k drops to about
0.915.The above formula which is given is often shortened to
the length in meters = 143/MHz or the length in feet =
468/MHz; MHz is the frequency in megahertz.
A. Elementary doublet
From a theoretical point of view, the dipole antenna is
the simplest antenna. An elementary doublet or Hertzian
dipole as shown in the figure is a small length of conductor
δℓ (small compared to the wavelength λ) carrying an
alternating current whose equation is:
Fig. 5: Elementary doublet.
Here ω = 2πf is the angular frequency (and f the
frequency), and i = √−1 is the imaginary unit, so that I is a
phasor. It is used in, for example, analytical calculation on
more complex antenna geometries. Note that physical
construction of the dipole is difficult because the current
needs somewhere to come from and somewhere to go to.
Actually, this small length of conductor will be just one of
the multiple segments into which we must divide a real
antenna, in order to calculate its properties. In the case of the
elementary doublet which is shown in the figure it is possible
to find exact, closed-form expressions for its electric field, E,
and its magnetic field, H. In spherical coordinates, they are
where r is the distance from the doublet to the point
where the fields are evaluated, k = 2π/λ is the wave number,
and Z = √μ/ε = 1/εc = μc is the wave impedance of the
surrounding medium (usually air or vacuum) and the
concerned equations are also shown .The energy associated
with the term of the near field flows alternately out of and
back into the antenna. The exponent of e accounts for the
phase dependence of the electric field on time and the
distance from the dipole. Often one is interested in the
antenna's radiation pattern only in the far field, when
r ≫ λ/2π. In this regime, only the 1/r term contributes, and
hence. The concerned equations are
The far electric field, Eθ, of the electromagnetic wave is co-planar with the conductor and perpendicular with the line
joining the dipole to the point where the field is calculated. If
the dipole were placed in the center of a sphere with the axis
south-north, the electric field would be parallel to geographic meridians and the magnetic field of the electromagnetic wave
would be parallel to geographic parallels
B. Dipole antenna techniques
Implementation of wideband antenna for smart grid applications with a frequency bandwidth of 40% and gain of
3 to 4dbThe antenna design and simulation was carried out
using ANSYS‘ HFSS software which is the industry-standard
simulation tool for 3-D full-wave electromagnetic field
simulation. The total size of the antenna is 20mm x 10mm x
2mm. This new design offers a wide fractional frequency
www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 13
bandwidth of about 40% with a gain from 3dB-4.3dB over
the frequency band (5GHz – 7.5GHz)
Using ultra wideband dipole antenna operating at 1.75 to 40
GHz .It is shown that the proposed antenna works well in
1.7GHz-40GHz frequency range and the main direction of
the radiation pattern keeps stable during the whole frequency range. The H plane demonstrates an excellent Omni-
directional pattern.
A Dual-band Wide-beam width WLAN Access Point
Antenna with similar gain and wide beam width in both the
2.4- and 5-GHz WLAN bands. This paper describes a dual
band printed dipole antenna that has nearly identical radiation
patterns with similar gain and wide beam width in both the
2.4- and 5-GHz WLAN bands. The proposed design employs
two techniques to improve the radiation pattern. These
techniques are the use of an angle dipole and vertical copper
plates arranged on the ground plane for improvement in the
radiation pattern of lower and upper bands, respectively
.Ultra band dipole antenna and circularly polarized antenna
provides the best Omni directional radiation pattern. Also the
techniques such as angled dipole and vertical copper plates
on ground plane are used for the further improvement of the
radiation pattern of the antenna.
IV. RESULTS AND DISCUSSION
In this section the lambda /2 dipole antenna is
constructed and the numerical and experimental results
regarding the radiation characteristics are presented and
discussed. The simulated results are obtained by using the An
soft simulation software high frequency structure simulator.
The measured and simulated characteristics of the antenna
are shown and from the far field report the rectangular plot,
the 3D polar plot and are drawn and the radiation
characteristics are also plotted.
Fig. 6: XY Rectangular Plot
Fig. 7: 3D Polar Plot
Unlike other antennas reported in the literature to date, the
proposed antenna displays a good omnidirectional radiation
pattern even at higher frequencies. The designed antenna has
a small size and good return loss and radiation pattern
characteristics are obtained in the frequency band of interest.
The simulated and experimental results show that the
proposed antenna could be a good candidate for UWB
applications. The radiation pattern is shown in the figure for
the dipole antenna.
Fig. 8: Radiation Pattern
Next the radiation pattern for a half wave dipole antenna is
shown along with the stacked XY plot
Fig. 9: XY stacked plot
International Journal of Electrical & Electronics Engineering 14 www.ijeee-apm.com
Fig. 10: Electric fields (blue) and magnetic fields (red) radiated by a
dipole antenna
A. Radiation Pattern and Gain
Dipoles have a radiation pattern, shaped like a toroids
(doughnut) symmetrical about the axis of the dipole. The
radiation is maximum at right angles to the dipole, dropping
off to zero on the antenna's axis. The theoretical maximum
gain of a Hertzian dipole is 10 log 1.5 or 1.76 dBi. The maximum theoretical gain of a λ/2-dipole is 10 log 1.64 or
2.15 dBi.
V. CONCLUSION AND FUTURE WORK
With the rapid progress of wireless technology in recent
years, various wireless systems such as GSM,
WCDMA/UMTS, Bluetooth, WLANs, and GPS have been
highly integrated into the mobile devices, and in order to
fulfill the RF system requirements using the different
frequency band, antenna technology is required to wideband
characteristics .On the other hand, many modern wireless
communication systems such as radar, navigation, satellite,
and mobile applications use the circular polarized (CP)
radiation pattern. The attractive advantages of the CP antenna
are existed as follows. Firstly, since the CP antennas send
and receive in all planes, it is strong for the reflection and
absorption of the radio signal. In the multi-path fading
channel environment, the CP antenna overcomes out of phase
problem which can cause dead-spots, decreased throughput,
reduced overall system performance. Additionally. Also
further improvements could be done by using antenna
substrates with higher dielectric constants in order to reduce
the size a broad band wide beam circular polarization micro
strip antenna. The configuration of the antenna is simple and
easy to fabricate compared with conventional micro strip
antenna, the radiation beam is broadened obviously. Further
research on circularly polarized wideband micro strip
antenna is required as it gives the best performance and
overall improvement of antenna parameters.
REFERENCES
[1] Gaboardi P., Rosa L., Cucinotta A., and Selleri S., ―Patch Array Antenna for UWB Radar Applications‖, in 3rdEuropean RadarConference, 2006, p.281-284.
[2] Yoann Letestu and Ala Sharaiha, ―Size reduced multi-band printed quadrifilar helical antenna,‖ IEEE Trans. Antennas Propag., vol. 59, pp. 3138-3143, 2011.
[3] A. Siligaris et al., ―A 65-nm CMOS fully integrated transceiver module for 60-GHz Wireless HD applications,‖
IEEE Journal of Solid-State Circuits, vol. 46, no. 12, pp. 3005-3017, Dec. 2011.
[4] S. Manafi, S. Nikmehr, and M. Bemani, "Planar reconfigurable multifunctional antennaforWLAN/wimax/UWB/pcsdcs/UMTS applications," Progress In Electromagnetics Research C, Vol.26, 123- 137, 2012.
[5] C. R. Medeiros, E. B. Lima, 1. R. Costa, and C. A.Fernandes,
"Wideband slot antenna for WLAN accesspoint, " IEEE
Antenna Wireless Propagate. Lett., vol. 9,pp. 79-82,2010.
[6] F. Ghanem, P. S. Hall and J. R. Kelly, ―Two port frequency
reconfigurable antenna for cognitive radios‖, Electronics
Letters,vol.45, 2009,pp.534-536.
[7] E. Ebrahimi, J. R. Kelly and P. S. Hall, ―A reconfigurable
Narrowband antenna integrated with wideband monopole for
cognitive radio applications‖, IEEE Antennas and
Propagation Society International Symposium( APSURSI),
2009.
[8] J. W. Baik, S. Pyo, T.H. Lee, and Y.S. Kim, ―Switchable
printed Yagi- Uda antenna with pattern reconfiguration‖,
ETRI Journal, vol.31 2009,pp.318-320
[9] M. Sanad, "A Small Size Micro strip Antenna Circuit", IEEE
International Conference on Antenna and Propagation, vol. 1,
pp. 465-471, April1995.
[10] P. Suraj and V. R. Gupta, ―Analysis of a Rectangular
Monopole Patch Antenna‖ ‗International Journal of Recent
Trends in Engineering,Vol. 2, No. 5, pp. 106-109, November
2009.
[11] M. N. Srifi, M. Meloui and M. Essaaidi, ―Rectangular Slotted
Patch Antenna for 5-6GHz Applications‖, International Journal
of Microwave and Optical Technology, Vol.5 No. 2, pp., 52-57
March 2010.
[12] Ansoft Corporations, HFSS V.12- Software based on the
finite element method [13] G. Augustin, S. V. Shynu, C. K.
Aanandan, and K. Vasudevan, "Compact dual-band antenna for
wireless access point, " Electron. Lett., vol. 42, no. 9, pp. 502-
503, Apr. 2006.
AUTHORS
First Author – Amandeep Batth:
M. Tech. in Electronics and
Communication Engineering from
Punjab Technical University,
MBA in Human Resource
Management from Punjab
Technical University , Bachelor in
Technology (B-Tech.) from Punjab Technical University .
Six years of work experience in teaching. Area of interest:
Antenna Design and Wireless Communication. International
Publication: 1, National Conferences and Publication: 4.
Working with Indo Global College of Engineering Abhipur,
Mohali, P.B. since 2008.
Email: [email protected]
www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 15
Second Author– Abhishek
Thakur: M. Tech. in Electronics
and Communication Engineering
from Punjab Technical University,
MBA in Information Technology
from Symbiosis Pune, M.H.
Bachelor in Engineering (B.E.-
Electronics) from Shivaji
University Kolhapur, M.H. Five years of work experience in
teaching and one year of work experience in industry. Area
of interest: Digital Image and Speech Processing, Antenna
Design and Wireless Communication. International
Publication: 7, National Conferences and Publication: 6,
Book Published: 4 (Microprocessor and Assembly Language
Programming, Microprocessor and Microcontroller, Digital
Communication and Wireless Communication). Working
with Indo Global College of Engineering Abhipur, Mohali,
P.B. since 2011.
Email: [email protected]
Third Author – Jitender Sharma: M. Tech. in Electronics
and Communication Engineering from Mullana University,
Ambala, Bachelor in Technology (B-Tech.)from Punjab
Technical University . Five years of work experience in
teaching. Area of interest:, Antenna Design and Wireless
Communication. International Publication: 1 National
Conferences and Publication:6 and Wireless
Communication). Working with Indo Global college since
2008.
E-mail:[email protected]
International Journal of Electrical & Electronics Engineering 16 www.ijeee-apm.com
IJEEE, Vol. 1, Spl. Issue 1 (March 2014) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
Speech Recognition Based Wireless Automation
of Home Appliances for Disabled Persons
Abhishek Thakur1, Rajesh Kumar
2, Amandeep Bath
3, Jitender Sharma
4
1,2,3,4Electronics & Communication Engineering Department,
Indo Global College of Engineering, Punjab, India [email protected],
Abstract- Matlab straight forward programming interface
make it an ideal tool for Hindi Key word Recognition. For
the extraction of the feature, Hindi Key word database has
been designed by using the Matlab 7.5. The database
consists of the eight key words. Each key word has been
stored in database by the ten speakers, eight male speakers
and two female speakers consist of total 80 samples for eight
commands. Features of the speech signal which are extracted in the form of MFCC coefficients and Dynamic Time
Warping (DTW) has been used as features matching
techniques. This thesis presents the technique to detect
utterance using end point detection, MFCC to extract
features and DTW to compare the test patterns. The
recognition results are tested for clean and noisy test data.
The system can be said to be robust as average accuracy for
clean data is 97.50 % while that for noisy data is 91.25 % or
above is acceptable since most people would not mind
repeating a command to the system one out of ten times or
less. The system can be implemented using one of the common microcontrollers with a small amount of dedicated
memory and an analog to digital converter to accept the
input speech. The system would be fast, small and cost
efficient to be incorporated into a wide variety of consumer
electronics. The aim of this thesis is therefore to develop a
speaker dependent, isolated word, limited vocabulary speech
recognition system that is small enough to fit in a small
household appliance and that can be operated in real time.
Index Terms- Automatic Speech Recognition (ASR), Mel
Frequency Cepstral Coefficient (MFCC) and Dynamic Time
Wrapping (DTW)
V. INTRODUCTION
Although many systems exist for speech recognition, none of
them address the needs for consumer level applications. In
order for a system to be incorporated in the everyday needs
of a consumer, the system must be speaker independent, fast,
low cost, require no training and small enough to be fit
inside a consumer appliance. Such a system will move speech recognition from the domain of the academic or
industrial application to that of a common home user. The
above system can be implemented using current technology
once a certain number of compromises are made. For
example, let's say a speech recognition system is to be
developed so that it can be incorporated into a home
microwave oven. One can immediately see that there is no
need to have a 60,000 word vocabulary for such a system, a
dozen words including the digits are sufficient for its
operation. The system could be further simplified if one does
not allow the user to change the number of words in the
vocabulary. The Second aspect of the system is that it does
not have to accept continuous speech. For example, a
common command may be "Move.... Forward.... Fast....
Start. Proposed design for home automation system and
Matlab based Hindi key word speech recognition system is
for disabled persons, as they are not able to move from one
place to other and can‘t locate switches. This paper attempt to provide them solution, by sitting on wheel chair or bed
they can switch on and off home appliances and also control
internal parameters like wheel chair direction, fan speed,
heater temperature. Physically challenged persons find
difficulty in power ON/OFF their home loads such as fan,
light, AC etc. they require an attendee to do these things. In
the absence of the attendee their world seems to be more
difficult. This design helps the person with physical
disability and elderly to navigate easily within their home in
a wheelchair by giving voice commands. [3-5] designed for
navigation of robot and forklift by giving voice commands. Some of the voice based design uses a voice recognition chip
with integrated or interfaced memory chip that has a
drawback of having limited number of voice commands. The
reported design Speech Recognition Based Wireless
Automation of Home Appliances for Disabled Persons
involves automation home loads by giving voice commands
in a wireless environment.
VI. SYSTEM OVERVIEW
This paper is related to the controlling of the
electronic/electrical equipment using voice key words. In
this paper we are going to recognize the Hindi key word of
the person and control the desired parameter. The goal of the thesis is to help the disabled and handicapped persons, who
are not able to locate switches or, not able to reach there.
