Capacity Enhancement of W-CDMA Wireless Network Using

International Journal of Advances in Science and Technology, Vol. 6, No.2, 2013

Capacity Enhancement of W-CDMA Wireless Network Using Adaptive Beamforming Technique

UFOAROH STEPHEN. U1, AKPADO K2, NWALOZIE G.C3, ONYISHI D.U4

1, 2, 3 Department of Electronic & Computer Engineering

Nnamdi Azikiwe University, Awka Anambra State Nigeria 4 Department of Electrical & Electronic Engineering,

Federal University of Petroleum Resources Effurun, Delta State Nigeria

[email protected], [email protected], [email protected], [email protected]

Abstract

Over the last few years, the number of subscribers to wireless services has increased enormously at an explosive rate. This ever growing demand for wireless communications services is constantly increasing the need for better coverage, improved capacity and higher quality of service. Adaptive Antennas can be used to increase the capacity, the link quality and the coverage of the existing and future mobile communication networks. Using beamforming algorithms the weight of antenna arrays can be adjusted to form certain amount of adaptive beam to track corresponding users automatically and at the same time to minimize interference arising from other users by introducing nulls in their directions. Using the approach of adaptive beamforming antenna system, an intelligent sector synthesis of varying azimuth and beamwidth can be established. By doing so, traffic load balancing can be achieved and headroom for the growth of more traffic can be created. The capacity, data rates, null steering and coverage of the cellular system are improved by using various beamforming algorithms. This paper presents the significance of the beamforming technique employed for the next generation broadband wireless mobile systems. The computer simulation carried out in MATLAB platform shows the signal processing technique optimally combines the components in such a way that it maximizes array gain in the desired direction simultaneously minimize it in the direction of interference, and thus improved capacity of the network

Keywords: Adaptive Antenna, adaptive beamforming, signal nulling, antenna arrays

Introduction

Global demand for voice, data and video related services continues to grow faster than the required infrastructure can be deployed. Despite huge amount of money that has been spent in attempts to meet the need of the world market, the vast majority of users still do not have access to quality communication facilities. The greatest challenge faced by governments and service providers is the “last-mile” connection, which is the final link between the individual home or business users and worldwide network. Omni-directional antennas have traditionally been used at the base stations to enhance the coverage area of the base station. But it also leads to a gross wastage of transmitted power, which is the main cause of co-channel interference at neighbouring base station. The need to improve system capacity and reduce co-channel interference effects led to the development of two antenna system techniques, namely Sectorized system and Diversity system. The sectorized systems increase the possible reuse of frequency channels in a cellular network, but they do not overcome the major disadvantage of Omni-directional antenna such as co-channel interference. The diversity system mitigates the effects of signal fading, but it does not increase gain, because of its use of one element at a time.

The need to transmit to numerous users more efficiently without compounding the interference problem led to the development of Adaptive antenna. Adaptive antenna is recognized as one of the promising technologies for higher user capacity in mobile wireless networks by effectively reducing multipath and co-channel interference [1], [2]. Adaptive antenna is a technique in which an array of antennas is exploited to achieve maximum reception in a specified direction by estimating the signal arrival from a desired direction (in the presence of noise) while signals of the same frequency from other directions are rejected. This is achieved by varying the weights of each of the sensors (antennas) used in the array. It basically uses the idea that, though the signals emanating from different transmitters occupy the same frequency channel, they still arrive from different directions. This spatial separation is

