background and history of cognitive...

2 Cognitive Radio overview 16

2. Cognitive Radio Overview

2.1. Background and history of Cognitive Radio

Spectrum is a scarce commodity, and considering the spectrum scarcity faced by the

wireless-based service providers led to high congestion levels. The main reason that

leads to inefficient utilization of the radio spectrum is the spectrum licensing system

itself. If the allocated radio spectrum is not used by licensed users, it cannot be

utilized by unlicensed users [M. M. Buddhikot]. Due to this static and rigid allocation,

wireless systems have to work only on a dedicated band of spectrum, and cannot

change the transmission band as changing the environment. For example, if one

channel of spectrum band is heavily used, the wireless system cannot change to work

on another more lightly used band.

The authorized access of the spectrum is usually defined by owner of spectrum

(i.e. licensee), transmit power, frequency, space, type of use, and the license duration.

In general, a license is allocated to one licensee, and the use of spectrum by this

owner must have the specification e.g. maximum power of transmit, base station

location. In the present spectrum-licensing system, the license cannot change the

application or giving the right to another licensee. This restriction causes in low

utilization of the frequency spectrum. A spectrum hole is a band of frequencies

assigned to a licensee, but, at a particular time and particular geographic location, that

user is not utilizing the band [S. Haykin paper].


The distribution of radio spectrum is under control of the central government;

the Federal Communications Commission (FCC) published a report in November

2002, prepared by the Spectrum-Policy Task Force, aimed at improving this precious

resource. The allocation of the unlicensed frequency bands has resulted in the

congestion of these bands. The most of the usable frequency spectrum already has

been assigned for licensed user, resulting in a scarcity of spectrum for new and

up-and-coming wireless applications. To resolve this crisis, regulators and policy

makers are working on new spectrum management strategies. Particularly, the U.S.

Federal Communications Commission (FCC) is tackling the problem in three ways

[Federal Communications Commission, Docket no. 02-135]: spectrum reallocation,

spectrum leases, and spectrum sharing. In spectrum reallocation, bandwidth from

government and other long-standing users is reassigned to new wireless-base services

such as mobile communication, broadband Internet and video distribution. In

spectrum leases, the FCC relaxes the technical and business limitations on existing

spectrum licenses by permitting existing licensees to use their spectrum flexibly for

various services or even lease their spectrum to third parties. Spectrum sharing has

allocation of an unmatched amount of spectrum that could be used for unlicensed or

shared service.

This thesis focuses on spectrum allocation in order to improve the efficiency

of spectrum usage. The FCC is considering a new spectrum-sharing pattern, where

licensed bands are opened to unlicensed operations on a non-interference basis.

Because some licensed bands (such as TV bands) are under-utilized, spectrum sharing

in empty sections of these licensed bands can fill the spectrum shortage problem. This

spectrum-sharing model frequently referred to as dynamic spectrum access (DSA).

Licensed users are referred to primary users (PU), whereas unlicensed users that


access spectrum opportunistically are referred as secondary users (SU). The spectrum

utilization can be improved notably by making it possible for a SU to access a

spectrum hole that unoccupied by the PU. The spectrum holes have been utilized to

promote the efficient use of the spectrum by taking advantage of the existence of

spectrum holes. This concept is known as Cognitive Radio coined by Joe Mitola,

after introducing the note of software radio in 1991, together with Gerald Maguire,

used the term cognitive radio (CR) for the first time in 1999. They exposed a CR as an

improvement of a software radio and state: “Radio protocol is the set of RF bands, air

interfaces, protocols, and spatial and sequential patterns that moderate the use of radio

spectrum. Cognitive radio extends the software radio with radio-domain model-based

reasoning about such protocols.” Mitola introduces radio cognition cycle, explicitly

elaborated in [J. Mitola’s dissertation] as shown in Figure. 2.1.

Figure 2.1 Cognitive cycle .

2.2. Software-defined radios (SDRs)

A software-defined radio (SDR) is a reconfigurable radio in which the transmission

parameters such as modulation mode, frequency band, and protocol may be adapted

dynamically. This adaptability function is obtained by software-controlled signal


processing algorithms. This additional ability has the radio being assembled with a

radio frequency front end, a down converter to an intermediate frequency (IF) or base

band processing, an A/D converter, and then a processor shown in Figure 2.2 as

general structure of SDR transceiver. The capacity of processing limits the complexity

of the signal that can be holds.

Figure 2.2 SDR transceiver

SDR is a main part to implementing CR. The major functions of SDR are as follows [F. K.