This thesis can also work as a security purpose by operating
the machines through the voice and will be operated by only
one person. This can also work for the home atomization and
replace the switches and remotes by the voice command.
This is done using software designed in Matlab 7.5 using
MFCC and DTW. By using this software Hindi key words
spoken in real time will match with pre recorded samples
and generate ASCII code. These ASCII codes send to
microcontroller using serial communication RS-232. All peripherals are controlled by the microcontroller. The output
of the microcontroller controls the various applications upon
receiving the input from the software. The relays are
controlled on the ports of microcontroller to activate a
particular appliance connected to the particular port.
www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 17
Fig. 1: Microcontroller Interfacing.
Automatic speech recognition system and home
automation system port connection with external peripherals
is shown in Table 1. All peripheral are connected to
corresponding port pin of microcontroller (89C52) as given in Table 1. These peripherals work according to our program
and discussed in software design section. When command
word given by user through microphone it is recognized by
proposed algorithm and ASCII code will be generated. These
ASCII code given to 89C52 microcontroller, if recognized
code match then appliance will perform particular operation
related to that key word.
TABLE 1: MICROCONTROLLER PORT CONNECTION
S.N. Ports of 89C52
µc
Hardware Devices
Control
Hindi Key
Word
1 P1.0, P1.1, P1.2 ADC BAND
2 P1.4 Temperature 30 deg. TIESH
3 P1.5 Temperature 50 deg. PACHAS
4 P1.6 Go AAGE
5 P1.7 Reverse PICHE
6 P1.6, P1.7 Break RUKO
7 P2.2 Fan low set DHEERE
8 P2.3 Fan medium set TEJ
As we can see in table 1, if AAGE key word recognized
then Port 1.7 goes logic one and Port 1.6 goes logic zero.
Which means that robot moves in forward direction. The logic one and logic zero position of the port is shows in table
1 for corresponding key word.
VII. HARDWARE DESIGN
a) Voice processor:
Next stage is voice processor stage consisting of .m
voice processor file. After comparison in voice processor
data is send to microcontroller for control or driving action,
we are using RS232 as application communication protocol.
The whole process goes in the following manner e.g. if we
say AAGE key word the action related to ―AAGE key word‖
has to performed and if we say ―PICHE key word‖ then the action related to PICHE key word has to be performed. As
shown in figure 2 when we say key any word the
microphone takes analog signal and converts it to the
electrical signal then attenuation of the signal is performed
by the attenuator. Attenuated signal is transferred to the voice processor, these files are executed and an ASCII code
is then transferred to the microcontroller through the RS 232
standard communication protocol. In this manner the voice
will hold the control action of the machine or the electric
appliance.
Fig. 2: Speaker recognition process.
b) Temperature sensor circuit:
We can use wide range of supply voltages lies between
single supply 3 V to 30 V (LM2902 and LM2902Q 3V to
26V), or Dual supplies. Common mode input voltage range
includes ground that allow direct sensing to near ground.
The low supply current drain is independent to the supply
voltage 0.8 mA Typ. Low input bias and offset parameters
includes input offset voltage 3 mV Typ. input offset current
2 nA Typ. input bias Current 20 nA Typ. differential input
voltage range equal to maximum rated supply voltage 32 V open loop differential voltage amplification 100 V/mV Typ.
Fig. 3: LM 35 Interface.
c) Analog to digital converter:
Analog to digital converter device is a high current four
channel driver designed to accept standard DTL or TTL
logic levels, monolithic integrated high voltage and drive inductive loads (such as relays solenoids, DC and stepping
motors) and switching power transistors. To simplify use as
two bridges each pair of channels is equipped with an enable
input. A separate supply input is provided for the logic,
allowing operation at a lower voltage and internal clamp
diodes are included. This device is suitable for use in
switching applications at frequencies up to 5 kHz. The
L293D is assembled in a 16 lead plastic package which has 4
center pins connected together and used for heat sinking.
The L293DD is assembled in a 20 lead surface mount which
has 8 center pins connected together and used for heat
sinking. 600Milli amperes output current capability per channel, 1.2A peak output current per channel, enable
facility, over temperature protection, logical input voltage up
to 1.5 V, internal clamp diodes.
International Journal of Electrical & Electronics Engineering 18 www.ijeee-apm.com
Fig. 4: Functional block diagram of A to D converter.
d) Building a wireless remote control:
Now question arises that how you can get rid of that
long wired tail dangling out of your remote control robot?
Well, transforming your wired remote control into a wireless one isn‘t as difficult as you may think. The easiest solution
would be to hack those cheap wireless toy cars, take their
electronic guts out and use them in your robot. But if you
want more flexibility, you can build a custom remote control
system. The idea is to use off the shelf RF Tx/Rx modules.
These modules, once a rare commodity, are now widely and
cheaply available. In this particular discussion, we shall be
using ASK (Amplitude Shift Keying) based TX/RX pair
operating at 433 MHz
Fig. 5: ASK Transmitter and Receiver.
The transmitter module accepts serial data at a
maximum of XX baud rate. They can be directly interfaced
to a microcontroller or can be used in remote control
applications with the help of encoder/decoder ICs. The
encoder IC takes in parallel data at the TX side packages it
into serial format and then transmits it with the help of a RF transmitter module. At the RX end, the decoder IC receives
the signal via the RF receiver module, decodes the serial data
and reproduces the original data in the parallel format. Now
in order to control say one motor, we require 2 bits of
information while we need 4 bits of information to control 2
motors. HT12E and HT12D is 4 channel encoder/decoder
ICs directly compatible with the specified RF module.
e) Wheel chair control:
Receiver receives the data in serial form then it decodes
that data and at last it is again converted into parallel form
and given to the receiver side CPU. At the receiver side the
decoder circuit IC HT 12D is used as a decoder. At the
decoder again the codes are received in serial form which
then again converted into parallel form. These decoded signals are then given as an input to CPU. At the receiver
side the IC MN4519 is used as the buffer.
-
CNTRL=0
R30
6K8
VCC
VCC +12V
U10A
NAND2
12 3
VREF
+12V
2K7
VREF
-
2K7
1 2
NOT
12
+
U13
NOT
12
+12V
VREF
NOT
12
+
-
U19C
LM339
9
814
312
PULSE
R31
2K7
TIP-127
TIP-122
+5V
2K7
DIR/1
VCC
TIP-127
PNP
DC--MOTOR
VCC
Q8
NPN
7404
VCC
+
-
U20B
LM339
5
42
312
2K7
+
-
U18A
LM339
7
61
312
+12V
+12V
U14
NOT
1 2
2K7
DC MOTOR CONTROL CARD
2K7
DIR/2
NOT
1 2
+
-
U21D
LM339
11
1013
312
Q11
PNP
7404
PAD4
OCPAD
Q9
NPN
12V
7404
7400
U11B
NAND1
12 3
2K7
2K7
7404
VREF
VREF
+
TIP-122
7400
DIR 1 DIR 2
CONTROL
-
+-
+
+
-
-
+
A B
NAND
NOTCOMP
NAND
NOT
COMP.
NOTCOMP.
1
2
3 adc
4
5
6
7
8
3
Fig. 6: Robot Control.
The nature of this buffer is FIFO that is First In First
Out. In order to drive motors, we would need to connect a
suitable motor driver at the output of the decoder IC. The
motor driver circuit can consist of a Relay, transistorized H-
Bridge or motor driver ICs like the L293D, L298 etc.
VIII. SOFTWARE DESIGN
Keyword recognition algorithm is designed according to
the block diagram as shown in figure below.
Fig. 7: Block diagram of Mel Frequency Cepstral Coefficient
Speech recognition algorithm is written in matlab 7.0 and
results are tested in clean or noisy test data. The explanation
and results are discussed in main program step by step as
shown below:
Step1. Declare variables:
clear all; % clear all variables
close all; % close all files
clc % clear screen ncoeff = 13; %Required number of mfcc coefficients
N = 8; %Number of words in vocabulary
k = 4; %Number of nearest neighbors to choose
fs=16000; %Sampling rate
duration1 = 0.15; %Initial silence duration in seconds
duration2 = 2; %Recording duration in seconds
G=2; %vary this factor to compensate for amplitude
variations
NSpeakers = 5; %Number of training speakers
Step2. Input Keyword and perform EPD:
www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 19
Fig. 8: End Point Detection for Hindi Key word ―AAGE‖.
for i=1:8; % Check real time 8 keywords
fprintf('Press any key to start %g seconds of speech
recording...', duration2);
pause; % Wait for 0.15 second
silence = wavrecord(duration1*fs, fs); %Record keyword
fprintf('Recording speech...'); speechIn = wavrecord(duration2*fs, fs); % duration*fs is the
total number of sample points
Fig. 9: After End Point Detection for Hindi Key word
―AAGE‖.
Step3. Addition of silence:
p=length(speechIn)-length(silence);
for i=1:p
silence=[silence ;0]; end
fprintf('Finished recording.\n');
fprintf('System is trying to recognize what you have
spoken...\n');
speechIn1 = [silence;speechIn]; %pads with 150 ms
silence
speechIn2 = speechIn1.*G;
Fig. 10: Addition of silence 0.15 seconds in Hindi key word
―AAGE‖.
Step4. Noise Reduction:
speechIn3 = speechIn2 - mean(speechIn2); %DC offset
elimination
speechIn = nreduce(speechIn3,fs); %Applies spectral
subtraction
Fig. 11: After noise reduction for Hindi key word ―AAGE‖.
Step5. Windowing, DFT and Mel filter bank:
rMatrix1 = mfccf(13,speechIn,fs); %Compute test feature
vector
Fig. 12: Shows the time signal of the Hindi key word AAGE
and Mel filter bank of the word computed via FFT.
Step6. Inverse DFT:
rMatrix = CMN(rMatrix1); %Removes convolutional noise
Sco = DTWScores(rMatrix,N); %computes all DTW scores
[SortedScores,EIndex] = sort(Sco); %Sort scores increasing
K_Vector = EIndex(1:k); %Gets k lowest scores
Neighbors = zeros(1,k); %will hold k-N neighbors
Fig.13: DCT and Spectrogram for ‗AAGE‘ Key Word.
% Code below uses the index of the returned k lowest scores
to determine their classes
for t = 1:k
u = K_Vector(t);
for r = 1:NSpeakers-1
if u <= (N)
break
else u = u - (N);
end end
Neighbors(t) = u;
end
International Journal of Electrical & Electronics Engineering 20 www.ijeee-apm.com
Fig.14: Result for keyword recognition ‗AAGE‘ Key Word.
%Apply k-Nearest Neighbor rule Nbr = Neighbors[Modal,Freq] = mode(Nbr); %most frequent
value
Word = strvcat('Forward-AAGE', 'Reverse-PICHE', 'Break-
RUKO', 'Thirty-TEESH', 'Fifty-PACHAS', 'low-DHERE',
'Medium-TEJ', 'Stop-BAND');
if mean(abs(speechIn)) < 0.01
fprintf('No microphone connected or you have not said
anything.\n');
elseif ((k/(Freq)) > 2) %if no majority
fprintf('The word you have said could not be properly
recognised.\n');
else fprintf('You have just said %s.\n',Word(Modal,:)); %Prints
recognized word
end
IX. RESULT DISCUSSION
We made two experiments, in noise and in clean
environment one using traditional method (Md. Rashidul
Hasan et al. 2004) and the other using the developed
technique. The templets were used as input to the same
recognition system using DTW in order to measure the
performance for each method. First experiment uses the
traditional method (Md. Rashidul Hasan et al. 2004). The
dictionary contains Hindi key words and digits. For each
hindi key word and digits were selected a number of
templates from several training candidates (4-10) and second
experiment use 8 templates. A new generated template was
used for each key word and digit. Both experiments were
speaker dependent. The test was made using 8 test records
for each key words and digits. The accuracy for Hindi Key
Word recognition is calculated by speaking one command 10 times and find out how many times it recognize Key
Words with different rate of speech. Chart shows
approximately 91.25 % accuracy with end point detection
when user 1 say key Word in 10 × 12 room with noise
environment (Fan On, Tv On, and Cooking in Kitchen) and
without end point detection average accuracy is 80.00 %.
Figure shows chart for Hindi key word recognition in noise
environment with or without EPD.
Fig. 16: Shows results in chart for clean environment with or without EPD.
Chart shows approximately 97.50 % accuracy with end
point detection when user 1 say key Word in 10 × 12 room
with clean environment (Fan Off, Tv Off, No Cooking in
Kitchen) and without end point detection average accuracy is
87.50 %. Figure 2 shows chart for hindi key word
recognition in clean environment with or without EPD. After
calculating MFCC features, DTW finds nearest distance
between spoken word and recorded samples of 10 speakers.
If nearest distance of recorded samples matches with five or
more samples then it will show output and related to key word operation performed, if match is below five samples
then play recording word not recognized please try again.
X. CONCLUSION AND FUTURE WORK
This paper presents a simple technique for word
detection using end point detection, feature extraction using
Mel frequency cepstral coefficient and feature matching
using dynamic time warping. The implemented algorithm
and control system control fan speed, temperature of heater
and robot direction using the voice key word. It
demonstrates its reliability and ease of future development. Based on obtained experimental results it demonstrates that
the proposed algorithm is indeed functional and it can be
used in voice key word recognition home automation system
and industrial robots. Percentage of correct recognition of
key word is high enough. The recognition results are tested
for clean and noisy test data. The system can be said to be
robust as average accuracy for clean data is 97.50 while that
for noisy data is 91.25 %.
The main contribution of this study is that it presents the
idea of Hindi key word recognition and Home Automation
0102030405060708090
100
WITH EPD
(91.25%)
WITHOUT
EPD (80.00 %)
0
20
40
60
80
100
AA
GE
PIC
HE
RU
KO
DH
EE
RE
TE
J
TE
ES
PA
CH
AS
BA
ND
WITH EPD (97.50%)
WITHOUT EPD (87.50%)
www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 21
system. The experiments also show that the approach is good for Hindi key word recognition. The proposed ASR and
Control System was completely implemented, our effort will
be directed toward developing the more appropriate and
convenient method.
REFERENCES [1] A. Rathinavelu, G.Anupriya, A.S.Muthanantha murugavel,
“Speech Recognition Model for Tamil Stops”, Proceedings of the World Congress on Engineering, ISBN:978-988-98671-5-7, Vol I, pp. 543 – 547, July 2 - 4, 2007.
[2] Adriana. Tapus and Brian Scassellati, “The grand challenges
in helping humans through social robotics”, IEEE Robotics & Automation Magazine, Vol 14, Issue 1, pp. 35–42, 2007.
