Seminar Report
On
Cognitive Radio Networks
Submitted in partial fulfillment of the requirements
For the degree of
Master of Technology
(Communication System)
By
Ajay Kumar Gautam
(Roll No. P08EC901)
under the guidance of
Mr. Golak Santra
2008-2009
Electronics Engineering Department
Sardar Vallabhbhai National Institute of Technology Surat-395007, Gujarat, India.
Sardar Vallabhbhai National Institute of Technology Surat- 395007, Gujarat, India.
Electronics Engineering Department
CERTIFICATE
This is to certify that Mr. Ajay Kumar Gautam Roll no. P08EC901 of M.Tech.-I
(Communication System) has satisfactory presented a Seminar on “Cognitive
Radio Networks” during the year 2008-2009.
Signature of Guide: Signature of HOD:
Mr. Golak Santra Prof. B.R Taunk
Lecturer, ECED Head, ECED
Signature of Internal Examiners:
1)
2)
SEAL OF DEPARTMENT
Contents
List of figures i
Abstract ii
1. Introduction 1
2. Cognitive Radio 2-8
2.1 what are Cognitive Radios
2.2 Cognitive Radio requirements
2.3 What is SDR
2.4 Relationship between Cognitive Radio and Software Defind Radio
2.5 Physical architecture of the cognitive radio
2
3
4
4
5
3. Cognitive Radio Networks 9-19
3.1 Introduction
3.2 Architecture for Cognitive Radio Networks
3.3 Spectrum Management Framework for Cognitive Radio Networks
3.4 Spectrum Sensing for Cognitive Radio Networks
3.5 Spectrum Decision Framework for Cognitive Radio Networks
3.6 Inter-Cell Spectrum Sharing in Cognitive Radio Networks
3.7 Spectrum Mobility for Cognitive Radio Networks
3.8 Spectrum Aware Routing Protocol for Cognitive Radio Networks
9
10
12
13
14
16
17
18
4. Transport Protocol for Cognitive Radio Networks 20-21
5. Cognitive Mesh Networks 22-23
6. Cognitive Sensor Networks: Interferer Classification and Transmission
Adaptation
24-25
7. Cognitive Radio Network applications
Summary
References
Acronyms
26
27
28
29
List Of Figures
Figure 1 : Relationship between SDR and CR
5
Figure 2. Physical architecture of the cognitive radio
a) Cognitive radio transceiver and
b) Wideband RF/analog front-end architecture.
6
Figure 3. Cognitive radio network architecture.
10
Figure 4. Spectrum Management Framework.
12
Figure 5. Periodic spectrum sensing structure
13
Figure 6. Spectrum decision framework for cognitive radio networks 15
Figure 7. Inter-Cell Spectrum Sharing Framework
16
Figure 8. Joint route and spectrum discovery
19
Figure 9. The state diagram for the TP-CRAHN protocol
20
Figure 10. Cognitive mesh network architecture
22
Figure 11. Identifying the interferer type and location
24
i
Abstract
Cognitive radio networks are intelligent networks that can automatically sense the
environment and adapt the communication parameters accordingly. These types of networks
have applications in dynamic spectrum access, co-existence of different wireless networks,
interference management, etc. They are touted to drive the next generation of devices, protocols
and applications. Clearly, the cognitive radio network paradigm poses many new technical
challenges in protocol design, power efficiency, spectrum management, spectrum detection,
environment awareness, new distributed algorithm design, distributed spectrum measurements,
QoS guarantees, and security. Overcoming these issues becomes even more challenging due to
non-uniform spectrum and other radio resource allocation policies, economic considerations, the
inherent transmission impairments of wireless links, and user mobility.
Cognitive radio is an emerging technology that enables the flexible development and
deployment of highly adaptive radios that are built upon software defined radio technology.
Cognitive radio has been considered as a key technology for future wireless
communications and mobile computing. We note the cognitive radios can form cognitive radio
networks (CRN) by extending the radio link features to network layer functions and above. We
categorize CRN architecture into several structures and classify the unidirectional links in such
structures
ii
1
Chapter 1. Introduction
The wireless communication systems are making the transition from wireless telephony to
interactive internet data and multi-media type of applications, for desired higher data rate
transmission. As more and more devices go wireless, it is not hard to imagine that future
technologies will face spectral crowding, and coexistence of wireless devices will be a major
issue. Considering the limited bandwidth availability, accommodating the demand for higher
capacity and data rates is a challenging task, requiring innovative technologies that can offer new
ways of exploiting the available radio spectrum. Cognitive radio is the exciting technologies that
offer new approaches to the spectrum usage. Cognitive radio is a novel concept for future
wireless communications, and it has been gaining significant interest among the academia,
industry, and regulatory bodies. Cognitive Radio provides a tempting solution to spectral
crowding problem by introducing the opportunistic usage of frequency bands that are not heavily
occupied by their licensed users. Cognitive radio concept proposes to furnish the radio systems
with the abilities to measure and be aware of parameters related to the radio channel
characteristics, availability of spectrum and power, interference and noise temperature, available
networks, nodes, and infrastructures, as well as local policies and other operating restrictions.
The primary advantage targeted with these features is to enable the cognitive systems to
utilize the available spectrum in the most efficient way. An interconnected set of cognitive radio
devices that share information is defined as a Cognitive Radio Network (CRN). Cognitive Radio
Networks aim at performing the cognitive operations such as sensing the spectrum, managing
available resources, and making user-independent, intelligent decisions based on cooperation of
multiple cognitive nodes. In order to be able to achieve the goals of the cognitive radio concept,
cognitive radio networks need a suitable wireless technology that will facilitate collaboration of
the nodes.
2
Chapter 2. Cognitive Radio
2.1 What are Cognititve Radios
Cognitive Radio (CR) is a paradigm for wireless communication in which either a network
or a wireless node changes its transmission or reception parameters to communicate efficiently
avoiding interference with licensed or unlicensed users. This alteration of parameters is based on
the active monitoring of several factors in the external and internal radio environment, such as
radio frequency spectrum, user behavior, and network state. The idea of CR was first proposed
by Joseph Mitola III and Gerald Q. Maguire. It was thought of as an ideal goal towards which a
Software-Defined Radio (SDR) platform should evolve: a fully reconfigurable wireless black-box
that automatically changes its communication variables in response to network and user
demands.