Jondral, pp. 275–283,]:

Multiband function: SDR will carry wireless data transmissions over a

different frequency spectrum used by different wireless access systems (e.g.

cellular band, ISM band, TV band).

Multiple standard: SDR will support various standards such as GSM, CDMA,

WiFi, WiMAX.

Multiple services: SDR will be able to work on various types of services such

as cellular telephony or broadband wireless Internet access.

Multi-channel access: SDR will be able to work on multiple frequency bands

simultaneously.

As shown in Figure 2.2, the transmission parameters in a SDR transceiver may be

reconfigured according to the communication requirements and specifications: The

radio parameters such as standard to be operated and frequency band can be set before


the device is delivered to the user. However, the parameters cannot be changed when

the device is configured once. Even though dynamic reconfiguration of the device is

not supported in SDR. The parameters can be seldom reconfigured, when the network

infrastructure modified or added on a connection basis. For example, when a user

wants to start a wireless Internet connection, the transceiver parameters can choose

from the availability of different wireless access networks such as GSM, WiFi or

WiMAX, based on performance and cost. The radio parameters can also be

dynamically changed on a basis of time-slot. For example, the transmission power can

be changed when the level of interference varied. The unlicensed user (SU) can

change the operating band of frequency when the activity of the licensed user (PU) is

detected. This SDR can supports from 800MHz to 5 GHz frequency spectrums.

2.3. Cognitive Radio characteristics and capabilities

A CR includes both a sensing and an adaptation component to the software-defined

radios, provides methods for intelligent spectrum sensing, spectrum management, and

access of spectrum for cognitive radio users (SU). A suitable description is found in

[Hykin paper]: “Cognitive radio is an intelligent wireless communication system that

is aware of its surrounding environment i.e. its outside world, and uses the

methodology of understanding by building to learn from the environment and adapt

its internal states to statistical variations in the incoming radio frequency (RF) stimuli

by making corresponding changes in certain operating parameters e.g. transmit power,

carrier frequency, and modulation strategy in real time. In other words, a CR is an

extended Software defined radio (SDR) that additionally senses its environment,

tracks changes, and possibly reacts upon its findings. A CR network facilitates to set


up communications among CR users/nodes. The wireless communication parameters

can be attuned according to change in the environment, topology, operating or

requirements of user. The two key objectives of CR are: (1) to attain highly reliable

and highly capable wireless communications, and (2) to improve the frequency

spectrum utilization.

2.3.1. Cognitive Radio architecture

The CR protocol architecture is shown in Figure 2.3. In the SDR transceiver are

implemented at physical layer as a RF front-end. The adaptive protocols in the

application, transport, network, and MAC, layers have to be aware of the variations in

the CR environment. In particular, the traffic activity of primary users, the

transmission requirements of SU and changes in channel feature, etc has to consider

by the adaptive protocols. To connect all components, a CR control is used to set up

interfaces with the adaptive protocols, SDR transceiver, and wireless use and services.

This CR component uses intelligent algorithms to process the calculated signal from

the physical layer, and accept the requested information on transmission requirements

from the SU to control the protocol parameters in the different layers [Dynamic

Spectrum Access and Management in Cognitive Radio Networks, EKRAM HOSSAIN].

Physical layer includes carrier frequency, duty cycle, transmit power, digital

modulation mode, processing gain, spectrum bandwidth, channel coding rate and

type, and waveform of the transmitted signal.

MAC layer includes packet size, packet type, channel/time slot allocation, data

rate, retransmission probability, scheduling scheme, and MAC protocol.


A network and transport layer includes network scheduling algorithm in the

routing layer, routing metric and parameters of congestion control (TCP window size)

and rate control parameter (token bucket size rate control) in the transport layer.

Lastly, Application layer includes encryption algorithm and source coding.

Figure 2.3 CR protocol stacks.

2.3.2. Functions of Cognitive Radio

The key functions of CR, to carry efficient and intelligent dynamic spectrum access,

are as follows [I.F. Akyildiz et al.]:

Spectrum sensing: The aim of spectrum sensing is to observe the status of the

spectrum and the movement of the licensed or primary user (PU) by

periodically sensing its frequency band. In this case, a CR transceiver senses a

spectrum hole or idle spectrum (i.e. time, location and band) and also observe

the way of accessing it (i.e. access duration and transmit power) without

interfering with the communication of a PU. Spectrum sensing is two types;

centralized and distributed. In case of centralized spectrum sensing, a base


station or access point act as sensing controller that senses the target frequency

band, and the information as a result obtained is shared with other nodes in the

cognitive radio network. The complexity of user terminals can reduce by

centralized spectrum sensing, since all the sensing functions are carrying out at

the sensing controller. However, centralized spectrum sensing having problem

from location multiplicity. For example, the sensing controller may not be able

to sense a secondary user (SU) at the periphery of the cell. In case of

distributed spectrum sharing, SU carry out spectrum sensing separately, and

the spectrum-sensing outcome can be either used by individual CRs (known as

non-cooperative sensing) or shared with other SUs (as a cooperative sensing).