[3] Anjli Bala, Abhijeet Kumar and Nidhika Birla, “Voice Command Recognition System Based on MFCC and DTW”, International Journal of Engineering Science and Technology, ISSN: 0975-5462, Vol. 2, No 12, pp. 7335-7342, Dec. 2010.
[4] Atanas Ouzounov (2010) “Acestral Feature and Text Dependent Speaker Identification-A Comparative stdy”, Cybernetics and Information Technologies, Vol. 10, No. 1, pp.
1-12, 2010. [5] B. H. Juang and Lawrence R. Rabiner, “Automatic Speech
Recognition – A Brief History of the Technology”, Vol. 10, No. 3, August 2004
[6] Bengt J. Borgstrom, “HMM-Based Reconstruction of Unreliable Spectrographic Data for Noise Robust Speech Recognition”, IEEE Transactions on Audio and Language Processing, Vol. 18, No. 6, pp. 1612-1623 August 2010.
[7] Bharti W. Gawali, Santosh Gaikwad, Pravin Yannawar, Suresh C.Mehrotra, “Marathi Isolated Word Recognition System using MFCC and DTW features”, ACEEE Int. J. on Information Technology, Vol. 01, No. 01, Mar 2011.
[8] Cini Kurian and Kannan Balakrishnan, “Automated Transcription System for Malayam Language”, International Journal of Computer Application, Vol. 19, No. 5, April 2011.
[9] F. K. Soong, A. E. Rosenberg, L. R. Rabiner and B. H. Juang,
“A Vector Quantization Approach to Speaker Recognition”, Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '85, vol 10, No 3, pp. 387-390, 1985.
[10] Fausto ―Tito‖ Poz and Durand R. Begault, “Voice Identification and Elimination Using Aural Spectographic Protocol”, AES 26th International Conference, Denver, Colorado, USA, 7–9 July 2005.
[11] Josef Rajnoha et al. (2011) “ASR systems in Noisy
Environment: Analysis and Solutions for Increasing Noise Robustness”, Radioengineering, Vol. 20, No. 1, April 2011.
[12] K. H. Davis, R. Biddulph and S. Balashek, ―Automatic Recognition of spoken digits”, The Journal of the acoustical society of america, vol 24, No 6, November, 1952.
[13] K. M. Ravikumar, R. Rajagopal and H. C. Nagaraj, “An Approach for Objective Assessment of Stuttered Speech Using MFCC Features”, DSP Journal, Volume 9, Issue 1, June, 2009.
[14] Khalid Saeed, “Sound and Voice Verification and Identification A Brief Review of Töeplitz Approach”, Znalosti 2008, pp. 22-27, 2008.
[15] Lindasalwa Muda, Mumtaj Begam and I. Elamvazuthi, “Voice Recognition Algorithms using Mel Frequency Cepstral Coefficient (MFCC) and Dynamic Time Warping (DTW) Techniques”, Journal of Computing, ISSN 2151-9617, Volume 2, Issue 3, March 2010
[16] M. A. Anusuya and S. K. Katti, “Speech Recognition by Machine: A Review”, (IJCSIS) International Journal of Computer Science and Information Security, Vol. 6, No. 3, 2009.
[17] Maayan Geffet, Yair Wiseman and Dror Feitelson, “Automatic Alphabet Recognition”, Springer Science, Vol. 8, pp. 25–40,
2005. [18] Mark D. Skowronski and John G. Harris, “Improving the
Filter Bank of a Classic Speech Feature Extraction Algorithm”, IEEE Intl Symposium on Circuits and Systems, Bangkok, Thailand, vol 4, pp. 281-284, May 25 - 28, 2003.
AUTHORS
First Author– Abhishek Thakur:
M. Tech. in Electronics and
Communication Engineering from Punjab Technical University, MBA
in Information Technology from
Symbiosis Pune, M.H. Bachelor in
Engineering (B.E.- Electronics)
from Shivaji University Kolhapur,
M.H. Five years of work experience
in teaching and one year of work experience in industry.
Area of interest: Digital Image and Speech Processing,
Antenna Design and Wireless Communication. International
Publication: 7, National Conferences and Publication: 6,
Book Published: 4 (Microprocessor and Assembly Language Programming, Microprocessor and Microcontroller, Digital
Communication and Wireless Communication). Working
with Indo Global College of Engineering Abhipur, Mohali,
P.B. since 2011.
Email: [email protected]
Second Author – Rajesh Kumar is
working as Associate Professor at
Indo Global College of Engineering,
Mohali, Punjab. He is pursuing
Ph.D from NIT, Hamirpur, H.P. and
has completed his M.Tech from GNE, Ludhiana, India. He
completed his B.Tech from HCTM,
Kaithal, India. He has 11 years of academic experience. He
has authored many research papers in reputed International
Journals, International and National conferences. His areas
of interest are VLSI, Microelectronics and Image & Speech
Processing.
Third Author – Amandeep Batth:
M. Tech. in Electronics and
Communication Engineering from Punjab Technical University,
MBA in Human Resource
Management from Punjab
Technical University , Bachelor in
Technology (B-Tech.) from
Punjab Technical University . Six
years of work experience in teaching. Area of interest:
Antenna Design and Wireless Communication. International
Publication: 1, National Conferences and Publication: 4.
Working with Indo Global College of Engineering
Abhipur, Mohali, P.B. since 2008. Email: [email protected]
Fourth Author – Jitender Sharma: M. Tech. in Electronics
and Communication Engineering from Mullana University,
Ambala, Bachelor in Technology (B-Tech.)from Punjab
Technical University . Five years of work experience in
teaching. Area of interest:, Antenna Design and Wireless
International Journal of Electrical & Electronics Engineering 22 www.ijeee-apm.com
Communication. International Publication: 1 National
Conferences and Publication:6 and Wireless
Communication). Working with Indo Global college since
2008.
E-mail: [email protected]
www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 23
IJEEE, Vol. 1, Spl. Issue 1 (March 2014) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
CHAIN BASED WIRELESS SENSOR
NETWORK ROUTING USING HYBRID
OPIMIZATION (HBO AND ACO)
Er.Sadhna 1, Er.Supreet singh
2
1Electronics and Communication Deptt., Swami Parmananad College of Engineering & Tech., Punjab, India 2Electronics and Communication Deptt., Baba Banda Singh Bahadur College of Engineering, Punjab, India
ABSTRACT: In Wireless Sensor Network, due to the
energy restriction of each nodes, efficient routing is very
important in order to save the energy of the hybrid
optimization technique. The results of new protocol i.e.
hybrid have been compared with EEPB and IEEPB.
Simulation results show that the lifetime of Hybrid is better
as compared to EEPB and IEEPB. Throughput has been
increased in the Hybrid since in 50% node mobility EEPB give 1854, IEEPB give 1981 and HEEPB gives 2390 . Thus,
the proposed protocol is more energy efficient as compared
to chain based protocols i.e. EEPB and IEEPB sensor node
and to enhance the lifetime of the network. In this
dissertation, a new Optimization Tech.i.e. HYBRID(ACO
and HBO) with Improved PEGASIS protocol has been
designed.A new approach has been used to overcome the
problem of PEGASIS by using.
KEYWORDS: Wireless sensor network, Energy efficient
PIGASIS based,Improved Energy efficient PIGASIS
based,Hybrid Energy efficient PIGASIS based,Improved
I. WIRELESS SENSOR NETWORKS
A Wireless Sensor Network (WSN) consists of a large
number of tiny wireless sensor nodes (often referred to as
sensor nodes) that are, typically, densely deployed. Ad hoc
networks are defined as the category of wireless networks
that utilize multi-hop radio relaying since the nodes are
dynamically and arbitrarily located. Ad hoc networks are
infrastructure independent networks.
• Sensor Node: A sensor node is the core component of a
WSN. The sensor nodes can take on multiple roles in a
network, such as simple sensing; data storage; routing; and
data processing.
• Clusters: Clusters are the organizational unit for WSNs.
Because of the dense nature of these networks it requires the
need for them to be broken down into clusters to simplify
tasks such a communication [2].
• Cluster heads: Cluster heads are the organization leader of a cluster. They often are required to organize activity in the
cluster. These tasks are not limited to data-aggregation and
organizing the communication schedule of a cluster [3].
•
Base Station: The base station is at the upper level of the
hierarchical WSN. It provide the communication link
between the sensor network and the end-user.
• End User: The data in a sensor network can be used for a
wide-range of applications [1]. Therefore, a particular
application may make use of the network data over the
internet using a PDA or even a desktop computer
Fig1. Wireless Network
II. ENERGY EFFICIENCY IN WIRELESS SENSOR
NETWORKS A sensor network consists of a large number of small, low-
cost devices with sensing processing, and transmitting
capabilities. Main goal of the operation is to observe a region
and gather and relay information to a sink node or set of sink
nodes, called Base Station (BS). Forwarding the data to the
BS is possible in two ways: using direct or multihop
communication. In the first case every sensor transmits its
data directly to the sink; in the second case, the sensors are
communicating with the neighbours that forward the
information in the direction of the sink [3].
The sensors are usually deployed densely and often on-the-
fly. They operate un-tethered and unattended, are limited in power, computational capacities and memory. Because of
these constraints the sensor network must have efficient self-
organizing capabilities, while optimizing energy
consumption. A primary design issue in sensor networks is
energy efficiency. The main goal is to prolong the lifetime of
the network, which can be defined in several ways [4]:
• The time when the first node depletes its battery,
• The time until a given percentage of the sensors has
enough energy to operate,
• The time until a given percentage of the region is
covered by alive sensors.
International Journal of Electrical & Electronics Engineering 24 www.ijeee-apm.com
III. ROUTING PROTOCOLS IN WSN Energy consumption can be reduced by the use of various
techniques like data aggregation, clustering, data-centric
methods, etc. The routing protocols can be classified as flat,
hierarchical or location-based as follow:
Flat networks: In flat networks, all nodes are equal. Hence each node plays the same role. This network
has no logical hierarchy. It uses a flat addressing
scheme. Routing Information Protocol (RIP) is an
example of a flat routing protocol.
Hierarchical networks: In hierarchical networks,
the nodes are partitioned into a number of small
groups called clusters. Each cluster has a cluster
head (CH) which is the coordinator of other nodes.
These CHs perform data aggregation so that energy
inefficiency may be reduced. The cluster heads may
change. The node which has the highest energy acts as the CH. Hierarchical routing is an efficient way
to lower energy consumption within a cluster. It has
major advantages of scalability, energy efficiency,
efficient bandwidth utilization, reduces channel
contention and packet collisions. Low Power
Adaptive Clustering Hierarchy (LEACH), Power
efficient gathering in sensor information and
(PEGASIS), Hybrid Energy-Efficient Distributed
Clustering (HEED), etc. are examples of
hierarchical networks.
PEGASIS
Hierarchical-based routing protocols are widely used for their high energy-efficiency and good expandability. The idea of
them is to select some nodes in charge of a certain region
routing. These chosen nodes have greater responsibility
relative to other nodes which leads to the incompletely equal
relationship between sensor nodes. It is the typical
hierarchical-based routing protocols. As an enhancement
algorithm of PEGASIS is a classical chain-based routing
protocol. chain based protocol saves significant energy
compared with the LEACH protocol by improving the cluster
configuration and the delivery method of sensing data.
However, the PEGASIS protocol also has many problems requiring solutions. In recent years, researchers have
proposed many improved algorithms based on PEGASIS
such as PEG-Ant, PDCH and EEPB et al.
•When EEPB builds a chain, the threshold adopted is
uncertain and complex to determine, which causes the
inevitability of LL if valued inappropriately.
• When EEPB selects the leader, it ignores the suitable
proportion of nodes energy and distance between node and
BS which optimizes the leader selection according to various
application environments. Based on the above analysis, this
paper presents an improved energy-efficient PEGASIS-based
routing protocol called IEEPB. IEEPB compares the distance between nodes twice, finds the shortest path to link the two
adjacent nodes. This chain-building method is more
simplified and effectively avoids the formation of LL
between neighbouring nodes.
IV.WSN USING HYBRID HBO AND ANT
OPTIMIZATION TECHNIQUE
A Wireless Sensor Network (WSN) consists of a large
number of tiny wireless sensor nodes (often referred to as
sensor nodes or, simply, nodes) that are, typically, densely
deployed. Energy efficiency is the most required quality in a sensor network where each node consumes some energy with
each transmission over the network. Energy efficiency is
required to improve the network life. Our proposed work is
defined to improve the energy efficiency in Wireless Sensor
Networks. The two algorithms from Artificial intelligence
will be used in our work. Also the PEGASIS protocol will
be enhanced and then implemented in the WSN scenario. In our work, we will take following parameters into
consideration:
I. Average energy per iteration
II. No of alive nodes per iteration
V. SIMULATION ENVIRONMENT
A 100 node field is used and generated by randomly placing
the nodes in a 100 m x 100 m square area. We assume that
the area contains homogeneous sensor nodes with a
communication range of 45m. The simulation focuses on
number of sensor nodes alive, Average Energy of network
and cost slot per iterations which are important indicators to measure performance of different algorithms. The simulation
parameters used are shown below:
Table 1: Simulation Parameters
Parameters Values
Number of Nodes 100
Area Size 100×100
Base Station (50, 300)
Energy Transmitted 50nj/bit
Energy Received 100pj/bit/m2
Amp Energy 0.0013pj/bit/m4
V1.SIMULATION RESULTS
0 500 1000 1500 2000 2500 30000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Number of rounds
Avera
ge E
nerg
y p
er
round
IEEPB
HEEPB
EEPB
www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 25
0 500 1000 1500 2000 2500 30000
10
20
30
40
50
60
70
80
90
100
Number of rounds
Num
ber
of
alive n
odes p
er
round
IEEPB
HEEPB
EEPB
Table 2 Network life time
VII.CONCLUSION & FUTURE SCOPE
A new enhanced scheme based on artificial intelligence has
been proposed for Wireless Sensor Networks which helps to
improve the energy efficiency as well as lifetime of the
Wireless sensor network. Energy efficiency is the most
required quality in a sensor network where each node consumes some energy with each transmission over the
network. Energy efficiency is also required to improve the
network life. The results of the proposed scheme are
evaluated in MATLAB.The simulation results shows that the
proposed scheme that is hybrid Honey bee optimization and
ant colony optimization with improved PEGASIS has the
better results as compare to previous techniques. In this
proposed work chain complexity is reduced by using hybrid
optimization technique and is more efficient in energy
saving.