Software Defined Radio (SDR) has now reached the level where each radio can perform
beneficial tasks that help the user, help the network, and helps to minimize spectral congestion.
A simple example is the adaptive digital European cordless telephone (DECT) wireless phone,
which finds and uses a frequency within its allowed plan with the least noise and interference on
that channel and time slot. Three major applications that raise an SDR’s capabilities and make it
a cognitive radio:
1. Spectrum management and optimizations.
2. Interface with a wide variety of networks and optimization of network resources.
3. Interface with a human and providing electromagnetic resources to aid the human in his or her
activities.
Cognitive radio can be defind as:
A “Cognitive Radio” is a radio that can change its transmitter parameters based on interaction
with the environment in which it operates.
A “Cognitive Radio” is an SDR that is aware of its environment, internal state, and location,
and autonomously adjusts its operations to achieve designated objectives.”
The cognitive radio is able to provide a wide variety of intelligent behaviors. It can
monitor the spectrum and choose frequencies that minimize interference to existing
communication activity. When doing so, it will follow a set of rules that define what frequencies
may be considered, what waveforms may be used, what power levels may be used for
transmission, and so forth. It may also be given rules about the access protocols by which
spectrum access is negotiated with spectrum license holders, if any, and the etiquettes by which
it must check with other users of the spectrum to ensure that no user hidden from the node
wishing to transmit is already communicating.
3
In addition to the spectrum optimization level, the cognitive radio may have the ability to
optimize a waveform to one or many criteria. For example, the radio may be able to optimize for
data rate, for packet success rate, for service cost, for battery power minimization, or for some
mixture of several criteria. The user does not see these levels of sophisticated channel analysis
and optimization except as the recipient of excellent service.
The cognitive radio may also exhibit behaviors that are more directly apparent to the user:
(a) awareness of geographic location,
(b) awareness of local networks and their available services,
(c) awareness of the user and the user’s biometric authentication to validate financial
transactions, and
(d) awareness of the user and his or her prioritized objectives.
Many of these services will be immediately valuable to the user without the need for complex
menu screens, activation sequences, or preference setup processes.
2.2 Cognitive Radio Requirements
One of the main goals targeted with cognitive radio is to utilize the existing radio
resources in the most efficient way. To ensure the optimum utilization, cognitive radio requires a
number of conditions to be satisfied. The primary cognitive radio requirements are
(a) negligible interference to licensed systems,
(b) capability to adapt itself to various link qualities,
(c) ability to sense and measure critical parameters about the environment, channel, etc.
(d) ability to exploit variety of spectral opportunity,
(e) flexible pulse shape and bandwidth,
(f) adjustable data rate, adaptive transmit power, information security, and limited cost.
The aim of Cognitive Radio is usage of frequency bands that are owned by their licensed
users. Therefore, one of the most significant requirements of cognitive radio is that the
interference caused by cognitive devices to licensed users remains at a negligible level.
One of the main features of the cognitive radio concept is that the targeted frequency
spectrum is scanned periodically in order to check its availability for opportunistic usage.
According to the results of this spectrum scan, the bands that will be utilized for cognitive
communication are determined. Since at different times and locations the available bands can
vary, cognitive radio is expected to have a high flexibility in determining the spectrum it
occupies.
4
Since the cognitive radio concept includes free utilization of unused frequency bands,
there will be a number of users willing to make use of the same spectrum opportunities at the
same time. Therefore, cognitive radio networks should be able to provide access to multiple
users simultaneously. During the operation of a cognitive radio, changes may occur in the overall
spectrum occupancy, or the signal quality observed by each user can fluctuate because of various
factors. These changes may require the cognitive radio to modify its multiple access parameters
accordingly.
2.3 What is SDR
An SDR is a radio in which the properties of carrier frequency, signal bandwidth,
modulation, and network access are defined by software. SDR is a general-purpose device in
which the same radio tuner and processors are used to implement many waveforms at many
frequencies. The advantage of this approach is that the equipment is more versatile and
costeffective. Additionally, it can be upgraded with new software for new waveforms and new
applications after sale, delivery, and installation.
2.4 Relationship between Cognitive Radio and Software Defind Radio
The main characteristics of cognitive radio is the adaptability where the radio parameters
(including frequency, power, modulation, bandwidth) can be changed depending on the radio
environment, user’s situation, and network condition. SDR can provide a very flexible radio
functionality by avoiding the use of application specific fixed analog circuits and components.
Therefore, cognitive radio needs to be designed around SDR. In other words, SDR is the
core enabling technology for cognitive radio. One of the most popular definitions of cognitive
radio, is: “A cognitive radio is an SDR that is aware of its environment, internal state, and
location, and autonomously adjusts its operations to achieve designated objectives.”Even though
many different models are possible, one of the simplest conceptual model that describes the
relation between cognitive radio and SDR can be described as shown in Figure. 1. In this simple
model, cognitive radio is wrapped around SDR. This model fits well to the definition of
cognitive radio, where the combination of cognitive engine, SDR, and the other supporting
functionalities (e.g. sensing) results in cognitive radio. Cognitive engine is responsible for
optimizing or controlling the SDR based on some input parameters such as that are sensed or
learned from the radio environment, user’s context, and network condition. Cognitive engine is
aware of the radio’s hardware resources and capabilities as well as the other input parameters.
5
Figure.1 : Relationship between SDR and CR
Hence, it tries to satisfy the radio link requirements of a higher layer application or user
with the available resources such as spectrum and power. Compared to hardware radio where the
radio can perform only single or very limited amount of radio functionality, SDR is built around
software based digital signal processing along with software tunable Radio Frequency (RF)
components. Hence, SDR represents a very flexible and generic radio platform which is capable
of operating with many different bandwidths over a wide range of frequencies and using many
different modulation. and waveform formats.