February Issue Page 1 of 85 ISSN 2229 5216


exploited to separate the desired signal from the interfering signals. In adaptive beamforming the optimum weights are iteratively computed using complex algorithms based upon different criteria. Adaptive algorithms form the heart of the array processing network. Several algorithms have been developed based on different criteria to compute the complex weights [3]. They have their own advantages and disadvantages as far as the convergence speed, complexity and other aspects are concerned. Gastpar M, and Vetterli in [4] Studied the capacity of wireless networks when network coding can be used to improve the capacity. Capacity can also be generalized to the notion of capacity region. For a given statistical description of the network, a set of constraints (such as power per node, link capacity, etc.), and a list of desired communication pairs, the capacity region is the closure of all rate tuples that can be achieved simultaneously. Here a rate tuple specifies the rate for each of the desired communications. . On the other aspect Tavli B in [5] recently studied how to satisfy a certain traffic demand vector from all wireless nodes by a joint routing, link scheduling, and channel assignment under certain wireless interference models. The author essentially shows that the broadcast capacity of a given network is Ɵ(W) for single source broadcast and the achievable broadcast capacity per node is only Ɵ (W/n) if each of the n nodes will serve as source node. The work assume a simple channel model: when no interference exists, a node can transmit to its neighbors at data rate at most W bits/second. It equally also assume that all wireless nodes have a uniform transmission range r and uniform interference range, a node v cannot receive data from a transmitting node u with ││u-v ││<= r if there is another transmitting node w with ││w-v││<= R.

In [6], the authors studied the broadcast capacity with dynamic power adjustment for physical interference model. The paper mainly considered the dense model. The paper considered both physical interference model and Generalized Physical Interference model (called Gaussian channel model here). In physical model used, a node can receive data correctly only if the SINR is at least a threshold. The Physical Model models interference more accurately, but still assigns a constant transmission rate once successful transmission is guaranteed. The Generalized Physical Model allows for a transmission rate that depends on the level of interference and the distance between sender and receiver and thus allows for a more precise assessment of the broadcast capacity. In this model the transmission rate 푊 between a sender I and a receiver j is determined using Shannon’s formula for a wireless channel with additive Gaussian white noise.

The authors give an exclusive summary of concepts, measurements, and parameters and validate results from research conducted within the scope of their work.

The main goal of this research is focused on how to use the adaptive beamforming techniques to enhance the channel capacity (data throughput) of a CDMA cellular network, the use of simulation enables a flexible platform to implement and test mobile communication system as a real world system.

Overview of Adaptive Beam Forming in W-CDMA

In wideband systems, lower frequency signal components have less phase shift for a given propagation distance, whereas higher frequency signal components have a greater phase shift as they travel the same lengths. It is therefore necessary to introduce different phase responses for the different frequency components of the wideband signal, in order to provide flat frequency response within the signal bandwidth. The tapped-delay line allows each element to have a phase response that varies with frequency, resulting in a flat frequency response within the signal bandwidth. Such wideband arrays are therefore also called space-time receivers. An important special case of the wideband array is the spatial processing CDMA Rake receiver, which offers some of the benefits of the wideband array at a complexity level which is closer to the narrowband array. In code division multiple access (CDMA) systems the users operate in the same frequency and time channel whereas in TDMA the users are separated in time. In direct sequence CDMA (DS-CDMA) each user has a unique spreading code. This code operates at a chip rate P times greater than the information data rate. P typically lies in the range of 100 to 1000 [7]. The DS-CDMA link therefore needs a large bandwidth channel that can be shared by multiple users. The user codes can be designed to be orthogonal or quasi-orthogonal. The spreading code can be viewed as a complex symbol waveform with a large



time-bandwidth product (approximately P), whereas in TDMA the time-bandwidth product of the symbol waveform is small (approximately 1).

If orthogonal codes are used and there is no multipath, code orthogonality can be maintained, thus users do not interfere with each other and signal detection is noise limited. Walsh codes are popular orthogonal codes, however, the number of Walsh codes is only equal to the code length, P. On the other hand, if we use quasi-orthogonal codes (whose number can be much larger) or orthogonal codes with multipath, the users interfere with each other and the detection becomes interference limited. This multiple-access interference (MAI) that arises from other users is reduced by the processing gain P during the detection process.

In essence, the presence of multiple co-channel users and the use of complex spreading code instead of symbol waveforms distinguish CDMA from TDMA signal processing. With the introduction of multiuser, TDMA signal processing (enabled by multiple antennas) there is increased convergence of TDMA and CDMA processing.