In future, the work can be extended by reducing the complexity of chain further by optimizing the energy
parameter along with the distance parameter or the nutrient
function can be changed.
REFERENCES
[1] Z. M Wang, S.Basagni, E.Melachrinoudis and C.Petrioli, ‗‗Exploiting Sink Mobility for Maximizing Sensor Networks Lifetime‘‘, Proceedings of the 38th Hawaii International Conference on System Sciences, IEEE Computer Society, 2005. [2] E. H. Callaway, Wireless Sensor Networks, Architectures and
Protocols, Auerbach Publications, Taylor & Francis Group, Boca Raton, Fla, USA, 2003. [3] Thanos Stathopoulos, R. Kapur, D.Estrin, ―Application-Based Collision Avoidance in Wireless Sensor Networks‖, Conference of Computer society, July-December 2005. [4] K. Padmanabhan, Dr. P. Kamalakkannan,― Energy-efficient Dynamic Clustering Protocol for Wireless Sensor Networks‖, International Journal of Computer Applications, Vol. 38, Issue. 11,
January 2012. [5] S. R. Das, C. E. Perkins, and E. M. Royer, ―Ad hoc on-demand distance vector (AODV) routing‖, IETF Internet draft, draft-ietf-manet-aodv- 13.txt, Feb 2003. [6] S.K Singh, M. P Singh and D K Singh , ―Routing Protocols in Wireless Sensor Networks –A Survey,‖ International Journal of Computer Science & Engineering Survey (IJCSES) Vol.1, No.2, November 2010. [7] P.Tyagi, R.P Gupta, R.K Gill,‖ Comparative Analysis of
Cluster Based Routing Protocols used in Heterogeneous Wireless Sensor Network‖, International Journal of Soft Computing and Engineering (IJSCE), Vol. 1, Issue. 5, November 2011.
Node
mortality
EEPB
IEEPB
HEEPB
1%
387
993
2100
50%
1854 1981 2390
100%
1902 2047 2420
International Journal of Electrical & Electronics Engineering 26 www.ijeee-apm.com
IJEEE, Vol. 1, Spl. Issue 1 (March 2014) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
Technical Recapitulation on LI-FI 1Maninder Singh,
2Dr.Hardeep Singh Saini,
3Dr. Pooja Sahni
1,2,3Indo Global college of Engineering, Punjab, India [email protected],
Abstract— Li-Fi stand for ―light fidelity‖, it is a wireless optical networking technology that uses light-
emitting diodes (LEDs) for data transmission. Li-Fi is
different from Wi-Fi that transmits data by using the
spectrum of visible light. As with increasing in demand for
wireless application and data rates associated with it we
explain the use of Li-Fi as a wireless technology for large
deployments, in the paper we present the introduction to
Li-Fi ,its history, architecture, features of Li-Fi and at the
last a conclusion is concluded.
1. INTRODUCTION
Li-Fi stands for ―light fidelity‖. Like Wi-Fi it is method of
transmitting data form one section to another wirelessly.
But in the case of Wi-Fi uses Radio Waves for
transmitting the data and in the Li-Fi uses Light to
communicate data/transmitting data. It is 5G[1] visible
light communication systems technology which using light
from light-emitting diodes (LEDs) as a medium to
transport networked, mobile and the high-speed
communication. All these function same as Wi-Fi and Optical fiber [2]. It leads to the Internet of things, in
which every electronic devices are connected with the
internet and the LED lights which used as an internet
access point [3].
Today in the market of Li-Fi, its annual growth rate is
near about 82% from 2013 to 2018 and to be worth over
$6 billion per year by 2018[4]. VLC, which means
―Visible light communications‖, VLC signals work by
switching the bulbs on and off within nanoseconds [5]. In
Li-Fi bulbs are kept on to transmit end in which it transmit
the data, the bulbs could be dim to the purpose that they weren't visible to the humans and but still purposeful [6].
The light wave‘s which cannot go through(penetrate) the
walls which makes a much shorter range, so this features
make it more secure from hacking, relative to the Wi-
Fi[7][8]. In the Li-Fi to transmit the signal line of sight is
not necessary and light reflected off of the walls can attain
70 Mbps [9]. The advantage of Li-Fi is that the actinic ray
is much a lot of plentiful than the spectrum (10,000 times
a lot of in fact) and may attain so much larger information
density (fig1).
Fig1. Spectrum radio versus light [20]
2. HISTORY
The University of Edinburgh in the UK, Professor Harald
Haas, is the original founder of Li-Fi [10]. Li-Fi is a VLC
(visible light communication) which includes use of the
visible light section of the electromagnetic spectrum to
transmit the information of the signals. At Edinburgh's
Institute, the D-Light project for Digital Communications
was funded from Jan 2010 to Jan 2012[11].
Professor Harald Haas, promoted this technology in his
2011 TED Global talk [12]. Pure Li-Fi, formerly pure
VLC, it's basically a creative instrumentality manufacturer
(OEM) firm discovered to commercialize Li-Fi
merchandise for integration with conferred LED-lighting
systems [13][14]. In the Consumer Electronics Show in
Las Vegas from January 7–10 in 2014, the first Li-Fi Smartphone prototype was presented. The phone uses
Sun Partner‘s Wysips CONNECT. it's a way that converts
light waves into useful energy and creating the phone
capable of receiving and cryptography the signals simply
while not drawing on its battery [15][16].
In this a flimsy layer of precious stone glass might be
added to little screens like watches and cell phones that
make them sun based fueled. Cell phones could build 15%
more battery life throughout a regular day. This first cell
phones utilizing this kind of engineering ought to show up in 2015. This screen can additionally work to acknowledge
Li-Fi signs thus can the cell phone Polaroid [17] .The sort
screens cost for every cell phone is between $2 and $3,
which is much less expensive than most new engineering
[18]. What's more this sort innovation is introduced in
display centers and organizations crosswise over France,
and is continuously grasped by EDF, which is one of the
country's biggest utilities [17]
For shoppers at stores, the Philips lighting company has
developed a Li-Fi system. In this they can easily download
an app on their smart phone and then their smart phone works with the LEDs in the store. It can pinpoint where
they are at in the store and give them corresponding
coupons and information based on where aisle they are on
and what they are looking at [19].
3. WORKING OF LI-FI TECHNOLOGY
The working of Li-Fi engineering is basic. Form the fig2
we seen that a light source toward one side like a LED and
a photograph identifier (Light Sensor) on the flip side.
www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 27
Fig2. Working of Li-Fi Technology [20]
The point when LED begins gleaming, photograph finder or light sensor on flip side will discover light and get a
paired 1 generally double (otherwise) 0. How can
information be transmit by means of this new Li-F-
engineering? Blazing a LED sure times will advance a
message to transmit. Blazing of light is located by the
photograph indicator or light sensor and it will gain a
message.[20]
When a relentless current is applied to Associate in
Nursing LED light-weight bulb a relentless stream of
photons area unit emitted from the bulb that is discovered
as actinic radiation. If this is varied slowly the output intensity of the sunshine dims up and down. As a result of
LED bulbs area unit semi-conductor devices, this, and
therefore the optical output, is modulated at
extraordinarily high speeds which might be detected by a
photo-detector device and reborn back to electrical
current. The intensity modulation is indiscernible to the
human eye, and so communication is simply as seamless
ad RF. mistreatment this system, high speed info is
transmitted from Associate in Nursing LED light-weight
bulb.Radio frequency communication needs radio circuits,
antennas and sophisticated receivers, whereas Li-Fi is far easier and uses direct modulation ways kind of like those
utilized in inexpensive infra-red communications devices
like device units. Infra-red communication is restricted in
power as a result of eye safety needs, whereas LED light-
weight bulbs have high intensities and may come through
terribly massive information rates [20].
Fig3. Show how Li-Fi transmit and receive signal [20]
Now, think about many LEDs with some totally different
colours, flashing along and building a large info to
transmit. it's ascertained that inexperienced optical maser with the red optical maser will transmit knowledge at one
GBPS.
Binary information is made up of strings of 1‘s and 0‘s.
Any light source can transmit this ON and OFF
information but LEDs are capable of height flickering
speed. Light receivers interpret the flickering LED as 1‘s
and 0‘s and thus we have our Li-Fi, light off=0 and light
on=1. Why is Li-Fi so much faster? Because visible light
is far more dense than radio waves 10,000 times (fig1)
more dense in fact, meaning much more data can be
transferred. What is so special about Li-Fi? The speed, the highest speed yet recorded with a Li-Fi connection is
10Gbt/s which is 250 times faster than the average
broadband speed [21],[22] .This estimate is with high –end
instrumentality, but industrial Li-Fi being created in china
is at concerning a hundred and fifty Mbps, that continues
to be ten times above the typical United Kingdom of Great
Britain and Northern Ireland affiliation speed. Some
specialists claim that Li-Fi represents the longer term of
mobile net, its reduced prices and larger potency compared
with Wi-Fi [4][5].
Wi-Fi and Li-Fi each transmit information over the
spectrum, however whereas Wi-Fi uses radio waves and
whereas Li-Fi uses visible radiation. This is often a
advantage therein the visible radiation is way additional
plentiful than the spectrum (10,000 times additional in
fact) and may attain way larger information density [7][8].
A venture between the universities of Strathclyde,
Edinburgh, Cambridge‘s Andrews and, Oxford during this
analysis was administered by the Ultra Parallel visible
radiation Communications project and funded by the
Engineering and Physical Sciences analysis Council
[23].The existing light-emitting diode lightweight bulbs can be reborn to transmit Li-Fi signals with one
semiconductor device, and therefore the technology would
even be of use in things wherever radio frequencies can't
International Journal of Electrical & Electronics Engineering 28 www.ijeee-apm.com
be used for concern of meddling with electronic
equipment.
4. ADVANTAGES
1. Li-Fi has higher speeds than Wi-Fi [4] [5]. 2. It has 10000 times the frequency spectrum of
radio [23].
3. Li-Fi safer than Wi-Fi with hackers unable to access unsecured net connections from out of
sight of the transmitter [9].
4. Because Li-Fi does not use radio waves as Wi-Fi
does. It does not interface with radio
communication; this means Li-Fi can be used
safely during flights [7] [8].
5. Although Wi-Fi can penetrate walls, this is not
always desirable, Li-Fi can prevent internet
piggybacking and may offer a more secures
connection for those in for example intelligence
agencies r embassies [7] [8].
6. For project that deal with massive amount of data such as at CERN with large Hadron collider. Li-
Fi s the clear winner over Wi-Fi.and wired
connections.
5. CONCLUSION
Li-Fi technology might change larger space of coverage
than one Wi-Fi router thanks to all the lights in and around
a building. The Drawbacks to the technology embrace the
necessity for a transparent line of sight, difficulties with
quality and therefore the demand that lights continue for
operation. The probabilities area unit varied and may be explored additional. If his technology may be place into
sensible use, each bulb may be used one thing sort of a
Wi-Fi hotspot to transmit wireless knowledge and that we
can proceed toward the cleaner, greener, safer and brighter
future. The thought of Li-Fi is presently attracting a good
deal of interest, not least as a result of it's going to provide
a real and really economical various to radio-based
wireless. As a growing variety of individuals and their
several devices access wireless net, the airwaves have
become progressively clogged, creating it a lot of and
tougher to induce a reliable, high-speed signal. This could
solve problems like the shortage of radio-frequency information measure and additionally enable net wherever
ancient radio based mostly wireless isn‘t allowed like craft
or hospitals. One amongst the shortcomings but is that it
solely add direct line of sight.
REFERENCES
[1] The University of Edinburgh and National Instruments
Collaborate on Massive MIMO Visible Light Communication Networks to Advance 5G, Cambridge Wireless, 20 November 2013. [2]Light bulbs could replace your Wi-Fi router, Digital Trends, 30 October 2013, Joshua Sherman.
[3]Tech firm sees the light with £3m funding, The Scotsman, Peter Ranscombe, 24 December 2013. [4]Visible Light Communication (VLC)/Li-Fi Technology Market worth $6,138.02 Million - 2018, New International, 13 November 2013. [5] LiFi beats Wi-Fi with 1GB wireless speeds over pulsing LEDs, gearburn, 13 January 2013, Jacques Coetzee. [6]Condliffe, Jamie (28 July 2011). "Will Li-Fi be the new Wi-
Fi?". New Scientist.
[7] Li-Fi – Internet at the Speed of Light, by Ian Lim, the gadgeteer, dated 29 August 2011. [8] "Visible-light communication: Tripping the light fantastic: A fast and cheap optical version of Wi-Fi is coming". The Economist. 28 January 2012. Retrieved 22 October 2013.
[9] The internet on beams of LED light, The Science Show, 7 December 2013. [10] The Future‘s Bright, The Future‘s Li-Fi, Calendonian Mercury, 29 November 2013. [11] Povey,, Gordon. "About Visible Light Communications". pureVLC. Archived from the original on 18 August 2013. Retrieved 22 October 2013. [12] Haas, Harald (July 2011). "Wireless data from every light
bulb". TED Global. Edinburgh,Scotland. [13] "pureLiFi Ltd". pureLiFi. Retrieved 22 December 2013. [14] "pureVLC Ltd". Enterprise showcase. University of Edinburgh. Retrieved 22 October 2013. [15] Breton, Johann (20 December 2013). "Li-Fi Smartphone to be Presented at CES 2014". Digital Versus. Retrieved January 16, 2014. [16] Rigg, Jamie (January 11, 2014). "Smartphone concept
incorporates LiFi sensor for receiving light-based data". Engadget. Retrieved January 16, 2014. [17] An Internet of Light: Going Online with LEDs and the First Li-Fi Smartphone, Motherboard Beta, Brian Merchant. [18] Your next phone may charge and receive data through this incredible screen, Digital Trends, 19 January 2014, Jeffrey Van Camp. [19] Philips Creates Shopping Assistant with LEDs and Smart
Phone, IEEE Spectrum, 18 February 2014, Martin LaMonica. [20] http://en.wikipedia.org/wiki/Li-Fi. 21]Li-Fi revolution-: internet connections using light bulbs are 250 times faster than broadband, The Independent, James Vincent, 28 October 2013. [22] 'Li-fi' via LED light bulb data speed breakthrough, BBC News, Matthew Wall, 28 October 2013. [23]High-speed wireless networking using visible light, Spie, Harald Haas, 19 April 2013.
AUTHORS
Maninder Singh is following M.Tech
from Indo Global College of Engineering, India. He has completed B.Tech from
IGCE, Mohali (Punjab), India in the year
2011. He has two year of educational
expertise. Working as Assistant Professor
(ECE) at indo global college of Engineering, Abhipur
(Mohali) since June-2012.His areas of interest are wireless
and mobile communication, Optical communication.
