Seminar Report

On

COGNITIVE RADIO

Submitted in partial fulfilment of the requirements

For the degree of

Bachelor of Technology

(Electronics & Communication Engineering)

By

Shubham Goyal

Roll No - 111503

Under the guidance of

Mr Gaurav Verma

(Assistant Professor)

Department of Electronics & Communication Engineering

National Institute of Technology, Kurukshetra

2013

i

Preface

The objective of this report is to provide an introduction to the cognitive radio. Reader should

be familiar with basics of communication system and signals and system. Helpful but not

required are prior exposure to linear system analysis, Fourier transforms and probability theory.

This report is so organised to bring readers with electrical and electronics background to a level

of understanding which will allow to them to read much of the current literature on new

applications.

As a guide to topic selection, the table of contents indicates the minimum prerequisites for each

chapter selection. List of abbreviations and list of figures are provided in the starting of the

report. The text has been divided into 6 chapters. The first two chapters discuss the basic

introduction of the cognitive radio and its evolution. It also deals with the detailed architecture

of cognitive radio. Next chapter describe the cognitive radio networks and dynamic system

allocation. The fourth chapter deals with the functions of cognitive radio including various

spectrum sensing techniques along with their detailed explanation. The last two chapters deals

with the applications of cognitive radio and the present scenario of cognitive radio. References

at the end of the report can be of great help if the reader wants to study the topic thoroughly.

Although great care has been exercised, some errors or omissions in the text will inevitably

occur. Anyone finding them will do me a great favour by writing to me so that they can be

corrected. I hope the report provides an enjoyable and rewarding experience for the readers.

ii

Acknowledgement

“Say a word of gratitude and splendour-

In a moment it is gone,

But there are a hundred ripples

Circling on & on & on……………………….”

I, student of National Institute of Technology, Kurukshetra (ECE - 5th Semester), express

my heartfelt thanks to all my friends and teachers for providing me their valuable guidance &

sharp vision in the development of this report. Their support helped me to focus in the right

direction.

I would like to thank and express my gratitude towards Mr Gaurav Verma (Asst. Professor) for

providing me the project and for his support and guidance in completing this report. Without

his guidance, this report would not have been possible. He offered help when needed in every

aspect.

I also wish to extend my thanks to my classmates for their generous help and useful

suggestions.

SHUBHAM GOYAL

iii

Abstract

Cognitive radio has been considered as a key technology for future wireless communications

and mobile computing. It is an emerging technology that enables the flexible development and

deployment of highly adaptive radios that are built upon software defined radio technology. As

we know that radio frequency spectrum is a scarce resource and its efficient use is of the great

importance. The spectrum bands are usually licensed to certain services, such as mobile, fixed,

broadcast, and satellite. Most spectrum bands are allocated to certain services but worldwide

spectrum table show that only portions of the spectrum band are fully used. Moreover, there

are large temporal and spatial variations in the spectrum occupancy. In the development of

future wireless systems the spectrum utilization functionalities will play a key role due to the

scarcity of unallocated spectrum. Moreover, the trend in wireless communication systems is

going from fully centralized systems into the direction of self-organizing systems where

individual nodes can instantaneously establish ad hoc networks whose structure is changing

over time. Cognitive radios, with the capabilities to sense the operating environment, learn and

adapt in real time according to environment creating a form of mesh network, are seen as a

promising technology.

iv

Table of Contents

Preface ................................................................................................................................................. - i -

Acknowledgement .............................................................................................................................. - ii -

Abstract .............................................................................................................................................. - iii -

Table of Contents ............................................................................................................................... - iv -

List of Abbreviations ........................................................................................................................... - v -

List of Figures ................................................................................................................................... - vii –

CHAPTER 1. INTRODUCTION ............................................................................................................... - 1 -

CHAPTER 2. COGNITIVE RADIO ........................................................................................................... - 2 -

2.1 What are Cognitive Radios? .................................................................................................. - 2 -

2.2 What is Software Defined Radio (SDR)? .............................................................................. - 2 -

2.3 Need of Cognitive Radio ......................................................................................................... - 3 -

2.4 How is Cognitive Radio different from other Radios? ........................................................ - 4 -

2.5 Physical architecture of the cognitive radio.......................................................................... - 4 -

CHAPTER 3. COGNITIVE RADIO NETWORKS........................................................................................ - 6 -

3.1 Architecture for Cognitive Radio Networks ......................................................................... - 6 -