SDR can support multiple standards (i.e GSM, EDGE, WCDMA, CDMA2000, Wi-Fi,
WiMAX) and multiple access technologies such as Time Division Multiple Access (TDMA),
Code Division Multiple Access (CDMA), Orthogonal Frequency Division Multiple Access
(OFDMA), and Space Division Multiple Access (SDMA).
2.5 Physical architecture of the cognitive radio
A generic architecture of a cognitive radio transceiver is shown in Fig. 2. The main
components of a cognitive radio transceiver are the radio front-end and the baseband processing
unit. Each component can be reconfigured via a control bus to adapt to the time-varying RF
6
environment. In the RF front-end, the received signal is amplified, mixed and A/D converted. In
the baseband processing unit, the signal is modulated/demodulated and encoded/decoded. The
baseband processing unit of a cognitive radio is essentially similar to existing transceivers.
However, the novelty of the cognitive radio is the RF front-end. Hence, next, we focus on the RF
front-end of the cognitive radios.
Figure. 2 Physical architecture of the cognitive radio (a) Cognitive radio transceiver and
(b)wideband RF/analog front-end architecture
(Courtesy of http://www.cmpe.boun.edu.tr/~tugcu/research.html)
The novel characteristic of cognitive radio transceiver is a wideband sensing capability of the RF
front-end. This function is mainly related to RF hardware technologies such as wideband
antenna, power amplifier, and adaptive filter. RF hardware for the cognitive radio should be
7
capable of tuning to any part of a large range of frequency spectrum. Also such spectrum sensing
enables real-time measurements of spectrum information from radio environment. Generally, a
wideband front-end architecture for the cognitive radio has the following structure as shown
in Fig.2. The components of a cognitive radio RF front-end are as follows:
1. RF filter: The RF filter selects the desired band by bandpass filtering the received RF signal.
2. Low noise amplifier (LNA): The LNA amplifies the desired signal while simultaneously
minimizing noise component.
3 .Mixer: In the mixer, the received signal is mixed with locally generated RF frequency and
converted to the baseband or the intermediate frequency (IF).
4. Voltage-controlled oscillator (VCO): The VCO generates a signal at a specific frequency for a
given voltage to mix with the incoming signal. This procedure converts the incoming signal to
baseband or an intermediate frequency.
5. Phase locked loop (PLL): The PLL ensures that a signal is locked on a specific frequency
and can also be used to generate precise frequencies with fine resolution.
6. Channel selection filter: The channel selection filter is used to select the desired channel and
to reject the adjacent channels. There are two types of channel selection filters. The direct
conversion receiver uses a low-pass filter for the channel selection. On the other hand,
the superheterodyne receiver adopts a bandpass filter.
7. Automatic gain control (AGC): The AGC maintains the gain or output power level of an
amplifier constant over a wide range of input signal levels.
In this architecture, a wideband signal is received through the RF front-end, sampled by
the high speed analog-to-digital (A/D) converter, and measurements are performed for the
detection of the licensed user signal. However, there exist some limitations on developing the
cognitive radio front-end. The wideband RF antenna receives signals from various transmitters
operating at different power levels, bandwidths, and locations. As a result, the RF front-end
should have the capability to detect a weak signal in a large dynamic range. However, this
capability requires a multi-GHz speed A/D converter with high resolution.
The requirement of a multi-GHz speed A/D converter necessitates the dynamic range of
the signal to be reduced before A/D conversion. This reduction can be achieved by filtering
strong signals. Since strong signals can be located anywhere in the wide spectrum range, tunable
notch filters are required for the reduction. Another approach is to use multiple antennas such
8
that signal filtering is performed in the spatial domain rather than in the frequency domain.
Multiple antennas can receive signals selectively using beamforming techniques.
The key challenge of the physical architecture of the cognitive radio is an accurate
detection of weak signals of licensed users over a wide spectrum range. Hence, the
implementation of RF wideband front-end and A/D converter are critical issues in Cognitive
Radio Networks.
9
Chapter 3. Cognitive Radio Networks
3.1 Introduction
When we look at the evolution of wireless standards and technologies, it can be seen that
the adaptive features and intelligent network capabilities are gradually adapted as the hardware
and software technologies improve. specially, with the recent trend and interest in software
defined radio based architectures, cognitive radio and cognitive networks attracted more interest.
In addition to these, the increasing demand for wireless access along with the scarcity of the
wireless resources (specifically the spectrum) bring about the desire for new approaches in
wireless communications. Therefore, even though cognitive networks and cognitive radio terms
have recently become popular, it is actually a natural evolution of the wireless technologies.
With the emergence of cognitive radio and cognitive network concepts, this evolution process
has been more formalized and structured. Also, with these new concepts the perception of
adaptation and optimization of wireless communication systems gained new dimensions and
perspectives. Especially, the emergence of cognitive networks (with cooperative functions and
cognitive engine concepts) is a promising solution for the barrier that arises from the flaws of the
conventional layered design architecture.
The term “cognitive radio” defines the wireless systems that can sense, be aware of, learn,
and adapt to the surrounding environment according to inner and outer stimuli. Overall cognition
cycle can be seen as an instance of artificial intelligence, since it encompasses observing,
learning, reasoning, and adaptation. Adaptation itself in the cognition cycle is a complex
problem, because cognitive radio needs to take into account several inputs at the same time
including its own past observations as a result of learning property. Although the adaptation of
wireless networks is not a new concept, the previous standards and technologies strive to obtain
an adaptive wireless communication network from a narrower perspective (commonly focused
on a single-layer adaptation with a single objective function) as compared to that of cognitive
radio, which considers a global adaptation that includes multiple layers and goal functions.