Even though cooperative sensing acquires a communication and operating

cost, the accuracy of spectrum sensing is superior to that of non-cooperative

sensing.

Spectrum analysis: The SUs use the information obtained from spectrum

sensing to schedule and plan spectrum access. In this case, the transmission

requirements of SUs have to be optimizing the transmission parameters. Main

mechanisms of spectrum management are spectrum analysis and spectrum

access optimization. In spectrum analysis, information from spectrum sensing

is analyzed to get knowledge concerning the spectrum holes such as duration

of availability, interference estimation, and collision probability with a PU due

to sensing error. Then, a decision to access the bandwidth, frequency, transmit

power, modulation mode, time duration and location of spectrum is made by

optimizing the system performance given the preferred objective i.e. maximize

the throughput of the SUs (this thesis focused on these) and maintain the

interference caused to PUs lower the target threshold.


Spectrum access: Before a decision is made on spectrum access based on

spectrum analysis, the unlicensed users access the spectrum holes. Spectrum

access is carries out based on a cognitive medium access control (MAC)

protocol, which proposes to avoid conflict with PUs and also with other SUs.

The CR transmitter is also required to make compromise with the CR receiver

to synchronize the transmission in order that the transmitted data received

successfully. A cognitive MAC protocol either is based on a fixed allocation

MAC (such as FDMA, TDMA, CDMA) or a random access MAC (such as

ALOHA, CSMA/CA) [Dynamic Spectrum Access and Management in Cognitive

Radio, EKRAM HOSSAIN].

Spectrum mobility: Spectrum mobility is a task related to the change of

operating frequency band of SUs. When a PU starts accessing a radio channel,

which is currently being used by an SU, the SU switched to another idle

spectrum band. This change in channel of operating frequency band is referred

to as spectrum handoff. In spectrum handoff, the different layers in the

protocol stacks, the protocol parameters have to be adjusted to match the new

channel of operating frequency band. Spectrum handoff must attempt to make

certain that the communication by the SUs can carry on in the new spectrum

band. This observable fact is most appealing in this thesis.

2.3.4. Dynamic spectrum access (DSA)

SUs exploits the implementation of cognitive radio will be based on DSA. Dynamic

spectrum access can be defined [Hykin’s paper] as a method to fine-tune the spectrum

resource handling in a real time approach in response to the changing objective and


environment (e.g. type of applications and available channel), changes of radio state

(e.g. battery condition, transmission mode, and location), and changes in external

constraints and environment (e.g. operational policy and radio propagation). There are

three main models of DSA, namely, general-use, shared-use, and private-use models.

In the general-use model, the spectrum is open for access to all users. This model is

already been using in the ISM band. In the shared-use model, licensed users (PU) are

allocated the frequency bands, which are opportunistically accessed by the unlicensed

users (SU) when the PU does not occupy them. In the private-use model, a PU agrees

to access of a particular frequency band to a SU for a certain time. This model is more

flexible than the spectrum-licensing model related with traditional command-and-

control, because the type of use and the spectrum licensee is able to changed

dynamically. In opportunistic spectrum access SU can exploit idle in-band sections

without causing interference to the active PUs. Spectrum overlay and spectrum

underlay are the two approaches for opportunistic spectrum access. The spectrum

overlay approach (or opportunistic spectrum access) does not essentially force any

strict constraint on the transmission power by SU. It allows SU to identify and utilize

the spectrum holes defined in space, time and frequency. This approach is well

matched with the existing spectrum allocation thus the PU systems can continue to

work without being affected by the SUs. The spectrum underlay approach limits the

transmission power of SU so that they work below the interference temperature limit

of PU. One possible approach is to transmit the signals in an ultra wide frequency

band (UWB) transmission in order that a high data rate is achieve with very low

transmission power. It is the worst-case hypothesis that the PU transmits all the time.