3.2 Dynamic System Allocation.................................................................................................... - 7 -

CHAPTER 4. FUNCTIONS OF COGNITIVE RADIO .................................................................................. - 8 -

4.1 Spectrum Sensing .................................................................................................................... - 8 -

4.1.1 Transmitter Detection ................................................................................................... - 10 -

4.1.2 Cooperative detection .................................................................................................... - 11 -

4.2 Spectrum Decision ................................................................................................................ - 12 -

4.3 Spectrum Mobility ................................................................................................................ - 12 -

4.4 Spectrum Sharing ................................................................................................................. - 13 -

CHAPTER 5. APPLICATIONS OF COGNITIVE RADIO ........................................................................... - 15 -

CHAPTER 6. PRESENT SCENARIO ....................................................................................................... - 16 -

Conclusion ......................................................................................................................................... - 17 -

References ........................................................................................................................................ - 18 -

v

List of Abbreviations

A/D Analog To Digital Converter

AP Access Point

AWGN Additive White Gaussian Noise

BER Bit Error Rate

CR Cognitive Radio

CRAHN Cognitive Radio Ad-Hoc Network

CRN Cognitive Radio Networks

D/A Digital To Analog Converter

DISA Defence Information Systems Agency

DoD Department Of Defence

DSA Dynamic Spectrum Access

DSO Defence Spectrum Organisation

DTT Digital Terrestrial Television

DTV Digital Television

FCC Federal Communications Commission

GEMSIS PMO Global Electromagnetic Spectrum Information System Program Management Office

GSM Global System For Mobile Communications

IEEE Institute of Electrical and Electronics Engineers

IF Intermediate Frequency

ISM Industrial, Scientific and Medical

ITU International Telecommunication Union

JSC Joint Spectrum Center

LAN Local Area Network

LNA Low Noise Amplifier

MAC Medium Access Control

ML Maximum Likelihood

NTIA National Telecommunication And Information Administration

OFDM Orthogonal Frequency Division Multiplexing

vi

PLL Phase Locked Loop

PU Primary User

QoS Quality Of Service

RF Radio Frequency

SCF Spectral Correlation Function

SDR Software Defined Radio

SU Secondary User

UNII Unlicensed National Information Infrastructure

VCO Voltage-Controlled Oscillator

Wi-Fi Wireless Fidelity

WLAN Wireless Local Area Network

WMN Wireless Mesh Network

vii

List of Figures

Figure 1: Use of frequency spectrum.......................................................................................................3

Figure 2: Difference between Cognitive Radio and other radio..............................................................4

Figure 3: Physical Architecture of the Cognitive Radio...........................................................................5 (a) Cognitive radio transceiver and (b) Wideband RF/analog front-end architecture

Figure 4: Cognitive Radio Network Architecture.....................................................................................6

Figure 5: Dynamic Spectrum Access.......................................................................................................7

Figure 6: Periodic Spectrum Sensing Structure......................................................................................8

Figure 7: Spectrum Holes or White Spaces.............................................................................................9

Figure 8: Spectrum Sensing Techniques.................................................................................................9

Figure 9: Block Diagram of Energy Detector........................................................................................10

Figure 10: Implementation of Cyclostationary Feature Detector..........................................................11

Figure 11: Cognitive Radio Operation..................................................................................................14

- 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.

- 2 -

CHAPTER 2

COGNITIVE RADIO

2.1 What are Cognitive Radios?

The concept of Cognitive Radio was first proposed by Joseph Mitola III in a seminar at the

Royal Institute of Technology in Stockholm in 1998 and published in an article by Mitola and

Gerald Q. Maguire, Jr. in 1999. It was thought of as an ideal goal towards which a Software-

Defined Radio (SDR) platform should evolve.

Listed below are the definitions of cognitive radio by some institutions -

� ITU: A radio or system that senses, and is aware of, its operational environment and

can dynamically and autonomously adjust its radio operating parameters accordingly.

� FCC: CR is a radio that can change its transmitter parameters based on interaction with

the environment in which it operates.

� NTIA: A radio or system that senses its operational electromagnetic environment and

can dynamically and autonomously adjust its radio operating parameters to modify

system operation.

� WWRF: CR employs a dynamic time-frequency power based radio measurement and

analysis of the RF environment, to make an optimum choice of carrier frequency and

channel bandwidth to guide the transceiver in its end-to-end communication, with

quality of service being an important design requirement.

They all talk about a radio, interaction with the environment, measuring, decision making,

autonomicity and adaptation.