For many researchers and engineers, the cognitive radio concept is not limited to a single
intelligent radio, but it also includes the networking functionalities. Cognitive networks can be
defined as intelligent networks that can automatically sense the environment (individually and
collaboratively) and current network conditions, and adapt the communication parameters
accordingly. Comparing the cognitive radio and cognitive networks definition, it can be seen that
the definitions are similar, except cognitive networks have more broader perspective that also
include all the network elements. Cognitive networks are expected to shape the future wireless
networks with important applications in dynamic spectrum access, and co-existence and
interoperability of different wireless networks. Among the special features of cognitive
networks, the leading ones are advanced interference management strategies, efficient use of
10
wireless resources, safe and secure wireless access methodologies, and excellent Quality of
Service (QoS). In spite of all these great features and possibilities, being a new concept, the
cognitive radio network poses many new technical challenges. As it will be described in the
subsequent sections, such networks have requirements in dynamic spectrum management, power
and hardware efficiency, complexity and size, spectrum sensing and interference identification,
environment awareness, user awareness, location awareness, new distributed algorithm design,
distributed spectrum measurements, QoS guarantees, and security. Addressing these
requirements is very critical for the success of these networks in wireless communication market,
and the authors of this chapter believe that ultra-wideband technology and networking has the
capability to accommodate some of these key requirements
3.2 Architecture for Cognitive Radio Networks
Current wireless network environment employs heterogeneity in terms of both spectrum
policy and communication technologies. Hence, a clear description of the cognitive radio
network architecture is crucial for development of communication protocols.
Figure. 3. Cognitive radio network architecture.
(courtesy of http://3g4g.blogspot.com/2007_06_01_archive.html)
11
The components of the cognitive radio network architecture, as shown in Figure. 3 , can be
classified in two groups as the primary network and the cognitive network. Primary network is
referred to as the legacy network that has an exclusive right to a certain spectrum band. While,
cognitive network does not have a license to operate in the desired band. The basic elements of
the primary and unlicensed networks are defined as follows:
1. Primary User: Primary user has a license to operate in a certain spectrum band. This access
can be only controlled by its base-station and should not be affected by the operations of any
other unauthorized user.
2. Primary Base-Station: Primary base-station is a fixed infrastructure network component
which has a spectrum license. In principle, the primary base-station does not have any cognitive
radio capability for sharing spectrum with cognitive radio users. However, primary base-station
may be required to have both legacy and cognitive radio protocols for the primary network
access of cognitive radio users.
3. Cognitive Radio User: Cognitive radio user has no spectrum license. Hence, the spectrum
access is allowed only in an opportunistic manner. Capabilities of the cognitive radio user
include spectrum sensing, spectrum decision, spectrum handoff and cognitive radio
MAC/routing/transport protocols. The cognitive radio user is assumed to have the capabilities to
communicate with not only the base-station but also other cognitive radio users.
4. Cognitive Radio Base-Station: Cognitive radio base-station is a fixed infrastructure
component with cognitive radio capabilities. Cognitive radio base-station provides single hop
connection to cognitive radio users without spectrum access license.
As shown in Figure 3, cognitive radio users can either communicate with each other in a
multihop manner or access the base-station. Thus, in our cognitive radio network architecture,
there are three different access types over heterogeneous networks, which show different
implementation requirements as follows:
1. Cognitive Radio Network Access: Cognitive radio users can access their own cognitive radio
base-station both in licensed and unlicensed spectrum bands. Since all interactions occur inside
the cognitive radio network, their medium access scheme is independent of that of primary
network.
2. Cognitive Radio Ad Hoc Access: Cognitive radio users can communicate with other
cognitive radio users through ad hoc connection on both licensed and unlicensed spectrum bands.
Also cognitive radio users can have their own medium access technology.
12
3. Primary Network Access: The cognitive radio user can access the primary base-station
through the licensed band, if the primary network is allowed. Unlike other access types,
cognitive radio users should support the medium access technology of primary network.
Furthermore, primary base-station should support cognitive radio capabilities.
3.3 Spectrum Management Framework for Cognitive Radio Networks
CR networks impose unique challenges due to the coexistence with primary networks as
well as diverse QoS requirements. Thus, new spectrum management functions are required for
CR networks with the following critical design challenges:
1. Interference Avoidance: CR network should avoid interference with primary networks.
2. QoS Awareness: In order to decide an appropriate spectrum band, CR networks should
support QoS-aware communication, considering dynamic and heterogeneous spectrum
environment.
3. Seamless Communication: CR networks should provide seamless communication regardless
of the appearance of the primary users.
Figure 4. Spectrum Management Framework.
(courtesy of http://ece.gatech.edu)
13
In order to address these challenges, we provide a directory for different functionalities
required for spectrum management in CR networks. The spectrum management process consists
of four major steps:
1. Spectrum Sensing: A CR user can only allocate an unused portion of the spectrum. Therefore,
the CR user should monitor the available spectrum bands, capture their information, and then
detect the spectrum holes.
2. Spectrum Decision: Based on the spectrum availability, CR users can allocate a channel. This
allocation not only depends on spectrum availability, but it is also determined based on internal
(and possibly external) policies.
3. Spectrum Sharing: Since there may be multiple CR users trying to access the spectrum, CR
network access should be coordinated in order to prevent multiple users colliding in overlapping
portions of the spectrum.
4. Spectrum Mobility: If the specific portion of the spectrum in use is required by a primary user,
the communication needs to be continued in another vacant portion of the spectrum. The
spectrum management framework for CR network communication is illustrated in Fig. 4. It is
evident from the significant number of interactions that the spectrum management functions
necessitate a cross-layer design approach. Thus, each spectrum management function cooperates
with application, transport, routing, medium access and physical layer functionalities with taking
into consideration the dynamic nature of the underlying spectrum.
3.4 Spectrum Sensing for Cognitive Radio Networks
A cognitive radio should monitor the available spectrum bands, capture their information,
and then detect the spectrum holes. Hence, spectrum sensing is a key enabling technology in
cognitive radio networks. In spectrum sensing, the detection accuracy has been considered as the
most important factor to determine the performance of cognitive radio networks.
Figure 5. Periodic spectrum sensing structure.