Adaptive Beamforming with CDMA

The implementation of adaptive beamforming in a (CDMA) system is different from that in a Time Division Multiple Access (TDMA) system. In a (TDMA) system beamforming is carried out on a frame-by-frame basis for the entire frequency band. In a (CDMA) system, beamforming is carried out continuously for the entire (CDMA) frequency band. Moreover, the choice of beamforming configuration depends on the type of a (CDMA) system, namely, a synchronous or asynchronous (CDMA).

In a synchronous (CDMA) system, the information bit duration of each user signal in the system is time aligned at the base station. The beamforming configurations for a base station system in both the reverse link and the forward link cases are shown in figure 1 and figure. 2, respectively. The reverse link (forward link) system consists of

1. An M-element antenna array

2. M receiver (transmitter) modules

3. M Analog-to-Digital Converter (ADCs), Digital-to-Analog Converter (DACs)

4. N digital demodulator (modulator) banks, each of which consists of M correlators and samplers

5. N Receive Digital Beam Former (RDBF), Transmit Digital Beam Former (TDBF) networks.

Fig 1 Reverse link DBF configuration for a CDMA system.

In the reverse link (uplink) case, a beamforming network is required for user signals that use an identical code. This requires that beamforming is carried out following the demodulation process (or code filtering). If each beamforming network produces L beam outputs, the number of simultaneous users can be up to N × L. In the



forward link (downlink) case, the N × L message signals to be transmitted are arranged into N groups. Beamforming is applied to the L messages in each group. The beamforming outputs are spread with a particular code. The code-division multiplexed (CDM) signals are combined and converted to analog signals for transmission. In these configurations, the beamforming networks operate at the bit rate. However, it should be noted that the demodulation and modulation processes must be both linear and coherent to preserve the phase information that is required for beamforming.

In order for DBF to be implemented in an asynchronous CDMA system, the above configuration must be modified. As shown in Figure below, the receive beamforming networks must be placed before the demodulators and the transmit beamforming networks placed after the modulators. That is, the alternative reverse link (forward link) system consists of

1. An M-element antenna array

2. M receiver (transmitter) modules

3. M ADCs (DACs)

4. M RDBF (TDBF)

5. N × L digital demodulators (modulators), each of which consists of a correlator and a sampler.

Figure 2 Forward link DBF configuration for a CDMA system

In Figure.2, the time index tl for cm denotes the fact that the information bit duration of the user signals do not have to be synchronized. In these configurations, the demodulation and modulation processes can be linear or nonlinear, and coherent or non-coherent. Furthermore, cm (tl) can be used as the reference to carry out adaptive beamforming. However, it is required that the beamforming networks operate at least at the chip rate.

Adaptive beamforming algorithms

The adaptive beam forming algorithm optimizes the array output beam pattern such that maximum radiated power is produced in the directions of desired mobile users and deep nulls are generated in the directions of undesired signals representing co-channel interference from mobile users in adjacent cells. [8] explained in detail the basic concept of adaptive beam forming starting from the use of two elements array to suppress interference. The fundamental method in adaptive beamforming is to choose the weights of array elements in order to optimize the beamformer



response to fulfill certain criterion. The criterion includes Minimum Mean-Square Error, Maximum Signal-to-Interference Ratio, Minimum Variance Distortionless Response Null steering beamforming algorithm. The choice of criteria is not critically important since they are closely related to each other. The most important part is the adaptive algorithms, which will determine the speed of convergence and hardware complexity required. The algorithms include Least Mean Squares algorithm (LMS), Constant Modulus Algorithm (CMA), Recursive Least Squares Algorithms (RLS), linearly constrained minimum variance (LCMV) beamformer. When the number of array elements becomes very large the system becomes complex to implement its adaptive function. Another important component in adaptive beamforming is the reference signals, also known as the prior knowledge of the signal of interest, which is needed to decrease the complexity, improve accuracy and achieve faster convergence. Prior to adaptive beamforming, the directions of users and interferes must be obtained using a direction-of-arrival (DOA) estimation algorithm. In this work it is assumed that the reference signal is known.