So 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.

2.2 What is Software Defined Radio (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 cost-effective.

Additionally, it can be upgraded with new software for new waveforms and new applications

after sale, delivery, and installation.

- 3 -

Figure 1: Use of frequency spectrum

2.3 Need of Cognitive Radio

Globally wireless networks are increasingly facing bandwidth crisis .The spectrum available

has become a scarce resource. Today mobile communications are allowed only certain

frequencies which are getting crowded. As demand for new enhanced services like music,

videos and internet are increasing day by day requirement for bandwidth is far more than

currently available. Hence fundamental problem facing future wireless communication systems

is where to find suitable carrier frequencies and bandwidths to meet the predicted demand of

future services.

However if one scans the radio spectrum, it would be found that

• Some frequencies are unutilized for most of the time

• Some frequencies are partially utilized

• Some frequencies are heavily loaded for most of the time

Thus the available spectrum is inefficiently utilized.

One can identify a range of frequencies dedicated to a particular user, but at a particular time

and place this frequencies are not being utilized. Cognitive radio exploits this to efficiently

utilize the available spectrum. With cognitive radio technology one can use all available

frequency even those dedicated to TV and Satellite. Intelligent devices will negotiate with each

other in order to utilize the whole spectrum available in the most efficient way.

- 4 -

Figure 2: Difference between Cognitive Radio and other radios

2.4 How is Cognitive Radio different from other Radios?

2.5 Physical architecture of the cognitive radio

A generic architecture of a cognitive radio transceiver is shown in figure. 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 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.

The components of a cognitive radio RF front-end are as follows:

1. RF filter: The RF filter selects the desired band by band pass 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.

- 5 -

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 super heterodyne receiver adopts a band pass 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.

Figure 3: Physical Architecture of the Cognitive Radio (a) Cognitive radio transceiver and (b) wideband RF/analog front-end architecture

- 6 -

CHAPTER 3

COGNITIVE RADIO NETWORKS

3.1 Architecture for Cognitive Radio Networks

The components of the cognitive radio network architecture, as shown in figure, 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.

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.

3. Cognitive Radio User: Cognitive radio user has no spectrum license. Hence, the spectrum

access is allowed only in an opportunistic manner.

4. Cognitive Radio Base-Station: Cognitive radio base-station is a fixed infrastructure

component with cognitive radio capabilities.

Figure 4: Cognitive Radio Network Architecture

- 7 -

In 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.

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.

3. Primary Network Access: The cognitive radio user can access the primary base-station

through the licensed band, if the primary network is allowed.

3.2 Dynamic System Allocation

Currently Static allocation model is deployed in which the portion of spectrum for a particular

service is always dedicated. This procedure ensures simplicity, guaranteed access to the

licensee and better quality of services. However deployment of cognitive networks call for a

model in which if the licensee called the primary user is not utilizing the band at a particular

time then the secondary user can opportunistically use the spectrum. This deployment called

Dynamic Spectrum Access would open up vast amount of spectrum.

Figure 5: Dynamic Spectrum Access

- 8 -

CHAPTER 4

FUNCTIONS OF COGNITIVE RADIO

There are four main functions of cognitive radio listed below:

• Spectrum sensing

• Spectrum decision

• Spectrum mobility

• Spectrum sharing

4.1 Spectrum Sensing

A cognitive radio should monitor the available spectrum bands, capture their information, and

then detect the spectrum holes.

However, in reality, RF frontend of CR users cannot differentiate the primary user signals and

CR user signals. In case of the energy detection, 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.

Active Spectrum Sensing Techniques:

To be capable to sense very weak signals, cognitive radios must have significantly better

sensitivity than conventional radios. The goal of the spectrum sensing is to decide between the

two hypotheses, namely

where x(t) is the complex signal received by the cognitive radio,

s(t) is the transmitted signal of the primary user,

n(t) is the additive white Gaussian noise and

h is the complex amplitude gain of the ideal channel.

Figure 6: Periodic Spectrum Sensing Structure

- 9 -

H0 is a null hypothesis, which states that no licensed user is present in a certain spectrum band.

H1 is the alternative hypothesis which indicates that some primary user signal exists.

A cognitive radio should monitor the available spectrum bands, capture their information and

then detect the spectrum holes. Primary user detection is the most efficient way to find the

spectrum holes.