(courtesy of http://ece.gatech.edu)
14
However, in reality, RF frontend of CR users cannot differentiate the primary user signals
and CR user signals. In case of the energy detection, widely used in spectrum sensing,
transmission and sensing cannot be performed at the same time. Thus, during the
sensing(observation time), all CR users should stop their transmissions and keep quiet. Due to
this hardware restriction, CR users should sense the spectrum periodically with sensing period Ts
and observation time ts, as described in Figure 5. The periodic spectrum sensing should consider
following design issues:
1. Interference Avoidance: In the periodic sensing, interference is related to not only sensing
accuracy depending on observation time but also the CR transmission time and tarffic statistics.
2. Spectrum Efficiency: The main objective of cognitive radio is the efficient use of spectrum
resources. However, since CR users cannot not transmit during the sensing, spectrum efficiency
will be degraded in evitably.
In Cognitive radio network, available spectrums may show different characteristics with
the bandwidth, the primary user activity, and acceptable interference limit, which affect both the
sensing accuracy and spectrum efficiency. Thus, spectral efficient sensing technique is essential
for cognitive radio networks. Hence, in this project we will propose the spectral efficient sensing
technique for cognitive radio networks, which provides optimal spectrum sensing period and
observation time to maximize the efficiency of each spectrum bands subject to the resource
limitation and interference restriction.
3.5 Spectrum Decision Framework for Cognitive Radio Networks
In cognitive radio (CR) networks, unused spectrum bands will be spread over a wide
frequency range including both unlicensed and licensed bands. These unused spectrum bands
detected through spectrum sensing show different characteristics according to the radio
environment. Since CR networks can have multiple available spectrum bands having different
channel characteristics, they should be capable of selecting the proper spectrum bands according
to the application requirements, called spectrum decision.
15
Figure 6. Spectrum decision framework for cognitive radio networks
(courtesy of http://ece.gatech.edu)
At first, each spectrum band is characterized for the spectrum decision, based on not only
local observations of CR users but also statistical information of primary networks. Through the
local measurement, CR users can estimate the channel conditions such as capacity, bit error rate
(BER), and delay. In order to describe the dynamic nature of CR networks, a new metric,
primary user activity, defined as the probability of the primary user appearance during the CR
user transmission. After the spectrum characterization, the CR network chooses the best
spectrum bands through the following spectrum operations. the CR network uses multi-spectrum
transmission based on OFDM technology. This decision process can be modeled as an
optimization problem.
In Cognitive Radio Networks, a QoS aware spectrum decision framework is proposed to
determine a set of spectrum bands by considering the application requirements as well as the
dynamic nature of spectrum bands as shown in Figure 5. Specifically, for real-time applications,
a minimum variance-based spectrum decision (MVSD) is proposed so as to minimize the
capacity variance of the decided spectrums subject to the capacity constraint. Furthermore, a
maximum capacity-based spectrum decision (MCSD) is proposed for the best effort applications
where spectrum bands are decided to maximize the total throughput. Moreover, a dynamic
admission control scheme is developed to decide on the spectrum bands adaptively dependent on
the time-varying CR network capacity.
16
3.6 Inter-Cell Spectrum Sharing in Cognitive Radio Networks
Cognitive radio (CR) networking achieves high utilization of the scarce spectrum
resources without causing any performance degradation to the licensed users. Since the spectrum
availability varies over time and space, the infrastructure-based CR networks are required to
have a dynamic inter-cell spectrum sharing capability. This allows fair resource allocation as
well as capacity maximization and avoids the starvation problems seen in the classical spectrum
sharing approaches. A joint spectrum and power allocation framework is proposed that addresses
these concerns by (i) opportunistically negotiating additional spectrum based on the licensed user
activity (exclusive allocation), and (ii) having a share of reserved spectrum for each cell
(common use sharing). Our algorithm accounts for the maximum cell capacity, minimizes the
interference caused to neighboring cells, and protects the licensed users through a sophisticated
power allocation method.
Figure 7. Inter-Cell Spectrum Sharing Framework
(courtesy of http://ece.gatech.edu)
Infrastructure-based CR networks are required to provide two different types of spectrum
sharing schemes: intra-spectrum sharing and inter-spectrum sharing. In order to share spectrum
resource efficiently, CR networks necessitate a unified framework to support cooperation among
inter- and intra-cell spectrum sharing schemes and other spectrum management functions. Figure
7 shows the framework for spectrum sharing in infrastructure based CR networks, which
consists of inter-cell spectrum sharing, intra-cell spectrum sharing, and event monitoring.
1. Event Monitoring: The event monitoring has two different functionalities. One is to detect
the Primary User (PU) activities, called spectrum sensing. CR users sense the radio environment
continuously and send monitoring results to their base-station. The periodic sensing has separate
time slots for sensing and transmission. In addition, CR users monitor the quality-of-service
17
(QoS) of their transmission. According to the detected event type, the base-station determines the
spectrum sharing strategies and allocates the spectrums to each user adaptively to the radio
environments.
2. Cell Spectrum Sharing: The intra-cell spectrum sharing enables the base-station to avoid the
interference to the primary networks as well as to maintain the QoS of its CR users by allocating
spectrum resource adaptively to the event detected inside its coverage. If a new CR user appears
in this cell, the base-station determines its acceptance and selects the best available spectrum
band if it is admitted. Furthermore, when some of its CR users cannot maintain the guaranteed
QoS or lose their connections due to the PU activities, the base-station should re-allocate the
spectrum resource to them immediately. Also a CR MAC protocol is required to allow multiple
CR users to access to the same spectrum band. The intra-cell spectrum sharing has been widely
investigated in many literatures and is out of the scope in this project.