Hardeep Singh Saini obtained his
Doctorate degree in Electronics and
Communication Engineering in 2012. He holds Master‘s degree in Electronic
and communication from Punjab
technical university, jalandhar passed
in 2007. His total experience is 15
year, presently, working as Professor (ECE) and Associate
Dean Academic at Indo Global college of Engineering,
Abhipur (Mohali), PUNJAB (INDIA) since June-2007. He
is author of 5 books in the field of communication
Engineering. He has presented 21 papers in international
/national conferences and published 30 papers in
international journals. He is a fellow and senior member of
various prestigious societies like IETE (India), IEEE, UACEE, IACSIT and he is also editorial member of
various international journals.
www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 29
IJEEE, Vol. 1, Spl. Issue 1 (March 2014) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
Capsulization of Existing Space Time Techniques
1Maninder singh,
2Dr.Hardeep singh Saini
Indo Global College of Engineering, Punjab, India [email protected],
Abstract— In this paper, we explore the fundamental concepts behind the emerging field of space-time coding for
wireless communication system. A space–time code (STC)
is a method which employed to increase the reliability of
data transmission in the wireless communication
systems using multiple transmit antennas. Space–time
code (STC) depends on transmitting
multiple, redundant copies of a data stream to the receiver in
the hope that at least some of them may live the physical
path between transmission and reception section with
reliable decoding.
Keywords— STC; STTC; BLAST;
1. INTRODUCTION
With the increase in demand of increasingly sophisticated
communication services available any-time, anywhere,
wireless communications has emerged as one of the largest
and most rapid and steadfastness sectors of the global
telecommunications industry. A quick look at the status quo
reveals that second and third generation cellular systems supporting data rates of 9.6 Kbps to 2 Mbps uses by a 700
million people around the world subscribe to existing. More
recently, in wireless LAN networks IEEE 802.11, which
provided 11 Mbps rate and attracted more than $1.6 billion
(USD) in equipment sales [1]. The capabilities of both of
these technologies over the next ten years, are expected to
move toward the 100 Mbps – 1 Gbps range [2] and
subscriber numbers to over 2 billion [3]. One of the most
significant technological developments of the last decade,
that promises to play a key role in realizing this tremendous
growth, is wireless communication using MIMO antenna
architectures.
A space time code (STC) which is used in the wireless
communication to improve the reliability of data
transmission. Space Time Code depends on transmitting
multiple, redundant copies of a data beam to the receiver.
The receiver which in the hope that at least one of them may
live the physical path between both transmission and
reception section. Space time code may be further divided
according to coherent STC and non coherent STC. When the
receiver section the channel impairment through training
called coherent STC[4] and in the non coherent is totally opposite to the coherent STC .Coherent STC basically is
used widely and division algebras over for making or
constructing codes[6,5],fig1.1 Space time code diagram.[7]
Fig 1.1Space time code diagram [7]
Space time techniques divide into two main parts (see in the
fig) -:
1) Transmit diversity
2) Spatial multiplexing
Fig 1.2 Classification of space time technique [3]
2.SPACE TIME TECHNIQUES
2.1Transmit diversity
2.1.1 Space time block codes –: The term Space-Time Code (STC) originally got into
existence in 1998 by Tarokh et al. to describe a new two-
dimensional way of encoding and decoding signals
transmitted over wireless fading channels using multiple transmit antennas.
In this technique, data stream of a multiple copies are
transmits across the number of antennas (MIMO) and this
technique improves the reliability of data transfer. Also, the
transmitted signal must transverse a potentially difficult
environment with scattering, reflection, refraction and then it
effects by thermal noise which effects the information or
International Journal of Electrical & Electronics Engineering 30 www.ijeee-apm.com
data in the receiver section. So, space- time coding basically
add all the copies of the received signal, so in this way it can
easily get the information.
It is divided into three sections. First one is flat quasi-static
fading channel is used in communication system operating
under narrow band conditions, second is frequency selective fading channel which is used in wideband communication
system[8].
2.1.1.1 Flat quasi-static channel-:
This is further divided into the first one is the Alamouti code
and second is extended version of Alamouti work on which
accommodates large number of transmit antennas, proposed
by Tarokh et al under the name of orthogonal designs. Lately
is linear depression code of Hassibi et al, which address the
capacity limitation of both of these codes and also support
arbitrary number of transmit antenna.
(a)Almouti Block Code-:
It is introduced to improve link-level performance based on
diversity. It is proposed a simple scheme for a 2*2 matrix
system that achieves a full diversity gain with a simple
maximum likelihood decoding algorithm. It is designed from
the view of diversity gain to increased the multiple antenna
transmission scheme in order to achieve the good
performance. Let in the case where these two transmit
antenna by arranging the input symbols (𝑥1,𝑥2) and input
their complex conjugates in a special 2*2 matrix.
𝑆 = 𝑥1 −𝑥2
∗
𝑥2 𝑥1∗
Each column of 𝑆 contains the symbols transmitted from the pair of antennas during a particular symbol period. We see
that second column is a permutation and a reflection of the
complex conjugate of the first. Then 𝑆 over flat fading channel, written as: where P is the appropriate permutation
reflection matrix.
ℎ−𝑇𝑆 = [ℎ−𝑇 x ℎ−𝑇P𝑥∗ ]
[(ℎ−𝑇𝑆 )1 (ℎ−𝑇𝑆 )2
∗] = ℎ−𝑇 (ℎ−𝑇P)∗]x
The principle of space time block coding with 2 transmit
antenna and one receive antenna is explained in the post
on Alamouti STBC. With two receive antenna‘s the
system can be modeled as shown in the figure below (fig2.1).
Fig2.1: Transmit 2 Receive Alamouti STBC
The Alamouti space-time block coding is a simple MIMO
technique which can be used to reduce the BER of a
system with a specific SNR and without any loss on the
data rate/information. The presented decoding technique is
called hard decision-based zero forcing and it is easily to
implement in hardware slot. [9]
(b)STBC based orthogonal design-:
It is basically advanced version of Almouti`s work. It
removes the capacity limitations. It also provides full
diversity gain. Example: the code N=U, transmit antenna is
given by
𝑆 =
𝑥1 −𝑥2−𝑥3 −𝑥4
𝑥2 𝑥1𝑥4 −𝑥3
𝑥3
𝑥4
−𝑥4
𝑥3
𝑥2 𝑥2
−𝑥2 𝑥1
We seen that each column of S differ from the first by
permutation reflection. Next, we consider a generalized real
orthogonal design, for N=3 transmit antenna.
𝑆 = 𝑥1 −𝑥2
−𝑥3 −𝑥4
𝑥2 𝑥1𝑥4 −𝑥3
𝑥3 −𝑥4𝑥1 𝑥2
It views like a counter intuitive at first complex orthogonal
design only exist for N=2 , namely the Almounti`s STBC .
Therefore generalized complex orthogonal design is derived
and various codes are constructed. So, generalized design for
N=4 is given by
𝑆 =
𝑥1 −𝑥2
𝑥2 𝑥1
−𝑥3 −𝑥4 𝑥1∗ −𝑥2
∗ −𝑥3∗ −𝑥4
∗
𝑥4 −𝑥3 𝑥2∗ 𝑥1
∗ 𝑥4∗ −𝑥3
∗
𝑥3 −𝑥4
𝑥4 𝑥3
𝑥2 𝑥2 𝑥3∗ −𝑥4
∗ −𝑥2∗ 𝑥2
∗
−𝑥2 𝑥1 𝑥4∗ 𝑥3
∗ −𝑥2∗ 𝑥1
∗
L=8 symbol periods are required to transmit Q=4 symbols, resulting in a significantly reduced rate but increased the
capacity offered by competitive MIMO scheme such as
BLAST [10,11]. STBC based on amicable designs, which
provide higher rates than those based orthogonal design for
some numbers of transmit and receive antennas [12] and
quasi-orthogonal STBC, which sacrifice diversity to achieve
rate 1 for some condition with more than two transmit
antennas.[13]
(c)Linear dispersion code-:
This is used to realize rates higher than 1 sym\s\hz\ using STBC transmission, Hassibi et.al. Study the effective
capacity of code based on orthogonal design. It basically
develops a new class of block code designed to maximize
the mutual information between the transmitted and received
signals. The resulting designs are called linear dispersion
codes. Codes for using a set of 2Q dispersion matrices
𝑆 = (𝑥𝑅𝑞𝑄𝑞=1 𝐴𝑞 + j𝑥𝐼𝑞𝐵𝑞 )
(1)
Where R stand for real part of complex valued structure and
it is imaginary part. For instance if Q=2 and
𝐴1 = 1 00 1
, 𝐵1 = 1 00 −1
, 𝐴2 = 0 −11 0
, 𝐵2 = 0 11 0
www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 31
then the linear combination of (1) gives
𝑆 = 𝑥𝑅1 + 𝑗𝑥𝐼1 −𝑥𝑅2 + 𝑗𝑥𝐼2
𝑥𝑅2 + 𝑗𝑥𝐼2 −𝑥𝑅1 − 𝑗𝑥𝐼1
= 𝑥1 −𝑥2
∗
𝑥2 𝑥1∗
The limitation of LDC is that good designs are not known to
follow systematic or algebraic rules.[14]
2.1.1.2 Frequency Selective Fading Channel:
It is used in STBC for transmission over frequency selective
or multipath fading channel. In this there are two main parts,
in the first class are those techniques for single-carrier
modulation techniques systems that focus on reducing
equalization complexity and this techniques known as time –
reversal approach by LindsKog et al. that takes benefit of space- time code structure to decrease the dimensionality of
the equalization step.
The second classes of techniques are built around block
processing operations that effectively convert the frequency
selective channel into a set of flat fading sub-channels.
These may employ OFDM with multi-carrier modulation or
Frequency Domain Equalization with single-carrier
modulation.
(a)Time Reversal (TR) STBC-:
This technique is used for single-carrier modulation system
which focuses on reducing equalization complexity. The proposal in this area is a time-reversal approach by
Zindskog.et.al that takes advantage of the space time code
structure to decrease the dimension of the equalization step.
It is flat fading channel based on orthogonal design. They
are designed for use with single-carrier modulation in
which it simplifying the equalization by decoupling the
problem from LN dimension to N L-dimensional tasks
which may be executed in parallel. The TR-STRC involves
protecting data symbol columns by enclosing each of them
between guard columns of known symbols.
They will refer to these guard blocks as the prefix and suffix, both must be of length at least K - 1, and denote by the net
length of the protected data block. It is clear that there is
some rate loss associated with the guard blocks, which can
be reduced by increasing the size of the data block.
However, the maximum size of the data blocks is also
limited by the coherence time of the channel 2 In addition
𝐿 , data columns where complex conjugation is applied in the underlying code are transmitted in time-reversed order,
hence the name given to the code. The accompanying guard
blocks are also conjugated and time-reversed. The
transmitted signal matrix has the following general structure:
It have seen that channel be slowly fading so that
𝐿 = 𝐿0[𝐿 + 2(K-1)] symbol periods, whereas before 𝐿 denotes the gross block length including guard symbols and
𝐿0 is the block length of the underlying STBC design for flat
fading.
The main limitation of the TR-STBC is its limited rate
compared to the potential multiplexing gain available in the
MIMO channel. [15,16]
(b)STBC with frequency domain processing-: A number of researchers have also considered extensions of
the Alamouti scheme to systems using frequency domain
processing. One of the first proposals for combining STBC
with OFDM and multi-carrier modulation was put forward
by Mudulodu et al. Subsequently, two works based on
single-carrier transmission systems with frequency domain
processing at the receiver were presented by Al-Dhahir and
Zhou et al. All three approaches share substantially similar
signal matrix structures and thus we will follow [17] here.
In this work STBC over frequency selective fading channels
is proposed in combination with FDE. As we shall see, it
exhibits a structure that bears some resemblance to time-reversal, and thus shares many properties of the TR-STBC.
The transmitted signal matrix is of the form
We note that the rate achieved by this transmission scheme
is fractionally higher than that of the TR-STBC because it
does not require a guard suffix block. [19, 18]
2.1.2 Space time trellis codes (STTC)-:
It is used in the multiple antenna wireless communication. It
transmits multiple redundant copies of a convolutional code
or trellis code distributed over time and with a number of
antennas (MIMO). Then receivers use these multiple,
'diverse' copies of the data to reconstruct the actual
transmitted data. In space time block code, they are able to
International Journal of Electrical & Electronics Engineering 32 www.ijeee-apm.com
provide both coding gain and a better bit error rate
performance. But in space time trellis code they are more
complex than STBCs to encode and decode. They depend on
a viterbi decoder at the receiver where STBCs need only
linear processing. STTC were proposed by Vahid Tarokh et
al. in 1998. Just as trellis codes impose structure within each code word (cover the code space) and also between code
words transmitted in sequence (over time) the diversity gain
of STTCs is determined via a PEP argument. The PEP
expresses the probability of transmitting 𝑆𝑐 and deciding in
favour of 𝑆𝜀 at the decoder. Defining the code word
difference matrix
B = 𝑆𝑐 − 𝑆𝜀 with SVD B = U 𝑉+ and r = rank B
P(𝑆𝑐 → 𝑆𝜀
) < 𝜋𝑖=1 𝑀 𝜋𝑗 =1
𝑟 (𝜎𝑗2 𝑝
4)−1
=(det[𝐵𝐵+])−𝑀(𝑝
4)−𝑀𝑟 (2)
above equation(2) is coding gain of approximately,
𝛾 = [det(𝐵𝐵+)]1
𝑟 is achieved.
Fig 2.2: Space time trellis codes
It has high complexity so this is it main limitation. [20]
Comparison between STBC and STTC-:
STBC STTC
1. It has no coding gain. 1. It has coding gain.
2. Easily decodable by
maximum likelihood
decoding via linear
processing.
2. Conserve capacity
irrespective of the number of
antennas.
3. STBC is simple to design
based on orthogonal
sequences.
3. STTC is difficult to
design.
4.For one receive antenna
and state code, performance
is similar to STTC
4. STTC outperforms with
increasing antennas and
trellis states.
5. Easily lends itself to
industrial applications
because of its simplicity.
5. Complex to organize.
6. Loses capacity with two or
more receive antennas.
6.Conserve capacity
irrespective of the number of
antennas.
2.2 Spatial multiplexing –:
In view of the narrowband nature of the transmission, each
data stream follows only one route to the receiver and there
are no multipath experienced by the individual data streams.