Research Metholody

The capacity enhancement of a W-CMDA network using an adaptive beamforming algorithm is the main focus of this research. In this work the Least Mean Square (LMS) algorithm have been proposed to enhance the capacity of the system using CAPCOM limited as the case study, a dense urban area Lagos in Nigeria was used. The path loss Model was used to predict the capacity of 3-sector antenna system that is currently in use by the company. The sectors where increased to demonstrate the effect of the number of sectors to the capacity of the system. Then the capacity of the system where compared to the capacity of a beamforming system. The simulation was done in MATLAB platform.

System Model of the sectored wireless Network

Channel allocation scheme being a technique by which the scarce resource (spectrum) is accessed by mobile users is limited by many factors, prominent among them being interference. The limited scarce resource can be maximized in its usage when the entire network users can be serviced at any time with minimal number of channels (resource) used [9]. According to them the beam-width of a sectored system is related in the following formula

= BW (1)

Where NS is the number of sectors in a cell unit, BW is the beam-width and 360 is the angle at a point.

If the number of co-channel interfering cells is denoted by Ni, and Pi be the interfering power in the ith interfering cell then the signal to interfering ratio at the desired receiver is given by

= ∑

(2)

Where S is the signal power

The number of users in a single CDMA cell can be predicted using equation by the equation [10]

푁 = 1+ − (3)

Where, Ns=total number of users, W=chip rate, R= base band information bit rate, Eb/No=Energy per bit to noise power spectral density ratio, η= background thermal noise, S=signal power=S1-P (d)-shadow fading, S1=UE power, P (d) =Propagation loss.



The capacity can be predicted with equation 4, considering the system we are using with chip rate of 3.84Mcps and bandwidth of 1.58Mbps it is also necessary to consider the affects of multiple cells or intra-cell interference (β), cell sectoring(D), soft handover factor(H), Array antenna gain (Ag).Thus the capacity for CDMA yields[10],

푁 = 1+ −

( ) * D * H * 퐴 (4)

The Base Station sensitivity is the power level for minimum signal necessary at the input of the Base Station receiver to meet requirements in terms of Eb/No, processing gain (Gp) and Base Station interference and noise power given in [10]

Base Station sensitivity

− 퐺 +푁 (5)

Eb/No=Energy per bit to noise power spectral density ratio,

Where Gp=Processing gain

10log = 10log . (6)

Now the maximum allowable path loss for Base Station, is given as

LP = (EIRP)-(base station sensitivity) +( Gp)- (fast fading margin) (7)

from radio propagation model, Path loss for dense urban area is 퐿 = 46.3 + 33.9 log(푓) − 13.82 log(ℎ ) − 3.2[푙표푔(11.75ℎ )] + 4.76 + (44.9− 6.55푙표푔ℎ ) + 푙표푔푑 + 3

(8)

From equation (7) and (8) a relationship can be expressed for coverage and data rates in dense urban case,

46.3 + 33.9 log(푓) − 13.82 log(ℎ )− 3.2[푙표푔(11.75ℎ )] + 4.76 + (44.9− 6.55푙표푔ℎ ) + 푙표푔푑 + 3 =퐸퐼푅푃 − 푏푎푠푒푠푡푎푡푖표푛푠푒푛푖푡푖푣푖푡푦+ 10 log . − 푓푎푠푡푓푎푑푖푛푔 (9)

where d is the coverage radius and R is the data rates.