3. Inter-Cell Spectrum Sharing: In CR networks, the available spectrum bands vary over time
and space which makes it difficult to provide reliable spectrum allocation. Especially in the
infrastructure-based networks, the inter-cell interference also needs to be considered in spectrum
sharing so as to maximize the network capacity. In the framework, the inter-cell spectrum
sharing is comprised of two subfunctionalities: spectrum allocation and power allocation. In the
spectrum allocation, the base-station determines its spectrum bands by considering the
geographical information of primary networks and current radio activities. The power allocation
enables the base-station to determine the transmission power of its assigned spectrum bands so as
to maximize the cell capacity without interference to the primary network. When the service
quality of the cell becomes worse or is below the guaranteed level, the base-station initiates the
inter-cell spectrum sharing and adjusts its spectrum allocation. Based on the spectrum allocation,
the base-station determines its transmission power over the allocated spectrum bands
3.7 Spectrum Mobility for Cognitive Radio Networks
As CR networks have capability to support flexible usage of wireless radio spectrum,
cognitive radio (CR) techniques have attracted increasing attention in recent years. In CR
networks, secondary users may dynamically access underutilized spectrum without interfering
with primary users, which is called spectrum handoff. Spectrum handoff refers to the procedure
invoked by the cognitive radio users when they users wish to transfer their connections to an
unused spectrum band. Spectrum handoff occurs
1) when primary user is detected or 2) current spectrum condition becomes worse.
The cognitive radio users monitor the entire unused spectrum continuously during the
transmission. If spectrum handoff occurs, they move to the "best matched" available spectrum
18
band. However, due to the latency caused by spectrum sensing, decision and handoff procedures,
quality degrades during spectrum handoff. Hence, our spectrum handoff method focuses on the
seamless transition with minimum quality degradation.
For infrastructure based CR networks, a novel hierarchical spectrum handoff scheme with
intra/inter-pool spectrum handoff is designed. The adaptive QoS intra-pool spectrum
handoff scheme considers both application level QoS and connection level QoS of different SUs
to efficiently re-allocate spectrum resource inside a cell. The adaptive threshold inter-pool
spectrum handoff scheme adopts mix load indicator of PUs and SUs to get adaptive threshold to
trigger network initiated handoff. Then opportunistic cell capacity is used to calculate the
handoff amount, and the most suitable SUs in the overlapping areas are chosen to accomplish the
handoff. As spectrum-space domain resource utilization, the combination of the two kinds of
handoff can maximize spectrum utilization and minimize spectrum handoff dropping probability.
3.8 Spectrum Aware Routing Protocol for Cognitive Radio Networks
Routing constitutes a rather important but yet unexplored problem in CR networks,
especially when a multi-hop architecture is considered. The activity of the primary users (PUs)
affects the channels of the licensed bands differently. This renders the channels unusable for the
CR network to different geographical extents around the PU. In such a situation, the key decision
is switching the channel in portions of the route, thus incurring a switching delay, or passing
through entirely different regions altogether, thus increasing the latency. In addition, the
frequently changing primary user (PU) activity and the mobility of the users make the problem
of maintaining optimal routes in ad-hoc CR networks challenging. A geographic forwarding
based Spectrum Aware Routing protocol for Cognitive Ad-hoc networks (SEARCH) has
developed, that:
1.Jointly undertakes path and channel selection to avoid regions of PU activity during route
formation.
2.Adapts to the newly discovered and lost spectrum opportunity during route operation.
3.Predicts node mobility and takes corrective measures to maintain end-to-end performance.
We consider a three-dimensional system, with the x-y plane representing the physical space
where the CR network and the PUs are located. The z-axis shows the frequency scale and also
the different channel bands. The shaded regions in the figure show that a single PU may affect
several channels (frequencies) around its location.
19
Figure 8. Joint route and spectrum discovery.
(courtesy of http://ece.gatech.edu)
Moreover, the channels may be affected to different geographical extents, depending upon
their frequency separation with the PU's transmission channel. SEARCH attempts to find paths
which circumvent the PU coverage regions (Path 1 and 2) and link them together, whenever a
performance benefit is seen.
1. Route Formation: Paths are first constucted on each channel, independently of the others.
They are then merged at the destination and these combination points indicate channel switching
where a finite switching delay is incurred. SEARCH balances the path delay due to
circumventing the PU region as against the switching delay in order to find the best route to the
destination.
2. Route Maintenance: SEARCH binds routes to regions found free of PU activity, rather than
particular CR users. Thus even if nodes move away, the route is kept operational by choosing
replacement nodes that are close to the original locations. Moreover, when new spectrum is
detected or the channel becomes unusable owing to the appearance of a PU, the route is updated.
During these update stages, the optimality of the route may be partially lost as the key
consideration is prevention of performance degradation to the PU. SEARCH intelligently
minimizes complete re-routing by undertaking local recovery actions.
20
Chapter 4. Transport Protocol for Cognitive Radio Networks
The dynamic nature of the underlying spectrum in cognitive radio networks is known to
have adverse influences on the overall throughput of the transmission functions. It is important to
seamless integrate the channel sensing and primary user (PU) detection techniques in the design
of higher layer, end-to-end transport protocols. Here a window-based transport protocol, TP-
CRAHN, that that not only addresses the key concerns on classical wireless ad hoc networks,
such as mobility, but also considers spectrum sensing, channel switching, PU activity and other
issues that are commonly seen CR networks.
Figure 9. The state diagram for the TP-CRAHN protocol.
(courtesy of http: //www.ece.gatech.edu)
The finite state machine diagram of the transport layer protocol is shown in Figure. 9, with
the state changes. These states are (i) Connection Establishment, (ii) Normal, (iii) Spectrum
Sensing, (iv) Spectrum Change, (v) Mobility Predicted, and (vi) Route Failure. Each of these
states addresses a particular CR network condition and we describe them as follows.
21
1. Connection Establishment State: In this state, a three-way handshake is used to setup the
TCP connection. The spectrum sensing durations and start times of the intermediate nodes are
also made known to the source. On successful handshake, the protocol enters into the Normal
state.
2. Normal State: This state comes into play when there is no periodic sensing, spectrum
switching or anticipated node mobility. The congestion window operates similar to the classical
TCP. The source collects the residual buffer capacity, link latency and the calculated link
bandwidth at each node by piggybacking this information over the incoming ACK.