Thus, probably it does not utilize spectrum holes. However, second approach is out of

scope of this thesis. In the general-use model, dynamic sharing can be between


homogenous networks (IEEE 802.11a uses in the 5 GHz band) or between

heterogeneous networks (coexistence between IEEE 802.11b and 802.15.1 Bluetooth

networks). When all the networks in a heterogeneous environment have cognitive or

adaptive capabilities, it is referred to as symmetric sharing. On the other hand, when

there is one or more network without cognitive/adaptive capabilities (e.g. coexistence

of legacy technology with cognitive radio technology, coexistence of powerful 802.11

networks with low-power 802.15.4 networks) is referred to as asymmetric spectrum

sharing. DSA is divided into two major parts, i.e. spectrum investigation (sensing and

analysis) and spectrum utilization (decide and handoff). Different design techniques

of CR can be used in these parts.

2.3.5. Cognitive Radio Components

The major components of cognitive radio, which functions and adapt the transmission

parameters according to the varying environment [J. Mitola and , T. Christian James

Rieser, Dissertation]. The different components in a CR that apply these functionalities

are shown in Figure 2.4.

Figure 2.4 Components in a node of cognitive radio.


Transmitter and receiver: This SDR-based wireless transceiver is the main

component by means of the functions of signal transmission and reception. In

addition, a wireless receiver is also sense the frequency spectrum used (observe

the activity on the frequency spectrum). The parameters of transceiver in the CR

can be dynamically changed as directed by the upper layer protocols.

Spectrum analyzer: The spectrum analyzer uses measured signals to analyze the

spectrum usage. This is to detect the signal signature from a PU and to locate

spectrum holes for SU to access. The spectrum analyzer should take care that the

transmission of a PU is not interfered with if an SU decides to access the

spectrum. In this case, different signal-processing methods can be used to obtain

spectrum-handling information.

Knowledge extraction/learning: Learning and knowledge extraction use the

information on spectrum usage to understand the behavior of RF environment of

PU. A knowledge base of the spectrum access environment is built and preserve,

which is consequently used to optimize and adjust the transmission parameters to

get the desired objective under different restrictions. Machine learning algorithms

taken from the area of artificial intelligence (AI) can be utilized for learning and

knowledge extraction.

Decision-making: After the knowledge of the spectrum usage is available, the

decision on accessing the spectrum has to be made. There are various techniques

used to achieve a best solution. For example, optimization technique such as GA

and PSO can be applied when the system can be modeled as a single entity with a

multiple objective. Stochastic optimization may be applied when the system states

are random. Some of examples has been given in chapter 4.


2.3.6. Spectrum sensing

The goal of spectrum sensing is to identify the existence of transmissions from PU.

There are three types of spectrum sensing, i.e. non-cooperative sensing, cooperative

sensing and interference-based sensing shown in Figure 2.5. The survey on various

types of spectrum sensing is explored in [Yahya Rahmat-Samii, Dynamic Spectrum

Access and Management in Cognitive Radio Networks by EKRAM HOSSAIN]. Non-

cooperative spectrum sensing is used by a SU to detect the transmitted signal from a

PU by using local observations and local measurements.

Energy detection is the best method for spectrum sensing when the

information from a PU is unavailable [A. Sahai et al.]. In the method of energy

detection, the output signal from a band pass filter is squared and integrated over the

inspection interval. .A decision algorithm compares the integrator output with a

threshold level [J. G. Proakis, Digital Communications] to decide whether a PU exists

or not. In general, the energy detection performance degrades when the SNR

decreases.

Figure 2.5 various types of spectrum sensing in the CR physical layer

The limitations of energy detection are: susceptible to the uncertainty of noise

power and it can only detect the presence of the PU signal but unable differentiate the


type of signal such signals from SUs sharing the same channel with the PU. Thus, the

error of detection would be high in presence of signal sources other than the PU.

Matched filter detection is commonly used to identify a signal by comparing a

reference signal (or pattern) with the input signal. A matched filter will maximize the

received SNR for the calculated signal. Thus, if the signal information (e.g. packet

format or modulation) from a PU is known a matched filter is an optimal detector in

stationary Gaussian noise [A. Sahai et al]. Since a reference signal is used for signal

detection, a matched filter needs only a small amount of time to operate. However, if

this reference signal does not exist or is erroneous, the spectrum sensing performance

degrades significantly. Matched filter detection is suitable when the transmission of a

PU has pilot, preamble, synchronization word or spreading codes, which can be used

to construct the pattern for spectrum sensing.