After calculating the cell range d, the coverage area can be calculated. The coverage area for one cell in hexagonal configuration can be estimated with

Coverage area, S =K.d2 (10)

where S is the coverage area, d is the maximum cell range, and K is a constant. In Table 1, some of the K values are listed

Table 1, some of the K values are listed



Least Mean Square (LMS) Algorithm

The least mean squares algorithm is a gradient based approach. Gradient based algorithms assume an established quadratic performance surface. When the performance surface is a quadratic function of the array weights, the performance surface J(w) is in the shape of an elliptic parabola having one minimum. One of the best ways to establish the minimum is through the use of a gradient method. We can establish the performance surface (cost function) by again finding the MSE. The output of the array is given as

푦 = 푾 푿(푡) (11)

The error signal is given as

휖(푡) = 푑(푡)−푾 푿(풕) (12)

Where 푑(푡) is the reference signal, and 푾 푿(푡) is the array output. The Mean Square Error (MSE) is given by

휖 (푡) = [푑(푡)−푾 푿(푡)] (13)

Taking expectation on both sides of the above equation we obtain

퐸[|휖 (푡)|] = 퐸{[푑(푡)−푾 푿(푡)] } (14)

퐸[휖 (푡)] = 퐸{[푑 (푡)]}− 2푾 풓+푾 푹풙풙푾 (15)

풓 = 퐸{[푑(푡)푿(푡)]} (16)

푹 = 퐸[푿푿 ] = 푹 + 푹 (17)

Where 풓 is the cross correlation matrix between the desired signal and the received signal. 푹 is the auto correlation matrix of the received signal, and 푹 is the source (desired signal) correlation matrix, and 푹 is the undesired (noise) signal correlation matrix. The minimum MSE can be obtained by taking the gradient of the MSE with respect to the weight vectors and equating it to zero.

∇푾(퐸[휖 (푡)]) = 2푹 푾− 2풓 = 0 (18)

Therefore the optimum solution for the weight vector 푾 is given by

푾 = 푹 풓 (19)

The Least Mean Square (LMS) algorithm uses a gradient based method of steepest decent [11]-[13]. This algorithm uses the estimate of the gradient vector from the available data. This algorithm computes the complex weights vector recursively using the equation, given as;

푊(푛 + 1) = 푊(푛) + 휇푋(푛)푒 ∗ (n) (20)

푒(푛) = 푑 ∗ (푛) − 푦(푛) (21)

Where 휇 is the step size parameter and controls the convergence characteristics of the LMS algorithm. The LMS algorithm is initiated with an arbitrary value of 푊(0) for the weight vector at 푛 = 0. The successive corrections of the weight vector eventually leads to minimum value of the Mean Square Error (MSE).

In order to ensure the stability and convergence of the algorithm, the adaptive step size should be chosen within the range specified as:



0 < 휇 < (22)

Where max is the maximum eigenvalue of the input covariance matrix푅 . As noted above, the LMS algorithm requires knowledge of the desired signal푑(푡),this can be done in a digital system by periodically transmitting a training sequence that is known to the receiver, or by using the spreading code in the case of a direct-sequence CDMA system. The least mean square algorithm (LMS) is important because of its simplicity and ease of computation and because it does not require off-line gradient estimations or repetition of data. If adaptive system is an adaptive linear combiner and if the input vector and desired response are available at each iteration, the LMS algorithm is generally the best choice for many different applications of adaptive signal processing. Errors between reference signal and array output have been calculated using standard methods.

Simulation and Performance Evaluation

For simulation purpose the uniform linear array with N number of elements is considered. The inter-element spacing is considered to be half wavelength. It is considered that the desired user is arriving at an angle of 30 degrees and an interferer at an angle of -60 degrees.

Figure 3: Polar plot of beam pattern of the LMS algorithm when the desired user with AOA 30 deg and interferer with AOA -60 deg, the spacing between the elements is . ퟓ흀

Fig.3 shows the polar plot of the LMS algorithm and Fig.4 shows the array factor plot and how the LMS algorithm places deep null in the direction of the interfering signal and maximum in the direction of the desired signal.