3. Spectrum Sensing State: As the source knows the exact start and stop times for sensing, it
limits the congestion window so that the previous hop node along the path does not incur a
buffer overflow for the duration of the sensing. Moreover, it decides on the optimal sensing time
for each link by maintaining a history of the PU activity in the vicinity.
4. Spectrum Switching State: When a PU appears, the time taken to identify a new channel is
not known in advance. At this time, the TCP state at the source is frozen. After the new spectrum
is chosen, the bandwidth is estimated by link layer interaction and communicated to the source.
This immediately changes the congestion window appropriately if the change in the bandwidth
affects the earlier bottleneck bandwidth of the path.
5. Mobility Predicted State: Based on Kalman Filtering, each node makes a prediction if the next
hop node will be out of range in the next calculation epoch. If this is true, the source is signaled
to limit the congestion window below the TCP threshold, thereby preventing large packet losses
if the route failure actually occurs.
6. Route Failure State: This state can be inferred if there is no expected sensing, no detected PU
but possibility of node mobility, as predicted by the above state. In this case, the source stops the
transmission and awaits further notification from the network layer for new route establishment.
22
Chapter 5. Cognitive Mesh Networks
The Wireless Mesh Network (WMN) paradigm is envisaged to be a key technology that
allows ubiquitous connectivity to the end user. A typical WMN consists of mesh routers (MRs)
forming the backbone of the network, interconnected in an ad-hoc fashion. Each MR can be
considered as an access point serving a number of users or mesh clients (MCs) under it. The
MCs could be mobile users, stationary workstations or laptops that exchange data over the
Internet. The COgnitive Mesh NETwork (COMNET) architecture, takes the first step in
leveraging the benefits of cognitive radio technology in the area of WMNs.
Figure 10. Cognitive mesh network architecture.
(courtesy of http: //www.ece.gatech.edu)
23
The following key challenges in a cognitive radio enabled mesh scenario:
1. Enabling MCs to monitor the primary channel while continuing normal operation in the 2.4
GHz ISM band.
2. Devising a theoretical framework for identifying primary transmitter frequencies through time
domain sampling.
3. Proposing theoretical models for estimating power injected in the primary band channels due
to the presence of secondary users.
4. Allowing a decentralized computation framework at each MR for load sharing between the
primary and secondary bands, based on the above models.
24
Chapter 6. Cognitive Sensor Networks: Interferer Classification and
Transmission Adaptation
Wireless sensor networks (WSNs) are being increasingly deployed for a variety of
environment monitoring, commercial utility metering, military and surveillance applications.
These applications necessitate reliable data delivery with minimum packet loss due to external
interference. When these sensors form a CR network, some of the transmission channels may be
affected by the PU. In a noisy environment with multiple interferer types, such as wireless LANs
and commercial microwave ovens, distinguishing between them becomes a key challenge. By
identifying the presence of a specific type of an interferer, the sensors can choose their
transmission channel, and also adapt their packet scheduling at the MAC layer to avoid packet
losses due to interference.
Figure 11. Identifying the interferer type and location.
(courtesy of http: //www.ece.gatech.edu)
The problem of detecting the above interferer characteristics is addressed by:
1. Experimentally profiling the spectral characteristics specific to wireless LANs based on the
IEEE 802.11b and commercial microwave ovens.
2. Devising a scheme for identifying interferer type based on matching the observed and the
previously obtained spectral data.
25
3. A channel selection scheme is given, where the sensors choose the channels not occupied by
WLAN transmissions and not affected by the radiation caused by microwave ovens.
4. A transmission adaptation scheme at the MAC layer given, in which, the sensor packets are
scheduled between two WLAN transmissions, and during the off times of the duty cycled
microwave ovens.
To see how the channels used by the sensors are affected due to specific interferers, the
WLAN and commercial microwave ovens are used. The measured received power in each
channel is used to create a reference vector (Figure 11 (a)) in an n-dimensional space, where n is
the total number of channels affected. This reference vector is then compared with an observed
channel power vector during the network operation to identify the type of the interferer based on
the channels that are affected. Finally, using clustering methods, the sensors that sense a
common interferer are grouped together. The PU or interferer location can then be approximated
as the cluster center (Figure 11 (b)). This process allows sensors to identify the PU
characteristics by delegating the computational complexity to the sink node.
26
Chapter 7. Cognitive Radio Network applications
Cognitive Radio Networks can be applied to the following cases:
1. Leased network: The primary network can provide a leased network by allowing opportunistic
access to its licensed spectrum with the agreement with a third party without sacrificing the
service quality of the primary user. For example, the primary network can lease its spectrum
access right to a mobile virtual network operator (MVNO). Also the primary network can
provide its spectrum access rights to a regional community for the purpose of broadband access.
2. Cognitive mesh network: Wireless mesh networks are emerging as a cost-effective technology
for providing broadband connectivity. However, as the network density increases and the
applications require higher throughput, mesh networks require higher capacity to meet the
requirements of the applications. Since the cognitive radio technology enables the access to
larger amount of spectrum, CR networks can be used for mesh networks that will be deployed in
dense urban areas with the possibility of significant contention. For example, the coverage area
of CR networks can be increased when a meshed wireless backbone network of infrastructure
links is established based on cognitive access points (CAPs) and fixed cognitive relay nodes. The
capacity of a CAP, connected via a wired broadband access to the Internet, is distributed into a
large area with the help of a fixed CRN. CR networks have the ability to add temporary or
permanent spectrum to the infrastructure links used for relaying in case of high traffic load.
3. Emergency network: Public safety and emergency networks are another area in which CR
networks can be implemented. In the case of natural disasters, which may temporarily disable or
destroy existing communication infrastructure, emergency personnel working in the disaster
areas need to establish emergency networks. Since emergency networks deal with the critical
information, reliable communication should be guaranteed with minimum latency. In addition,
emergency communication requires a significant amount of radio spectrum for handling huge
volume of traffic including voice, video and data. CR networks can enable the usage of the
existing spectrum without the need for an infrastructure and by maintaining communication
priority and response time.