In cyclostationary feature detection, when the transmitted signal from a PU

usually has a periodic pattern. This periodic pattern is views as cyclostationarity, and

can be used to detect the presence of a PU [Dynamic Spectrum Access and Management

in Cognitive Radio, EKRAM HOSSAIN]. A signal is cyclostationary if the

autocorrelation is a periodic function. The transmitted signal from a PU can be

distinguished from noise with periodic pattern, which is a wide-sense stationary signal

without correlation. In general, cyclostationary detection can provide a more accurate

sensing result and it is robust to variations in noise power. However, the detection is

complex and requires long observation periods to obtain the sensing result. A pattern

recognition scheme based on a artificial intelligence can be used to implement

cyclostationary feature detection for spectrum sensing.


In cooperative sensing, an SU transmitter may not always be able to sense the

signal from a PU transmitter due to its channel fading and geographic separation. For

example, as shown in Figure 2.6, the transmitter and receiver of the SU cannot sense

the signal from the transmitter of the PU because they are out-of-range. This is view

as the hidden node problem. While the transmitter of the SU transmits, it will interfere

with the receiver of the PU. To overcome the hidden node problem in non-cooperative

transmitter sensing, cooperative spectrum sensing may be used. In cooperative

sensing, information of spectrum sensing from multiple SUs are exchanged with each

other to sense the presence of PUs. The cooperative spectrum sensing architecture can

be either centralized or distributed [G. Ganesan and Y. G. Li, pp. 137–143.]. Using

cooperative exchange of spectrum sensing information, the hidden node problem can

be solved and the sensing probability can be appreciably enhanced in a heavily

shadowed environment.

Figure 2.6 Hidden node problem

On the other hand, this acquires a greater communication and computation operating

cost compared with non-cooperative sensing. The scheme of interference-based

sensing has been given by FCC. In this case, the sensing algorithm will measure the

noise and interference level from all sources of signals at the receiver of the PU. This


information is used by an SU to control the spectrum access by computing expected

interference level without violating the interference temperature limit. Alternatively,

an SU transmitter may observe the feedback signal from a SU receiver to gain

knowledge on the interference level. However, spectrum-sensing topic is out of scope

of this research work.

2.3.7. Spectrum analysis and spectrum decision

Spectrum analysis is required for the description of different spectrum bands in terms

of PU activity, operating frequency, interference, bandwidth, and channel capacity. In

the spectrum underlay approach, the interference temperature limit at the PU receiver

and operating frequency, the allowable transmission power at the CR can be

determined. Consequently, the capacity of channel can be estimated. Spectrum

analysis models can be based on either present spectrum sensing (real-time) results or

past spectrum results. The architecture of spectrum analysis can be either non-

cooperative or cooperative. A cooperative architecture, which can be either distributed

or centralized, can get better the accuracy of the spectrum usage model. A cooperative

architecture requires exchanging information between CRs (pay additional operating

cost) and could suffer from the problem of scalability. This is out of scope of the

thesis and assumption made as the result of spectrum analysis has been obtained.

The spectrum decision deals, how to utilize the spectrum holes, in other

words, what power level and modulation to use, how to allocate the spectrum holes

with CRs. This is mainly a medium access control (MAC) problem for a CR. In

addition, spectrum access decisions may require to be communicated between the CR


nodes and the intended receivers. Spectrum decisions may be made based on either a

local or a global optimization principle. In case of local optimization, the spectrum

access decision is made in a way of non-cooperative. In the case of global

optimization, a cooperative spectrum access decision is made either in a distributed or

a centralized way. In a non-cooperative spectrum access approach each CR node is

responsible for its own decision. If the miss-detection probability is large, the access

policy subjected to conservative. If the false alarm probability is large, the access

policy subjected to aggressive. Therefore, the access approach can be jointly

optimized with the sensing approach. A non-cooperative spectrum access strategy has

minimal communication requirements (lower operating cost), but it can result in poor

spectrum exploitation.

In a cooperative centralized approach, a centralized server maintains a

database of availability of spectrum and access information that depends on the

information received from a group of SUs through a dedicated control channel. Thus,

spectrum management is simpler which enables efficient spectrum sharing. In both

the centralized and the distributed approaches, the PU may or may not cooperate.

Once a decision is made to access the spectrum opportunities, various issues related to

radio link control and resource management have to be determined. These consist of

pulse shaping, choice of the number of spectrum bands to access, transmission power

control and the set of suitable bands, adaptive modulation and coding etc.