Figure 7: Spectrum Holes or White Spaces

Figure 8: Spectrum Sensing Techniques

- 10 -

4.1.1 Transmitter Detection

Cognitive radios must have the capability to determine if a signal from a primary transmitter is

locally present in a certain spectrum. There are several proposed approaches to transmitter

detection:

o Matched Filter Detection

o Energy Detection

o Cyclostationary Feature Detection

1. MATCHED FILTER DETECTION:

The most important property of a matched filter is that if a signal s(t) is corrupted by additive

white Gaussian noise (AWGN), the filter with an impulse response matched to the signal s(t)

maximizes the output signal-to-noise ratio. In the matched filter, the input y(t) is correlated

with a stored replica of the signal s(t). The output is compared to a threshold in order to make

a decision.

When secondary user has a priori knowledge of primary user signal at both physical and

medium access control (MAC) layers, such as the pulse shape, modulation type and the packet

format, the optimal signal detection method is a matched filter, since it maximizes received

signal-to-noise ratio. The main advantage is that matched filter needs less time to achieve high

processing gain due to coherent detection. A huge drawback in the use of matched filter is that

it would require dedicated sensing receiver for every PU signal type. Digital television (DTV)

band is an example where matched filter detection can be performed.

2. ENERGY DETECTION:

If the secondary receiver cannot gather sufficient information about the PU signal, the optimal

detector is an energy detector, also called as a radiometer. It is a common method for detection

of unknown signals. It measures the energy in the received waveform over an observation time

window. The block diagram of the energy detector is shown in figure 8.

Figure 9: Block Diagram of Energy Detector

- 11 -

First, the input signal y(t) is filtered with a band pass filter (BPF) in order to limit the noise

and to select the bandwidth of interest. The noise in the output of the filter has a band-limited,

flat spectral density. Next, in the figure there is the energy detector consisting of a squaring

device and a finite time integrator. The output signal V from the integrator is

� = 1� �|�()|�

���

Finally, this output signal V is compared to the threshold η in order to decide whether a signal

is present or not. The threshold η is set according to statistical properties of the output V when

only noise is present. The energy detector is also often referred to as a quadratic detector.

3. CYCLOSTATIONARY FEATURE DETECTION:

Cyclostationary processes are random processes for which the signal characteristics are

periodically time-variant. This means that statistical properties such as the mean and

autocorrelation change periodically as functions of time. Many of the signals used in wireless

communication and radar systems possess this property. The idea of the cyclostationary feature

detection is to utilize the built-in periodicity of the modulated signal. Cyclostationary signals

exhibit correlation between widely separated spectral components due to spectral redundancy

caused by periodicity. Cyclostationarity may be caused by modulation or coding, or it may be

also intentionally produced in order to aid channel estimation synchronization. In wireless

communication systems we typically have some knowledge on the waveforms and structural

or statistical properties of the signals that the primary user of the spectrum is using. An

illustration of a cyclostationary feature detector is presented in figure 10. Parameter α is the

cycle frequency.

4.1.2 Cooperative detection

Advantages and challenges: Considering spectrum sensing performed by a single radio,

sensing requirements are set by the worst case channel conditions introduced by multipath,

shadowing and local interference. By allowing multiple radios to share their sensing

measurements it is possible to improve the overall probability of detection through exploiting

Figure 10: Implementation of Cyclostationary Feature Detector

- 12 -

the inherent variability of the channel. Several cognitive radios in various locations will not

experience the worst channel conditions; therefore, the one with good channel conditions can

provide reliable sensing information for the whole network. In the cooperative sensing, all the

SUs send their knowledge about the channel state to an access point or a “master” node. The

node collects the channel state information and makes the final decisions whether PU is present

or not.

Boosting protocol: Different approaches have been suggested for collecting and sharing the

information in cooperative spectrum sensing. A boosting protocol for spectrum pooling system

is suggested in. The boosting protocol consists of two different phases. In the first phase, the

sub-bands that are accessed since the last detection cycle are indicated. In the second phase,

the sub-bands that have become idle since last detection system are signalled. The basic idea

is that the information will be sent by transmitting complex symbols at maximum power level

on the OFDM symbols that want to be pointed out and on the remaining OFDM symbols zeros

will be transmitted. This information will be gathered by an access point (AP) and the

information about actual pool allocation will be distributed among all associated mobile

terminals and those who want to get associated.

4.2 Spectrum Decision

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.

4.3 Spectrum Mobility

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

- 13 -

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

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.