Fig 4: Array factor against the angle of arrival using the LMS algorithm

-90 -60 -30 0 30 60 900

0.2

0.4

0.6

0.8

1

AOA (deg)

|AF n|



Fig 5: Weighted LMS array plot.

The analysis is carried for capacity and coverage with sectoring cell for dense urban using MATLAB.The performances are also described in Figure 8, Figure 9. The simulation parameters are given below;

energy per bit to noise spectral density ratio Eb/No range 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 16, 20

soft handover gain (H) factor range 0.1, 1, 1.5, 2, 3

inter-cell interference (β) range 1, 1.2, 1.55, 2

channel activity for data (α) = 1

channel activity for voice (α) range 0.1, .3, .38, 0.7, 0.9

thermal noise (η) in (20 Kelvin) dbm/Hz= -173.93

user signal power (S1) in dbm= 21

shadow fading (sh_fd) in db = 8

cell range in Km (Rcell) = 2

chip rate (W) = 3840000

base band information rate in Kbps (R) = 12.2, 64, 144, 384, 2000

base antenna height in meter (hb) = 20

user antenna height in meter (hUE) = 2

sector (D) = 1 2 3 4 6

frequency range in MHz (fc) = 1900

data rate in Kbps (R) = 12.2, 64, 144, 384, 2000

array antenna gain (Ag) in db= 1, 2, 3.5, 5

0 10 20 30 40 50 60 70 80 90 1000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Iteration no.

Mea

n sq

uare erro

r



Fig 6: Number of simultaneous 384 Kbps users vs.inter-cell interference in sectors cell

The interference from other cell is known as inter-cell interference (β). For multi-cell configuration, the number of outer cells can reduce cell capacity in a network. Figure 6 shows, for increasing demand of users the value of β in a network needs to be small. Figure 6 also represents dynamic inter-cell interference with changing of sectors, where number of simultaneous 384Kbps data users increases or decreases. From Figure 6 it has been observed that for increasing value of β, it is needed to increase sectors.

Fig 7: Number of simultaneous voice users vs. voice activity factor in sectors cell

Figure 7 shows that the number of voice users depends on the value of voice activity factors (α).This is true only for 12.2 Kbps voice users, not for data users, as for data services it will always be 1. Figure 7 also shows that, for increasing amount of voice users the value of α in a network needs to be as small as possible. Varying α and changing the number of sectors and its effect on the number of simultaneous voice users is observed in figure 7.



Figure 8 below shows the cell capacity with respect to the antenna types, call admission and call drop thresholds. The call holding time is set as infinity so that there are no call departures. 0.5 dB hysteresis means that the call drop threshold is set 0.5 dB above the call admission threshold. Otherwise, the call drop threshold is fixed at .13 dB. Figure 8 shows that the cell capacity increases with the increase of the call admission threshold. Both the call admission and the call drop thresholds determine the capacity of the cell. However, the major factor determining the capacity is the call admission threshold since it is below the call drop threshold. Larger hysteresis will increase the capacity, but just a little. On the other hand, smart antenna obviously increases the system capacity.

Fig 8 Cell Capacity with respect to Antenna, Call Admission and Drop Threshold

Conclusion

This research presents adaptive beamforming technique which has gained importance in wireless mobile communication system due to its ability to reduced co-channel and adjacent channel interferences. This research presented the Least Mean Square (LMS) Algorithm. Depending on the application requirements one of the beamforming algorithms is selected. Thus beamforming has proved its benefits for next generation mobile system and plays a vital role in next generation mobile networks. Beamforming is a good candidate which fulfils user demands with efficient spectra utilization. In this work, the performance analyses in coverage and capacity of CDMA cellular network using sectorization have been simulated and evaluated for dynamic parameters. The number of simultaneous users increases or decreases for increasing or decreasing sectors with dynamic parameters. The choice of which algorithm to use depends on the environment and the system complexity and some beamforming algorithm requires the knowledge of the signal strength and also the reference signal to perform. And this is hard to determine. The Least Mean Square (LMS) Algorithm overcomes the limitation through the application of weight to the weight vector. And in general the application of beamforming technique enhances the quality of service (QoS) and the capacity of the network channel. By increasing the distance covered by particular base station. The capacity of a cell increases with increase in the number of sectors in a cell and the capacity of network increases when beamforming is employed

REFERENCES

[1] R. H. ROY, “An overview of Smart Antenna Technology: The next Wave in Wireless Communications,” in Proc.1998 IEEE Aerospace Conference, vol. 3, May 1998, pp. 339-345.

