m. Tech Seminar

8/3/2019 m. Tech Seminar

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Seminar Report

On

Cogni tive Radio Networks

Submitted in partial ful f i l lment of the requirements

For the degree of

Master of Technology

(Communication System)

By

Ajay Kumar Gautam

(Roll No. P08EC901)

under the guidance of

M r. Golak Santra

2008-2009

Electronics Engineering Department

Sardar Vallabhbhai National Institute of TechnologySurat-395007, Gujarat, India.


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Sardar Vallabhbhai National Institute of TechnologySurat- 395007, Gujarat, India.

Electronics Engineering Department

CERTIFICATE

This is to certify that Mr. Ajay Kumar GautamRoll no. P08EC901 of M.Tech.-I

(Communication System) has satisfactory presented a Seminar on Cognitive

Radio Networksduring 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


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Contents

List of figures i

Abstract ii1. 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

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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

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4. Transport Protocol for Cognitive Radio Networks 20-21

5. Cognitive Mesh Networks 22-23

6. Cognitive Sensor Networks: Interferer Classification and Transmission

Adaptation24-25

7. Cognitive Radio Network applications

Summary

References

Acronyms

26

27

28

29


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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


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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


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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 channelcharacteristics, 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 Cogniti ve 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 ofthe nodes.


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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 performbeneficial 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 SDRs 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 defindas:

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,

andautonomously 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.


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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 users 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.


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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 newapplications 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, users 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 thecore 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, users context, and network condition. Cognitive engine is

aware of the radios hardware resources and capabilities as well as the other input parameters.


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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 theradio 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


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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


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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 fi lter: The RF filter selects the desired band by bandpass filtering the received RF signal.

2. Low noise ampli fi er (LNA):The LNA amplifies the desired signal while simultaneously

minimizing noise component.

3 .M ixer: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-control led oscil lator (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 f il ter: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 receiveruses a low-pass filter for the channel selection. On the other hand,

thesuperheterodyne receiveradopts 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


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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.


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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 andperspectives. 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 ofwireless 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


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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)


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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. Primar y User: Primary user has a license to operate in a certain spectrum band. This accesscan 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 componentwhich 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. Cogni tive Radio User: Cognitive radio user has no spectrum license. Hence, the spectrumaccess 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. Cogni tive Radio Network Access: Cognitive radio users can access their own cognitive radiobase-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. Cogni tive 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.


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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. I nterference 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)


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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 Shari ng: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 Mobil ity: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.



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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. I nterference 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 Ef f iciency: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, calledspectrum decision.


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Figure 6. Spectrum decision framework for cognitive radio networks


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 anoptimization 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 onthe time-varying CR network capacity.


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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


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, whichconsists of inter-cell spectrum sharing, intra-cell spectrum sharing, and event monitoring.

1. Event Monitori ng: 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


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(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. I nter-Cell Spectrum Shar ing:In CR networks, the available spectrum bands vary over timeand 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 calledspectrum 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


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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 handoffis designed. The adaptive QoS intra-pool spectrum

handoffscheme 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 handoffscheme 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) hasdeveloped, 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 affectseveral channels (frequencies) around its location.


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Figure 8. Joint route and spectrum discovery.


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 thanparticular 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.


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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.


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1. Connection Establ ishment State:In this state, a three-way handshake is used to setup theTCP 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, spectrumswitching 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, itlimits 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 isnot 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. Mobili ty Predicted State:Based on Kalman Filtering, each node makes a prediction if the nexthop 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 Fai lu re State:This state can be inferred if there is no expected sensing, no detected PUbut 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.


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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.



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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 timedomain 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.


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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.


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.


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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 PUcharacteristics by delegating the computational complexity to the sink node.


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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. Cogni tive 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 disasterareas 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. M il itary 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 tothe 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.


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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 variesapproaches 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.


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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,IEEETransactions 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 IGeneral Structure IEEE.

[9] N. Devroye, P. Mitran, et al., "Limits on communications in a cognitive radio channel," IEEECommunications 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 networkmanagement: tuning in to real time conditions, IEEE Vehi. Technol. Mag., vol. 3, no . 1, pp. 28--35, March 2008.


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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


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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

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