4.4 Spectrum Sharing

Spectrum sharing is the simultaneous usage of a specific radio frequency band in a specific

geographical area by a number of independent entities. 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.

LICENSED SPECTRUM SHARING

• Licensed Band Cognitive Radio is capable of using bands assigned exclusively to a

licensed users, for instance, a specific mobile operator

• Also known as VERTICAL spectrum sharing.

• Higher priority is given to the primary user (license holder) in accessing the spectrum.

UNLICENSED SPECTRUM SHARING

• Unlicensed Band Cognitive Radio can only utilize unlicensed part of the radio frequency

(RF) spectrum. These are prone to interference.

• Also known as HORIZONTAL sharing (all participating systems have equal right in

accessing the spectrum)

• These unprotected bands are highly used because

o Ease of developing innovative technologies to operate in unlicensed bands

o No cost to the consumer of using such bands

- 14 -

Figure 11: Cognitive Radio Operation

- 15 -

CHAPTER 5

APPLICATIONS OF COGNITIVE RADIO

Cognitive radio has the potential to drastically alter the way we would manage our

communication in the future. Some of the promising applications are-

1. Emergency Services Use of Cognitive Radio:

The public safety users have direct application for cognitive radios. In conditions of emergency

need for quality of service and interoperability among various standards becomes vital. CR

with their inherent ability to adapt and adjust to different standards would help in such situation.

2. Low Cost Internet Access:

The dynamic spectrum access model approach would enable broadband access on the unused

spectrums, thereby enabling lower Internet access cost by drastically reducing cost component

associated with purchase of spectrum.

3. Rural connectivity:

By deploying smart mesh CRN systems remote rural areas can be provided connectivity.

4. New services:

Radio based advertising in which the user gets only relevant information

Application in public safety: Emergency situation generally require great deal of coordination

of between different relief workers, fire brigade, police and other concerned person. Chances

of communication breakdown both internally and externally increase due to lack of common

standards and overburden of emergency bands. Cognitive radio would help in such situation

by prioritizing such communication and enabling communication all standards.

Application in security: Imagine a situation in which a soldier has to only turn on the device

which he is carrying. Once engaged the cognitive radio will determine what spectrum to use,

sense any disruptions in the environment (jamming) and adjust accordingly.

Application in daily life: A flight entering into the borders of another country. Currently the

radio parameters need to be set by the pilot with the help of ground controllers as the standards

for communication are different in different countries With cognitive radios no human

involvement would be required.

- 16 -

CHAPTER 6

PRESENT SCENARIO

DISA- US Defense Information Systems Agency

The US Defense Information Systems Agency (DISA) is moving into the age of dynamic

spectrum access (DSA), which is the key near-term contribution of cognitive radio. DISA's

Defense Spectrum Organization (DSO) is the DoD center of excellence for spectrum

management. The key DSO elements are:

• Global Electromagnetic Spectrum Information System Program Management Office

(GEMSIS PMO)

• Joint Spectrum Center (JSC)

Nokia

Nokia a market leader with the aim of improving user experiences for more innovative and

integrated telecommunication has already adapted cognitive radio within allocated spectrum

bands to manage heterogeneous more efficiently. Nokia research center is working extensively

in this field

Cognitive Access to TV Bands

In UK digital switchover is expected to be completed by 2012 that would require the TV

stations to convert from analogue to digital transmission. After the switchover a portion of TV

analogue channels would become vacant which be auctioned off by regulators to other services.

In addition there will be a number of TV channels in a given area that would not be used by

DTV station because such stations would not be able to operate without interference to adjacent

channels. These vacant TV channel care called white spaces. The proposed new rules would,

in principle allow the operation of both fixed and portable broadband devices on a non-

interference basis in this white spaces. A preliminary study by Ofcom indicates that “at any

one location, around 100 MHz on average is not being used by DTT (Digital Terrestrial

Television) and could, in principle be used by license-exempt devices”

- 17 -

Conclusion

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 configurable

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 efficient usage of the wireless medium while simultaneously providing

fairness between competing users and respecting the priority of the primary users.

- 18 -

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] Joe Evans, U. Kansas Gary Minden, U. Kansas Ed Knightly, Rice, Sep 15 2006, “Technical

Document on Cognitive Radio Networks”, IEEE.

[6] Aleksandar Jovicic and Pramod Viswanath, May 8 2006, “Cognitive Radio: An Information

Theoretic Perspective”, IEEE ISIT 2006, Seattle, USA, July 9-14, 2006.

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