[2] Joseph C. Liberti, Theodore S. Rappaport. “Smart Antennas for Wireless Communications: IS-95 and Third Generation CDMA Applications”, Prentice Hall PTR, 12 April, 1999.



[3] Lal. C. Godara. “Application of Antenna Arrays to Mobile Communications part II. Beamforming and Directiona-of-Arrival Consideration”. Proceedings of IEEE, August 1997, 85(8):1195-1245.

[4] Gastpar, M., and Vetterli, m. “On the capacity of wireless networks: the relay case”. in IEEE INFOCOM 2002.

[5] Tavli, B. “Broadcast capacity of wireless networks”. IEEE Communication Letters 10, 2 February 2006.

[6] Keshavarz-Haddad, A. and Riedi R. “On the Broadcast Capacity of Multihop Wireless Networks: Interplay of Power, Density and Interference”, 4th IEEE SECON, June 2007. X

[7] Paul Petrus. “Novel Adaptive Array Algorithms and Their Impact on Cellular System Capacity”. PhD thesis, Faculty of the Virginia Polytechnic Institute and State University, March 1997.

[8] J. Litva, T. K. Lo, “Digital Beamforming in Wireless Communications,” Artech House, Norwood, 1996

[9] Ohaneme Cletus Ogbonna; at’el “Improving the Channel Capacity of Wireless Network Using Cell Sectoring”, International Journal of Computer Networks and Wireless Communications (IJCNWC), Vol. 2, No. 1, 2012,pages 100-101

[10] B. Sklar, “Rayleigh Fading Channels in Mobile Digital Communication Systems; Part I: Characterization; Part II: Mitigation”, IEEE Communication Magazine, Vol. 35, No. 7, pp. 90–109, 1997

[11] Frank B. Gross, “Smart Antennas for Wireless Communication with MATLAB” MCGRAW-HILL 2005.

[12] Sidi Bahiri, Fethi Bendimerad. “Performance of Adaptive Beamforming Algorithm for LMS-MCCDMA MIMO Smart Antennas”. The international Arab Journal of Information Technology, vol. 6, No. 3, July 2009.

[13] Raed M. Shubair, Mahmoud A. AL-Qutayri, Jassim M. Samhan. “A Setup for the Evaluation of MUSIC and LMS Algorithms for a Smart Antenna System”. Journal of Communications, vol.2, No.4, June 2007.

[14] P.Strobach, “Total Least Squares Phased Averaging and 3-D ESPRIT for Joint Azimuth-Elevation-Carrier Estimation”. IEEE Trans. Signal Processing, vol.49, pp. 54-62, Jan. 2001.

[15] Susmita Das. “Smart Antenna Design for Wireless Communication using Adaptive Beamforming approach”. Department of Electrical Engineering, National Institute of Technology Rourkela-769008, Orissa,India. May, 2009.

[16] Litva, John & Titus Kwok-Yeung Lo. “Digital Beamforming in Wireless Communications”. Artech House Publishers. Boston-London. 1996

[17] Gupta, P., and Kumar, P. “Capacity of wireless networks. Tech. rep”, University of illinois, Urbana-

Champaign, 1999.


Date post:	13-May-2017
Category:	Documents
Upload:	appeal-matthew
View:	233 times
Download:	3 times

Capacity Enhancement of W-CDMA Wireless Network Using

Documents