In SM system, the maximum number of modulation symbols
that can be transmitted per symbol, maximum (𝑟𝑠) is given
by
max(𝑟𝑠)) =𝑁𝑡
which implies that the maximum spectral efficiency of an
SM system given by
𝜂𝑚𝑎𝑥 =𝑁𝑡𝑟𝑡 𝑙𝑜𝑔2(M)bps/Hz
Where 𝑟𝑡 s the rate of any conventional coding used in the
spatial multiplexing system and M is the modulation order.In
general, spatial multiplexing is achieved using a concept called layered space-time (LST) coding.[21]
2.2.1 Layered space time (LST)-:
Spatial multiplexing is achieved by raising a concept of
layered space time (LST) coding. Foschini proposed LST
architecture. In LST method, SM can also be achieved using
Eigen beam forming, it is a practical SM technique that is
used in most modern wireless communication system. They
are three main approaches are-:
Bell Laboratory layered space-time (BLAST) family of
techniques-:
a) V-Blast (Vertical-Blast)
b) H- Blast (Horizontal Blast)
c) D-Blast (Diagonal Blast
The type of decoding algorithm that is used is an important
consideration for LST coded SM system. Four decoding
schemes are-:
1) Zero Forcing (ZF)
2) Zero Forcing with interference cancellation (ZF-IC)
3) Linear minimum mean square error estimation
(LMMSE)
4) LMMSE with interference cancellation (LMMSE-IC)
(a) VERTICAL BLAST-:
In V-Blast the information bit stream is processed by an
optional conventional error encoder and then split into 𝑁𝑟
data stream, each of which is separately modulation before
being passed to its respective antenna for transmission. The
use of the adjective vertical in v-blast is a reference to the
fact that the input is split into parallel streams that are
depicted vertically in most diagrams encoder employs its
own modulator the V-blast architecture is capable of
accommodating applications where different data rates are
applied to different layers. Layer with higher data rates
might use higher order modulation schemes so that each
layer would have the same bandwidth (fig 2.2 a).
www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 33
Fig2.2 (a) V-Blast encoding architecture [21]
Since distinct data stream are applied to each of the 𝑁𝑡 layer,
during each use of the channel there are 𝑁𝑡 different
modulation symbols transmitted. Therefore the space-time
code rate associated with the V-BLAST encoder is 𝑅𝑠=𝑁𝑡
and the spectral efficiency is 𝑁𝑡𝑅𝑡 (M)bps/HZ; where M is
the modulation order. In the case of V-BLAST, Loyka and
Gagnon prove that the diversity order varies from (𝑁𝑟-𝑁𝑡+1)
up to 𝑁𝑟 , depending on which layer is being decoded. We see that N*N V-BLAST only achieves a maximum diversity
gain equal to 1, compared with 𝑁𝑡𝑁𝑟 for system with full
diversity. [22, 23]
(b) HORIZONTAL-BLAST (H-BLAST)
The H-BLAST encoding architecture shown in fig 2.2 b , it
is basically similar with V-BLAST but only difference is it
includes separate conventional error encoder on each of the
transmit data stream. In this ―horizontal‖ suggest that the
encoder on each layer perform coding in the time domain,
which can be pictured as being horizontal in the picture,
compared with the space dimension that is depicted being
vertical(fig2.2 b).[24]
Fig 2.2(b) H-Blast encoding architecture[21,25]
(c) DIAGONAL-BLAST (D-BLAST)
The D-BLAST encoding architecture shown in fig 2.2 c, it is
basically similar with H-BLAST but only difference is it
includes a block after the modulator that performs stream
rotation. Let we take a example we assume that 𝑁𝑡=4 and
output are divided into blocks consisting of 𝑁𝑡 consecutive
segments, the output of the four convential encoders are vectors denoted by a, b, c and d and then output of four
encoded segments out of convential encoder 1 by 𝑎1,𝑎2,𝑎3,
and 𝑎4,the next set of four encoded segments by
a5 , a6,a7,anda8 Rather than simply passing the modulated
outputs from each encoder onto its respective antenna, the
stream rotator rotates the modulated segments in a round-
robin fashion by performing two operation: a) it distributes
consecutive sequences of 𝑁𝑡 segments from each encoder
onto each of the antenna; b) the order of the encoders that it
operated on is chosen in a circularly rotated manner rather
than simply sequentially from encoder 1 to 𝑁𝑡.
In D-BLAST, each diagonal layer constitutes a complete
code word then decoding is done layer by layer. The
advantage of this type of BLAST techniques is that the
outputs from each conventional encoder are distributed over
space which provides a grater spatial diversity (fig2.2 c).
[26]
Fig2.2(c) D-Blast encoding architecture [21, 25]
2.2.2 THREADED SPACE-TIME ENCODING (TSTE)-:
TST proposed by El Gamal et al. It was developed to enable
the construction of full rates and full diversity MIMO
transmission by combining layering ideas with constituent
space time codes. it is based on partitioning the space time signal matrix into non-overlapping threads .In this method
mixes the signal more thoroughly across the antennas than
does the D-BLAST diagonal system. The last block is a
spatial interleave, which interleaves the symbols as shown in
fig2.3 in the space time matrix and each shade shows a
International Journal of Electrical & Electronics Engineering 34 www.ijeee-apm.com
thread. We have one code word per thread, in the first
columns the symbols of each layer are not shifted and in
second columns they are shifted once in a cyclic manner. In
the third column they are shifted twice and so on. The 𝑀𝑡
*matrix A contains the symbols transmitted over the Mt transmit antennas for l symbol periods. We can describe
each layer in general by specifying a set of elements from A.
Let L= (𝐿1, 𝐿2,.. 𝐿𝑚𝑡 ) be set of indices specifying the
elements of A. Mathematically LI is defined as[27,28]
𝐿𝑖 = { ([t+i-1]𝑀𝑇 + 1,l): 0≤ 𝑡 ≤ 𝑙}
Fig2.3Threaded Space-Time encoding architecture [21, 25]
3. CONCLUSION
We have study the various types of the space-time codes
techniques in which every techniques it own advantages and
limitation like generally, in the interest of coding gain, we
prefer to use trellis codes instead of block codes within the
space-time architecture, trellis codes provides higher coding
gain but come at the cost of increased decoding complexity.
We have also study that TLST codes yielded the maximum transmit diversity. The V-BLAST which has gained a lot of
popularity because of its simplicity.
REFERENCE [1] CommWeb. Wireless industry statistics, 2001. [2] Ari Hottinen, Olav Tirkkonen, and Risto Wichman. Multi-antenna transceiver techniques for 3G and beyond. John Wiley & Sons, 2003.
[3] Theodore S. Rappaport, A. Annamalai, R. M. Buehrer, and William H. Tranter. Wireless communications: Past events and a future perspective. IEEE Communications Magazine, 40(5):148{161, May 2002. [4] B.A. Sethuraman, B. Sundar Rajan, and V. Shashidhar (October 2003). "Full-diversity, high-rate space-time block codes from division algebras". IEEE Transactions on Information Theory 49 (10)
[5]Marzetta, T.L. and Hochwald, B.M. (January 1999). "Capacity of a mobile multiple-antenna communication link in Rayleigh flat fading". IEEE Transactions of Information Theory 45 (1): 139–157.
[6] V. Tarokh and H. Jafarkhani (July 2000). "A Differential Detection Scheme for Transmit Diversity". IEEE Journal on Selected Areas in Communications 18 (7): 1169–1174. [7] http://en.wikipedia.org/wiki [8] S.M. Alamouti (October 1998). "A simple transmit diversity
technique for wireless communications". IEEE Journal on Selected Areas in Communications 16 (8): 1451–1458. [9] Siavash M. Alamouti. A simple transmit diversity technique for wireless communications. IEEE Journal on Selected Areas in Communications, 16(8):1451{1458, October1998. [10] Vahid Tarokh, Hamid Jafarkhani, and A. Robert Calderbank. Space-time block codes from orthogonal designs. IEEE Transactions on Information Theory, 45(5):1456{1467,July 1999.
[11] Vahid Tarokh, Hamid Jafarkhani, and A. Robert Calderbank. Space-time block coding for wireless communications: Performance results. IEEE Journal on Selected Areas in Communications, 17(3):451{460, March 1999. [12]Girish Ganesan and Petre Stoica. Space-time diversity using orthogonal and amicable orthogonal designs. Wireless Personal Communications, 18(2):165{178, August 2001. [13]Hamid Jafarkhani. A quasi-orthogonal space-time block code.
IEEE Communications Letters, 49(1):1{4, January 2001. [14] Babak Hassibi and Bertrand Hochwald. High-rate codes that are linear in space and time. IEEE Transactions on Information Theory, 48(7):1804{1824, July 2002. [15] Erik G. Larsson, Petre Stoica, Erik Lindskog, and Jian Li. Space-time block coding for frequency-selective channels. In IEEE International Conference on Acoustics, Speech and Signal Processing, volume 3, pages 2405{2408, May 2002.
[16] Erik Lindskog and Arogyaswami J. Paulraj. A transmit diversity scheme for channels with intersymbol interference. In IEEE International Conference on Communications volume 1, pages 307{311, June 2000. [17] Naofal Al-Dhahir. Single-carrier frequency-domain equalization for space-time block-coded transmissions over frequency-selective fading channels. IEEE Communications Letters, 5(7):304{306, July 2001. [18] Shengli Zhou and Georgios B. Giannakis. Space-time coding
with maximum diversity gains over frequency-selective fading channels. IEEE SIgnal Processing Letters,8(10):269{272, October 2001. [19] Sriram Mudulodu and Arogyaswami J. Paulraj. A transmit diversity scheme for frequency selective fading channels. In IEEE Global Telecommunications Conference,volume 2, pages 1089{1093, November 2000. [20] Vahid Tarokh, Nambi Seshadri, and A. Robert Calderbank.
Space-time codes for high data rate wireless communication: Performance criterion and code construction. IEEE Transactions on Information Theory, 44(2):744{765, March 1998. [21] INTRODUCTION TO MIMO COMMUNICATIONS BY JERRY R. HAMPTON CAMBRIDGE UNIVERSITY PRESS, 28-NOV-2013 [22] Gerard J. Foschini, Glen D. Golden, Reinaldo A. Valenzuela, and Peter W. Wolniansky. Simplifed processing for high spectral efficiency wireless communication employing multi-element
arrays. IEEE Journal on Selected Areas in Communications,17(11):1841{1852, November 1999. [23] Peter W. Wolniansky, Gerard J. Foschini, Glen D. Golden, and Reinaldo A. Valenzuela.V-BLAST: An architecture for realizing very high data rates over the rich-scattering wireless channel. In International Symposium on Signals, Systems, and Electronics,pages 295{300, September 1998. [24] Gerard J. Foschini, Dmitry Chizhik, Micahel J. Gans,
Constantinos B. Papadias, and Reinaldo A. Valenzuela. Analysis and performance of some basic space-time architectures. IEEE Journal on Selected Areas in Communications, 21(3):303{320, April2003. [25] SPACE-TIME CODES AND MIMO SYSTEMS BY MOHINDER
JANKIRAMAN ARTECH HOUSE, 01-JAN-2004. [26] Gerard J. Foschini. Layered space-time architecture for wireless communication in a fading environment when using
www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 35
multiple antennas. Bell Labs Technical Journal,1(2):41{59, September 1996. [27] Hesham El Gamal and Jr. A. Roger Hammons. A new approach to layered space-time coding and signal processing. IEEE Transactions on Information Theory, 47(6):2321{2334, September
2001. [28] Hesham El Gamal and Mohamed Oussama Damen. Universal space-time coding. IEEE Transactions on Information Theory, 49(5):1097{1119, May 2003.
AUTHORS
Maninder Singh is following M.Tech
from Indo Global College of
Engineering, India. He has completed B.Tech from IGCE, Mohali (Punjab),
India in the year 2011. He has two
year of educational expertise.
Working as Assistant Professor (ECE)
at indo global college of Engineering, Abhipur (Mohali) since
June-2012.His areas of interest are wireless and mobile
communication, Optical communication.
Hardeep Singh Saini obtained his
Doctorate degree in Electronics and
Communication Engineering in 2012. He holds Master‘s degree in Electronic
and communication from Punjab
technical university, jalandhar passed in
2007. His total experience is 15 year,
presently, working as Professor (ECE) and Associate Dean
Academic at Indo Global college of Engineering, Abhipur
(Mohali), PUNJAB (INDIA) since June-2007. He is author
of 5 books in the field of communication Engineering. He
has presented 21 papers in international /national
conferences and published 30 papers in international
journals. He is a fellow and senior member of various prestigious societies like IETE (India), IEEE, UACEE,
IACSIT and he is also editorial member of various
international journals.
IJEEE, Vol. 1, Spl. Issue 1 (March 2014) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
Optimization of Transmission Schemes in
Energy-Constrained Wireless Sensor
Networks1Vivek Rana,
2Jaspal Singh,
3Leena Mahajan
1,2Rayat Institute of Engineering & Information Technology,Railmajra, Punjab, India. 3Indo Global college of Engineering, Abhipur,Distt. Mohali,Punjab,India
[email protected], [email protected], [email protected]
Abstract- This paper reviews medium access control
(MAC) in wireless sensor network (WSN),and different management methods to save energy.MAC protocol
controls how sensors access a shared radio channel to
communicate with neighbours. This paper discusses design
trade-offs with an emphasis on energy efficiency, latency,
fairness and throughput. One mechanism used to reduce
energy expenditure is to periodically turn off the radio
receivers of the sensor nodes in a coordinated manner. S-
MAC may require some nodes to follow multiple sleep
schedules causing them to wake up mmore often than other
nodes. A typical node in WSN consists of one or more
sensors, embedded processors, moderate amount of
memories and transmitter/receiver circuitry. These sensors are battery powered and recharging of these nodes is very
expensive and normally not possible. The proposed
modification in MAC protocol solves the energy
inefficiency caused by idle listening, control packet,
overhead, and overhearing taking nodes latency into
consideration based on network traffic. The modified
version improves the energy efficiency, latency and the
throughput and hence increases the life span of a wireless
sensor network. Simulation experiments have been
performed to demonstrate the effectiveness of the proposed
approach. This protocol has been simulated in Qualnet 5.0.
Keywords- Wireless Sensor Network, Medium Access
Control, Energy Efficiency , latency, throughput, fairness.
I. Introduction
A wireless sensor network (WSN) of spatially distributed
autonomous sensors to monitor physical or environmental
conditions, such as temperature, sound, pressure, etc. and to
cooperatively pass their data through the network to a main
location. The more modern networks are bi-directional, also enabling control of sensor activity. The development of
wireless sensor networks was motivated by military
applications such as battle field surveillance; today such
networks are used in many industrial and consumer
applications, such as industrial process monitoring and
control, machine health monitoring, and so on.