4. Military network: One of the most interesting potential applications of an CR network is in a
military radio environment. CR networks can enable the military radios choose arbitrary,
intermediate frequency (IF) bandwidth, modulation schemes, and coding schemes, adapting to
the variable radio environment of battlefield. Also military networks have a strong need for
security and protection of the communication in hostile environment. CR networks could allow
military personnel to perform spectrum handoff to find secure spectrum band for themselves and
their allies.
27
Summary
Cognitive radio is an immature but rapidly developing technology area. In terms of
spectrum regulation, the key benefit of CR is more efficient use of spectrum, because CR will
enable new systems to share spectrum with existing legacy devices, with managed degrees of
interference. There are significant regulatory, technological and application challenges that need
to be addressed and CR will not suddenly emerge.
Cognitive radio networks are being studied intensively. The major motivation for this is
the currently heavily underutilized frequency spectrum. The development is being pushed
forward by the rapid advances in SDR technology enabling a spectrum agile and highly
conigurable radio transmitter/receiver. A fundamental property of the cognitive radio networks is
the highly dynamic relationship between the primary users having an exclusive priority to their
respective licensed spectrum and the secondary users representing the cognitive network devices.
This creates new challenges for the network design which have been addressed applying varies
approaches as has been discussed in the previous sections.
The fundamental problems in detecting the spectrum holes are naturally mostly related to
signal processing at the physical layer. From the traffic point of view careful attention must be
paid in order to guarantee an effcient usage of the wireless medium while simultaneously
providing fairness between competing users and respecting the priority of the primary users.
28
References
[1] Bruce A. Fette, 2006. Cognitive Radio Technology, 1st ed, ELSEVIER, USA.
[2] Ekram Hossain & Vijay Bhargava 2007. “Cognitive Wireless Communication Networks”,
Springer Science+Business Media, LLC.
[3] Huseyin Asrlan 2007. Cognitive Radio, Software Defined Radio, and Adaptive Wireless
Systems, Springer, P.O. Box 17, 3300 AA Dordrecht, The Netherlands.
[4] Lars Berlemann, George Dimitrakopoulos, Klaus Moessner, Jim Hoffmeyer, 2005 “Cognitive
Radio and Management of Spectrum and Radio Resources in Reconfigurable Networks” IEEE
2005, Wireless World Research Forum.
[5] Won-Yeol Lee and Ian. F. Akyildiz, “Optimal Spectrum Sensing Framework for Cognitive
Radio Networks”, IEEE Transactions on wireless communications, vol. 7, NO. 10, OCTOBER
2008.
[6] Joe Evans, U. Kansas Gary Minden, U. Kansas Ed Knightly, Rice, Sep 15 2006, “Technical
Document on Cognitive Radio Networks”, IEEE.
[7] Aleksandar Jovicic and Pramod Viswanath May 8 2006. “Cognitive Radio: An Information-
Theoretic Perspective”, IEEE ISIT 2006, Seattle, USA, July 9-14, 2006.
[8] K. C. Chen, Y. J. Peng, N. Prasad, Y. C. Liang, S. Sun, “Cognitive Radio Network
Architecture: Part I – General Structure” IEEE.
[9] N. Devroye, P. Mitran, et al., "Limits on communications in a cognitive radio channel," IEEE
Communications Magazine, Vol.44, No.6, pp. 44-49, 2006.
[10] X. Hong, Z. Chen, C.-X. Wang, S. A. Vorobyov, and J. S. Thompson “Interference
cancellation for cognitive radio networks,” IEEE Vehi. Technol. Mag, submitted for publication.
[11] X. Hong, C.-X. Wang, H.-H. Chen, and Y. Zhang, “Secondary spectrum access networks:
spatial modellingand system design,” IEEE Vehi. Technol. Mag., accepted for publication, 2009.
[12] C.-X. Wang, X. Hong, H.-H. Chen, and J. S. Thompson, “On capacity of cognitive radio
networks under average interference power constraints”, IEEE Trans. Wireless Commun.,
revised version submitted for publication.
[13] C.-X. Wang, H.-H. Chen, X. Hong, and M. Guizani, “Cognitive radio network
management: tuning in to real time conditions”, IEEE Vehi. Technol. Mag., vol. 3, no. 1, pp. 28-
-35, March 2008.
29
Acronyms
A/D Analog to Digital Converter
ACK Acknowledgement
AGC Automatic Gain Control
BER Bit Error Rate
CAP Cognitive Access Points
CDMA Code Division Multiple Access
COMNET COgnitive Mesh NETwork
CR Cognitive Radio
CRN Cognitive Radio Networks
CRAHN Cognitive Radio Ad-hoc Network
D/A Digital to analog converter
DECT Digital European Cordless Telephone
ECN Explicit Congestion Notification
EDGE Enhanced Data Rates for GSM Evolution
GSM Global System for Mobile communications
IF Intermediate Frequency
LAN Local Area Network
LNA Low Noise Amplifier
MAC Medium Access Control Layer
MCSD Maximum Capacity based spectrum decision
MR Mesh routers
MVNO Mobile virtual Network Operator
MVSD
OFDMA
Minimum variance based spectrum decision
Orthogonal Frequency Division Multiple Access
PLL Phase Locked Loop
PU Primary User
QoS Quality of Service
RF Radio Frequency
30
SDMA Space Division Multiple Access
SDR Software Defined Radio
SEARCH Spectrum Aware Routing protocol for Cognitive Ad-hoc networks
TCP Transmission Control Protocol
TDMA Time Division Multiple Access
VCO Voltage-Controlled Oscillator
WCDMA Wireless Code Division Multiple Access
WDMA Wavelength Division Multiple Access
Wi-Fi Wireless Fidelity
Wi MAX Worldwide Interoperability for Microwave Access
WLAN Wireless Local Area Network
WMN Wireless Mesh Network
WSN Wireless Sensor Networks