A WSN generally consists of a host or ―gateway‖ that
communicates with a number of wireless sensors via a radio
link. Data is collected at the wireless sensor node,
compressed, and communicated to the gateway directly or,
if required, uses other wireless sensor nodes to forward data
to the gateway. The gateway then ensures that the data is input into the system. The main function of a wireless
sensor network (WSN) is to collect data from environment
and send it to a reporting site where the data can be
observed and analyzed Each wireless sensor is considered a
node and presents wireless communication capability, along
with a certain level of intelligence for signal processing and
networking data. Depending on the type of application, each
node can have a specific address. Figure 1 represents a
generic block diagram of a node. It usually comprises a
sensing unit, a microcontroller to process data, and a RF
block for the wireless connection. Depending on the
network definition, the RF block can function as a simple transmitter or transceiver (TX/RX). When designing the
nodes, it is very important to pay attention to the current
consumption as well as the processing capability. The
microcontroller‘s memory is very dependent of the software
stack used.
Fig.1: Generic block diagram of a node of a WSN.
Wireless Sensor Networks (WSNs) are an important new
class of networked system.
Dealing with both scale and density is hard enough in ideal environments. Unfortunately, we don‘t have the luxury of
ideal environments with sensor networks. Because sensor
networks are intended to monitor the physical world, they
must often be deployed in natural and uncontrolled
environments. No longer can we assume the carefully
controlled temperature, abundant power, and human
monitoring of server rooms and data centers. Instead,
wireless sensor networks must be designed to operate while
no external power is connected, unattended, irregularly
connected (radios may be turned off for significant periods
of time to conserve power), and uncontrolled environment
[1]. MAC protocols have a significant effect on the function of
WSN. MAC protocol, which builds bottom infrastructure in
www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 37
sensor network systems, decides how to use wireless
channel and allocate limited wireless communication
resources for sensor nodes. MAC protocol, one of the key
network protocols that ensure effective communication in
sensor network, is in the bottom part of the sensor network
protocol and has a great impact on the performance of
sensor network [6].
II. RELATED WORK
A. Proposed S-MAC Protocol Design Challenges
It is necessary to establish communication links between
nodes because a great number of sensor nodes are
distributed to the medium in Wireless Sensor Networks. For this reason, MAC protocol has two aims in WSNs. The first
is to build a sensor network infrastructure. The second is to
share the communication medium in a fair and efficient way
[8].
Attributes that should be taken into consideration in the
design of MAC protocol are listed on below :
Energy efficiency: Energy efficiency is the most important
issue when designing a new MAC protocol in WSNs
because the network‘s lifetime is determined by the nodes‘
energy.
Latency: The elapsed time for sending a MAC-layer data
packet successfully is called ―Latency‖.
Throughput: The ratio of the messages served by
communication systems is called ―Throughput‖.
Robustness: Robustness is composed of the attributes
including reliability, usability, and durability. It shows the
protocol‘s degree of resistance to errors and false
information.
Scalability: Capability of communication system regardless
of the number of sensor nodes performing a transaction and
the size of the network is called ―Scalability‖.
Stability: The ability of communication system to handle
the issue of traffic congestion in the medium that changes
constantly is called ―Stability‖. A stable MAC protocol
should handle sudden loads that can exceed maximum
channel capacity.
Fairness: Bandwidth is limited in most of WSNs
applications, but the base station must receive data equally
from all the nodes. Channel capacity should be fairly shared among the nodes without reducing the efficiency of the
network.
The main goal in our S- MAC protocol design is to reduce
energy consumption, while supporting good scalability,
fairness and collision avoidance. Our protocol tries to
reduce energy consumption from all the sources that we
have identified to cause energy waste. To achieve the design
goal, we have developed the S-MAC that consists of three
major components: periodic listen and sleep, collision and
overhearing avoidance, and message passing. A
modification of the protocol is then proposed to eliminate the need for some nodes to stay awake longer than the other
nodes. The modified version improves the energy
efficiency, latency, fairness and the throughput and hence
increases the life span of a wireless sensor network.
Wireless sensor networks use battery-operated computing
and sensing devices [3]. We expect sensor networks to be
deployed in an ad hoc fashion, with nodes remaining largely
inactive for long time, but becoming suddenly active when something is detected. These characteristics of sensor
networks and applications motivate a MAC that is different
from traditional wireless MACs such as IEEE 802.11 in
several ways [2, 4]: energy conservation and self-
configuration are primary goals, while per-node fairness and
latency are less important. S-MAC uses a few novel
techniques to reduce energy consumption and support self-
configuration. It enables low-duty-cycle operation in a
multi-hop network. Nodes form virtual clusters based on
common sleep schedules to reduce control overhead and
enable traffic-adaptive wake-up. S-MAC uses in-channel
signaling to avoid overhearing unnecessary traffic. Finally, S-MAC applies message passing to reduce contention
latency for applications that require in-network data
processing.
B. S-MAC Protocol
S-MAC [9] is a CSMA –based MAC protocol designed with
a modified IEEE 802.11. Its primary goal is power
consumption. S-MAC supports message transition so that
large-sized packets can be sent more efficiently. The
innovations in this protocol are periodical listening,
reducing collision, preventing unintentional receiving, and
message transition. Nodes generally sleep instead of continuously listening to the medium. Listening and
sleeping times are stable and periodic. There should be a
strict synchronization so that the nodes can move together.
The timing diagram of S-MAC is shown in Figure 2.
Fig. 2. Timing diagram of S-MAC
The Sensor MAC (S-MAC) protocol was introduced in [5]
to solve the energy consumption related problems of idle
listening, collisions, and overhearing in WSNs using only
one transceiver. S-MAC considers that nodes do not need to
be awake all the time given the low sensing event and
transmission rates. S-MAC [3] reduces the idle listening problem by turning the radio off and on periodically. Nodes
are synchronized to go to sleep and wake up at the same
time. In order to address the issue of synchronization over
multi-hop networks, nodes broadcast their schedules to all
its neighbors. This is performed sending a small SYNC
frame with the node schedule periodically. S-MAC divides
time in two parts: the active (listening) part and the inactive
(sleeping) part. The active part is divided at the same time
in two time slots. During the first time slot, nodes are
expected to send their SYNC frames to synchronize their
schedules. The second time slot is for data transmission in
which the S-MAC protocol transmits all frames that were queued up during the inactive part. In order to send SYNC
frames over the first time slot or RTS–CTS–DATA–ACK
frames over the second time slot, nodes obtain access to the
media utilizing the same contention mechanism included in
IEEE 802.11, which avoids the hidden terminal problem
and does a very good job avoiding collisions too. However,
nodes using the IEEE 802.11 protocol waste a considerable
amount of energy listening and decoding frames not
intended for them [4]. In order to address this problem, S-MAC allows nodes to go to sleep after they hear RTS or
CTS frames. During the sleeping time, a node turns off its
radio to preserve energy.
Fig.3: S-MAC frame
C. Problems with S-MAC
The following two problems have been identified in S-MAC
[3] protocol with multiple schedules.
1. Longer listen period
2. Sleep delay
1. Longer listen period
While choosing and maintaining the listen and sleep
schedule some nodes may have to keep wake during the
listen time of more than one schedule [3]. This happens, for
example, if a node,
(A): Before
(B): After
Fig.4: Sleep schedule before and after node M join the network
When it starts up, finds some of its neighbors following one schedules and the rest following another. The nodes
following a shared schedule are said to form a virtual
cluster. Figure 4 shows an example of this situation. Before
node M starts up, two isolated virtual clusters of nodes
exist. Nodes A, B and C follow one schedule (schedule 1);
and nodes X, Y and Z follow another schedule (schedule 2).
The circle around a node indicates the communication range
of the node. When M starts, during its initial listening
spanning a synchronization period, it receives sync frames
corresponding to both the schedules. M will then adopt one of the schedules (e.g. schedule 2) as its own, and announce
this schedule in its sync frames. However, it will also have
to wake up during the listen time of the other schedule.
Thus M has higher duty cycle, and consumes more energy.
2. Sleep delay
Sleep delay introduce extra end to end delay called sleep
delay [3]. Sleep delay increases communication latency in
multihop networks, as intermediate nodes on a route do not
necessarily share a common schedule. In a nutshell, the
difficulty is to make a trade off between sleep delay and optimal active periods.
D. Proposed Modification in S-MAC
In this section we propose a modification of the S-MAC
protocol. The following features were included in the S-
MAC design:
RTS/CTS for hidden terminal problem.
Both virtual and physical carrier sense.
Back off and retry.
RTS/CTS/ACK.
Broadcast packets are sent directly without using
The RTS/CTS reserves the medium for the entire
message.ACK is used for immediate error
recovery.
Node goes to sleep when its neighbor is
communicating with another node. Each node
follows a periodic listen/sleep schedule.
At boot up time each node listens for a fixed Sync
period and then tries to send out a sync packet. It
suppresses sending out of sync packet if it happens
to receive a sync packet from a neighbor and follows the neighbor's schedule.
A node can choose its own schedule instead of
following others, the schedule start time is user
configurable.
Neighbor Discovery: in order to prevent that two
neighbors cannot find each other due to following
complete different schedules, each node
periodically listen for a whole period of the
SYNCPERIOD.
Duty cycle is user configurable.
III. RESULT AND DISCUSSION
The objective of this discussion is to compare the S-MAC
and the modified proposed S-MAC protocol in terms of
energy efficiency, latency, fairness, security and throughput.
We need to have set of protocols to perform successful
communication among different nodes. There are more
steps for design a MAC protocol. First, researchers have to
decide that in which application do they use this protocol.
Because there are more priority such as energy efficiency,
www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 39
latency, fairness, throughput, security. If your first priority
is energy efficiency, you can neglect to more security.
Because, each work for security causes that consumption
and delay. Otherwise, if you develop a protocol which will
be use in military or healthcare applications, you have to
provide security requirements. In order to meet the
application level security requirements, the individual nodes must be capable of performing complex encrypting and
authentication algorithms. Long mechanism of encryption
and decryption should not be kept as they consume more
energy. In WSNs, energy efficiency is the main task. After
measuring the effect of the parameters like power, lifetime
of sensor network, memory, security and type of radio
communication on different protocols, it can be concluded
that these evaluation parameters should be kept in mind
while designing MAC protocol. Simulation results of the
WSN models are presented under varying network load
conditions followed by performance comparisons and
analysis.
A. Measurement of Energy Consumption
We measured the energy consumption in the ten-hop
network. In each test, the source node sends a fixed amount
of data, 20 messages of 100-bytes each. Figure 5 shows that
S-MAC with periodic sleep consumes much more energy
over MAC without sleep, but the proposed MAC achieves
better energy efficiency than the S-MAC protocol.
FIG.5: Energy Consumption
B. Measurement of Average Message Latency
Since S-MAC makes the trade-off of latency for energy
savings, we expect that it can have longer latency under
both the high and low traffic loads due to the periodic sleep
on each node as shown in figure 6(A) and figure 6(B). We consider two extreme traffic conditions, the lowest traffic
load and highest traffic load. Under the lowest traffic load,
the second message is generated on the source node after
the first one is received by the sink. To do this, a
coordinating node is placed near the sink. When it hears that
the sink receives the message, it signals the source directly
by sending at the highest power. In this traffic load, there is
no queuing delay on each node. Compared with the MAC
without sleep, the extra delay is only caused by the periodic
sleep on each node. Under the highest traffic load, all
messages are generated and queued on the source node at the same time. So there is a maximum queuing delay on
each node including the source node. The latency of the
proposed MAC protocol is nearly equal to that of MAC
without periodic sleep but still it doesn‘t reach the shortest
latency.
FIG.6 (A): Average Message Latency under the lowest traffic load
FIG. 6(B): Average Message Latency under the highest traffic load
C. Measurement of Throughput
Just as S-MAC may increase latency, it may also reduce the
throughput. Therefore we next evaluate throughput in the
same 10-hop network. We first consider throughput for the highest traffic load, which is the same as that when
measuring the latency in the highest traffic load. It delivers
the maximum possible number of bytes of data in a unit
time. The results in figure 5 show that for S-MAC as well as
for proposed S-MAC, throughput drops as the number of
hops increases, due to the RTS/CTS contention in the
multihop network.
0
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rgy
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Mod S-MAC
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5
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rage
Lat
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(S)
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No Sleep
S-MAC
Mod S-MAC
FIG.7: Throughput over 10-hops under varying traffic loads.
IV. FUTURE SCOPE OF WORK
During this work we realized that the MAC protocols for
the wireless sensor networks are a hard and extensive area.
Although modification in S-MAC protocol has been
proposed, there is possible future work for system performance optimization. Therefore, some of the planned
work has to be rationalized away for future work. We see
clear paths for future work:
Verification through implementation and
extensive simulations.
Formal descriptions to address other type of MAC
protocols and extension of components.
Cross layer optimization is an area that needs to be
explored more extensively.
REFERENCES
[1] Akyildiz, I.F. ; Su, W. ; Sankarasubramaniam, Y. ;Cayirci, E. (2002)
―A survey on sensor networks‖, IEEE Communications Magazine 40.8
(2002) 102-114.
[2] Brenner, Pablo. (1996) ―A Technical Tutorial on the IEEE
802.11Protocol‖, Breezecom Wireless Communications, July 1996.
[3] Cui, S. ; Goldsmith A. J., and Bahai A., ―Energy-constrained
modulation optimization,‖ IEEE Trans. Wireless Commun., vol. 4, no. 5,
pp. 2349–2360, Sep. 2005.
[4] Ghosh, S.; Veeraraghavan, P.; Singh, S.; Zhang, L. (2009)
―Performance of a Wireless Sensor Network MAC Protocol with a Global
Sleep Schedule‖ International Journal of Multimedia and Ubiquitous
Engineering Vol. 4, No. 2, April, 2009
[5] IEEE Standard 802.11. (1999) ―Wireless LAN Medium Access Control
(MAC) and Physical Layer (PHY) Specifications‖, 1999.
[6] Kodialam, M and Nandagopal T., ―Characterizing achievable rates in
ulti-hop wireless networks: The joint routing and scheduling problem,‖ in
Proc. ACM MobiCom‘03, Sep. 2003, pp. 42–54.
[7] Labrador, M. A.; Wightman, P. M. (2009) ―Topology Control in
Wireless Sensor Networks‖ Springer, USA.
[8]Pottie, G. and Kaiser, W. ―Wireless sensor networks,‖ Communication.
ACM, vol. 43, no. 5, pp. 51–58, 2000.
[9] Ye, W.; Heidemann, J. ; Estrin, D. (2002) ―An Energy-Efficient MAC
Protocol for Wireless Sensor Networks‖, Twenty-First Annual Joint
Conference of the IEEE Computer and Communications Societies
(INFOCOM) 3 (2002) 1567-1576.
AUTHORS
Vivek Rana graduated in Electronics
& Communication Engineering from
Rayat Institute Of Information and
Technology, Railmajra, Punjab. Now
he is a student of M-Tech in
Electronics & Communication
Engineering in Rayat institute of
information and Technology Railmajra, Punjab. His active
research interests include wireless sensor network, Wireless
communication, computer networking & semiconductor
devices.
Jaspal Singh graduated in
Electronics & Communication
Engineering from Baba Banda Singh
Bahadur Engineering College,
Fatehgarh Sahib, Punjab. He has
received his M-Tech degree in
Electronics & Communication
Engineering from Thapar Institute of
Engineering and Technology, Patiala, Punjab. He is
working as Associate Professor and HOD in ECE
department in Rayat Institute of Engineering and
Technology, Railmajra, Punjab. He is a life member of
ISTE. His active research interests include intelligent
sensor network, wireless sensor network, Optical wireless
communication, Wireless communication & network,
microwave engineering, semiconductor devices
Leena Mahajan graduated in
Electronics & Communication
Engineering from Institute of
Electronics and Telecommunication
Engineering , New Delhi. She has received her M-Tech degree in
Electronics & Communication
Engineering from Baba Banda Singh
Bahadur Engineering College,
Fatehgarh Sahib, Punjab. She has a very rich experience of
13 years in Telecom sector. She has served many
organizations like Himachal Futuristic Communications
Limited, Chambaghat, Himachal Pradesh, India, Punjab
Communications Limited, Mohali, Punjab, India. Presently
she is working as Assistant Professor in Indo Global
College of Engineering, Abhipur, Punjab,India. She is a
corporate life member of IETE. She is guiding many thesis of M Tech students. She has published many national and
international papers on WSN and Managememt . Her active
research interests include intelligent sensor network,
wireless sensor network, Optical wireless communication,
Wireless communication network & switching devices.
0
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90
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Thro
ugh
pu
t (B
yte
s/ s
)
Message Inter-Arrival Period (S)
No Sleep
S-MAC
Mod S-MAC
www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 41
IJEEE, Vol. 1, Spl. Issue 1 (March 2014) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
Effect on Channel Capacity of Multi-User MIMO
System in Crowded Area
Vinay Thakur1, Surinder Kumar Rana
2, Abhishek Thakur
3
1,2Electronics & Communication Department, Sri Sai University, India 3Electronics & Communication Department, Indo Global College of Engineering, Punjab, India
INTRODUCTION
Multiple-Input Multiple-Output (MIMO) and Multi-User
MIMO (MU-MIMO) systems have been expected to
improve the channel capacity over a limited bandwidth of
existing networks [1], [2]. The effects on channel capacity of
Single-User MIMO (SU-MIMO) systems in urban scenarios
have been previously studied [3]. It has been clarified that
the larger number of antennas cannot contribute the improvement on the channel capacity in urban SU-MIMO
scenarios due to very high spatial correlation. MIMO is also
called by some people my moh and me moh by other people,
for the better communication we mostly use multiple
antennas at receiver and transmission end. In the latest
technology there are several forms of the antennas. In this
paper, we focus on the MU-MIMO transmission because it
can discriminate multiple users by the difference of Angle of
Arrival (AoA). We compare the Multi Access Channel
(MAC) capacity in uplink with the channel capacity in SU-
MIMO by setting the total numbers of transmitting and
receiving antennas of SU-MIMO and MU-MIMO to be the same. Multiple input and multiple output technique has call
the notice in wireless communications, because it gives a
hike in data output and range without any need of any other
external power and any change in bandwidth. It attains this
target by giving the same total transmitting power over the
antennas to achieve the spectral efficiency and to attain a
gain that improves the reliability by reducing the fading
effect. When the same numbers of antenna elements are
used, the better performance is obtained with MU-MIMO in
urban scenarios, unlike identical independent distributed
(i.i.d.) channels which are generally assumed in MIMO transmission. We also clarify an interesting relationship
between the channel capacity improvement of MU-MIMO
compared with SU-MIMO and a path visibility.
A. Antenna and User Models
The antennas and the user are simulated through fullwave
EM simulations that are performed with a three dimensional
(3D) solver, FEKO [12]. The MIMO handset has two classic
single-band PIFAs designed co-polarized to each other and
both resonate at 2.6 GHz. We consider three usage scenarios:
i) Head only (H), ii) voice scenario with the user head and
hand (HH); and iii) data scenario (D) with the user‘s two hands. The examined usage scenarios are
shown in Fig. 1(a)-(c) where the phantom head and the hand
models are used to simulate the user.
B. Antenna Efficiency
An important factor in characterizing antennas is the
radiation pattern and hence, gain and efficiency of the
antenna. The antenna patterns and efficiency definitions are
not obvious and cannot be directly derived from
conventional pattern descriptions when the antenna is placed
in the vicinity of or on a lossy medium. This is due to losses
in the medium that cause waves in the far-field to attenuate
more quickly and finally to zero. The antenna efficiency is
proportional to its gain [11] (,) (,) GDθφ =η⋅ θφ. (2) In (2) ηis the total efficiency factor and D(,) θφ is the antenna
directivity, which is obtained from the antenna normalized
power pattern that is observed in the far-field. An antenna
within a handset, for example, and/or in the vicinity of a user
would have different efficiency from an antenna in free
space due to changes in the far-field radiation pattern. Fig. 2
shows the total far-field pattern of the antenna in the
different usage scenarios described in Fig. 1. The difference
in the patterns among the different scenarios is obvious.
These differences arise from the change in the electric field
distributions at varying distances from the body or any other obstacles in the communications channel.
I. ANALYSIS MODEL
The urban propagation model employed in this paper is
represented in Fig. 1. This model is composed of 64 blocks
of 50m×50m. Each block is composed of 4 buildings. The
road width is 20m. The buildings are assumed to be
constructed of concrete and the relative
dielectric constant and conductivity are set to 5 and 0.01S/m,
respectively. The uplink scenario of (M1+M2)×NMU-
MIMO systems (from MT to BS) are considered. The
characters M1, M2, and N respectively represent the
numbers of antenna elements of the first MT, the second
MT, and the BS. It is noted that M1+M2is supposed to be
not greater than N. A linear-array BS is located at the top of
a building on one side of the model as shown in Fig. 1. Since
an accurate reflection or diffraction cannot be obtained at the
edge of analysis model, the MTs are assumed to move independently on the road in an area of 280m×280m around
the center of the model along the broken lines in Fig. 1 at the
height of 1.5m. The MT antennas are set in a symmetrical
array at half-wavelength (λ/2) spacing. The propagation
characteristics between MT and BS are then calculated by
using the ray-tracing method. The distribution of the height
of buildings is assumed following chi-squared
distribution:χ2(k), with kdegrees of freedom (DoF) which is
herein set to 5. The minimum height of these buildings is set
to 4m. The height of building:h, can be expressed as [4]
() 4, =+ hkχ
The carrier frequency is 3GHz. The numbers of reflection and diffraction are 30 and 2, respectively. The channel
response matrices are obtained from the complex received
voltage matrices which are calculated at intervals of 14m in
length along the broken lines in Fig. 1. The uplink scenario
is considered. It is assumed that the Channel State
Information (CSI) between the transmitter and receiver is not
known by the MT. Whenthe transmitter does not know the
CSI, the channel capacity of SU-MIMO can be obtained in
the units of bps/Hz as [1]
In cases of MU-MIMO, the analysis of channel is commonly
referred to the MAC [5]. The MAC capacity (CMAC) is
considered as the total channel capacity which the BS
antenna can receive from the MTs moving in the propagation area. In MAC channel, the BS can estimate all the CSI from
the MTs. In cases of 2-user MIMO systems, this CMACcan
be obtained by a substitution of the combined CSI (HMAC)
shown in Fig. 2 into (3).
II. FUNCTION OF MIMO
Three main categories of MIMO, Precoding ,Spatial
multiplexing and Diversity coding. Precoding is multistream
beam forming and considered to be all spatial processing. In single stream beam signal is transmitted with appropriate
gain, phase and maximized power at receiver. Its advantages
are to increase received signal gain with all signals get add
up from different antennas & reduce multipath fading. In
Line of sight, beam formed is directional but conventional
beam are not good analogy in cellular network ,with multiple
antenna, the transmitting beam formed cannot maximized
signal level at receiving antenna. So precoding is used and
requires channel state information (CSI) at transmitter and
receiver. In spatial multiplexing, splits high rate signal
stream into multiple lower rate signals ,each signal stream is
transmitted from different transmitting antennas at same frequency channel and required MIMO antenna
configuration. If these signals arrive at the receiver antenna
array with sufficiently different spatial signatures and the
receiver has accurate CSI, it can separate these streams into
(almost) parallel channels. It increase channel capacity at
higher signal-to-noise ratios (SNR) and maximum number of
spatial streams is limited by less number of antennas at the
transmitter or receiver. It can be used without CSI at the
transmitter, but can be combined with precoding if CSI is
available. It can also be used for simultaneous transmission
to multiple receivers, known as space-division multiple access or multi-user MIMO, in which case CSI is required at
the transmitter.
Channel Capacity Characteristics of Urban MU-MIMO
Systems
The channel capacity of urban SU-MIMO has been
evaluated [3]. It has been clarified that the channel capacity
of SU-MIMO is deteriorated compared with the i.i.d. cases
due to a very high spatial correlation in urban propagation
environment. Hence, to reduce the effect of the spatial
correlation, the MU-MIMO transmission is introduced.
Figure 3 shows the effects of model configurations on the channel capacity of (2+2)×4 MU-MIMO compared with 4×4
SU-MIMO. The results present significance, since there are
situations that CMAC> CSU, i.e. the MU-MIMO
transmission presents effectiveness. These results confirm
that the channel capacity characteristics of MU-MIMO are
greatly different from those in neither indoor nor i.i.d.
scenarios [6]. These are supported by Fig. 4. The average
spatial correlation between users of (2+2)×4 MU-MIMO
which two MTs moving independently in the propagation
area is much lower than the average spatial correlation
between each antenna element of 4×4 SU-MIMO which all MT antenna elements always stay closely. Since the spatial
correlation becomes low, its effect on the channel capacity is
also deteriorated.
www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 43
From the view of receiving antenna (BS), the AoA-diff. is
definedas the difference of angle which the signal from each
MT arrives at BS.Its effect on the channel capacity is
indicated. Figure 5 shows the CMACand CSUat each
AoAdiff. In cases of MU-MIMO, when the AoA-diff is
increased or the MTs stay farther from each other, the channel capacity is relatively increased. Even if the BS is
low mounted (50m) which MU-MIMO transmission is not
much more effective than SU-MIMO (see Fig. 3), the
channel capacity is also increased when two MTs are far
apart which the correlation becomes low. Moreover, when
the MTs stay at very near locations, or the AoA-diff is small,
(2+2)×4-MU-MIMO channels can be approximately
regarded as 4×4 SU-MIMO, and the channel capacity
becomes low due to high correlation.
Figure 6 shows the channel capacity improvement of MU-
MIMO over SU-MIMO. The curves show the ratio between
CMAC and CSU. The intersections between these curves
and the horizontal dashed line indicate the turning points
which CMAC becomes greater than CSU(CMAC/CSU> 1).
As the average building height is higher, the turning points
relatively present at a higher BS antenna height. For a clear
discussion, the path visibility defined as the probability that the direct wave can be received at the receiving antenna or
Line-o Sight (LoS) exists [3], is considered. Figure 7 shows
the effect of the path visibility on the characteristics of
CMAC/CSU. As the results, along the increment of the path
visibility, the ratio between CMAC/CSUis relatively
increased, because in urban propagation scenario which the
spatial correlation is very high, the independent movements
of users in MUMIMO can reduce the spatial correlation.
That is the reason why the MU-MIMO transmission can
present the effectiveness while the SU-MIMO cannot.
Furthermore, considering the fitting curve in Fig. 7, it is
clarified that CMAC becomes greater than CSU, when the path visibility is about 13 percent. That is to say, to obtain an
effectiveness of urban wireless communication, not only the
MU-MIMO transmission is supposed to be employed, but
also the BS antenna should be mounted at the height so as
the path visibility is greater than 13 percent. This result will
be useful when considering the installation of the BS in
urban SU/MU-MIMO systems.
III. CONCLUSION
Throughout this paper, the channel capacity characteristics
of urban SU-MIMO and MUMIMO considering the uplink
scenario were studied. The MU-MIMO transmission was
introduced to reduce the spatial correlation. The MAC
capacity in 2-user 2×4 ((2+2)×4) MU-MIMO was compared
with the channel capacity in 4×4 SU-MIMO. It was clarified
that the spatial correlation between users of MU-MIMO
which two MTs moving independently in the propagation
area was much lower than that of SU-MIMO which all MT
antenna elements stayed closely all the times. Its effect on the channel capacity was consequently deteriorated. By the
definition of AoA-diff, it was shown that when the MTs
stayed farther from each other which the spatial correlation
became low, the channel capacity was increased. Moreover,
when the AoA-diff was small or the MTs stayed at very near
locations, (2+2)×4 MU-MIMO channels could be
approximately
regarded as 4×4 SU-MIMO. Finally, it was shown that the
channel capacity improvement of MU-MIMO over SU-
MIMO was relatively increased along with the increment of
the path visibility.
REFERENCES.
[1] S. Hemrungrote, T. Hori, M. Fujimoto, and K. Nishimori, ―Effects of path visibility on urban MIMO systems,‖ Proc. ISAP2009, Bangkok, Thailand, pp.157-160, Oct. 2009..
[2] Y. Ito, "The distribution of height and width of buildings", in Radiowave Propagation Handbook, Eds. Japan: Realize Inc., 1999, pp. 342–349, Realize Inc., Japan, 1999.
[3] A. Goldsmith, S.A. Jafar, N. Jindal, and S. Vishwanath,
―Capacity limits of MIMO channels,‖ IEEE J. Commun., vol.21, no.5, pp.684 -702, Jun. 2003.
[4] P. Kildal, K. Rosengren, ―Correlation and capacity of MIMO systems and mutual coupling, and diversity gain of their antennas: simulations and measurements in a reverberation chamber,‖ IEEE Communications Magazine, Dec. 2004.