2.3.8. Applications of Cognitive Radio

CR concepts can be applied to different types of wireless communications, a few of

which are explained below:

Next generation wireless networks: CR is expected to be a core technology for

next generation heterogeneous wireless networks. This will provide intelligence to

both the user-side and service-side equipments to manage the network and air

interface efficiently. At the user-side, mobile equipment with multiple air

interfaces (e.g. cellular, WiFi and WiMAX) can observe the condition of the

wireless access networks (e.g. transmission quality, delay, throughput, and

congestion) and make a decision on selecting the access network to connect

according to cost. At the service-side, radio resource from multiple networks can

be optimized for the given set of mobile users and their QoS requirements. Based

on the mobility and traffic outline of the users, efficient load balancing systems

can be implemented at the infrastructure of service provider to allocate the traffic

load among multiple available networks to reduce network congestion [I.F.

Akyildiz at al.].

Coexistence of different wireless technologies: New wireless technologies (e.g.

IEEE 802.22-based WRANs [33]) are being developed to reuse the radio spectrum

allocated to other wireless services such as TV service. CR is a solution to provide

coexistence between these different technologies and wireless services. IEEE

802.22 based WRAN users can opportunistically use the TV band when a TV

station is not broadcasting. Spectrum sensing and spectrum management will be


critical mechanism for IEEE 802.22 standard WRAN technology to avoid

interference to TV users and to maximize throughput for the WRAN users.

eHealth services: Different types of wireless technologies are implemented in

healthcare services to improve efficiency of the healthcare and patient care

management. However, using wireless communication devices in healthcare

application are limited by electromagnetic compatibility (EMC) and

electromagnetic interference (EMI) requirements. Since the bio-signal sensors and

medical equipments are sensitive to EMI, the transmit power of the wireless

devices has to be cautiously controlled. Also, different biomedical devices (e.g.

surgical equipment, monitoring and diagnostic devices) use RF transmission. The

spectrum handling of these devices has to be carefully selected to avoid

interference with each other. The CR concepts can be applied for many wireless

medical sensors that are designed to operate in the ISM (industrial, scientific, and

medical) band, which can implement CR concepts to choose suitable transmission

bands to avoid interference [J. Mitola dissertation].

Intelligent transportation system: Intelligent transportation systems (ITS) widely

use different wireless access technologies to improve the safety and efficiency of

transportation by vehicles. Two different types of communications take place in

ITS system – vehicle-to-vehicle (V2V) communication and vehicle-to-roadside

(V2R) communication. In V2V communications, a special form of ad hoc

network, i.e. a vehicular ad hoc network is formed among vehicles to exchange

safety-related information. In V2R communications, information is exchanged

between the roadside unit and the onboard unit in a vehicle. High speed of the

vehicles and quick variations in network topologies cause significant challenges to

efficient V2V and V2R communications. CR concepts can be used in both


onboard units and roadside units so that they can adapt their transmissions to deal

with the rapid variations in the ambient RF environment [J. F. Hauris, pp. 427–

431.]. With multiple-radio abilities at the onboard units, they must be able to

adaptively opt the radio to communicate with the roadside units.

Emergency networks: Emergency and public safety networks can take advantages

of the CR concepts to provide flexible and reliable wireless communication. In the

case of disaster situation, the infrastructure of standard communication may

possibly not exist, and then, a CR network as an emergency network may require

to be established to carry disaster recovery. Such a network may use the CR

concept to make possible wireless transmission and reception over a wide range of

the radio spectrum.

Military networks: With CR, the parameters wireless communication can be

dynamically adapted based on the location and time as well as the mission of the

militaries. Suppose, if some frequencies are noisy or jammed, the CR transceiver

can search for and access another frequency bands for communication.

Furthermore, location-aware CR can manage the transmitted waveform in a

particular area to keep away from interference to the high priority military

communication systems.

2.4. Machine learning

Machine learning is one of the branches in artificial intelligence. This deals with the

design and development of learning algorithms by means of test data or past

occurrence to optimize the performance of a system given definite objectives and

limitations. Machine learning exploits the hypothesis of mathematics and statistics to


build inference models from test data so that algorithms may be considered based on

these models. Two main steps in machine learning are training and making decision.

In the training step, test data or past knowledge are used to make knowledge about the

system or the environment. In this step, able algorithms are essential to take out useful

information from unprocessed data. Once the knowledge it makes, a decision is made

based on the available knowledge and present state and input data. Machine learning

techniques can be implemented to solve problems correlated to pattern recognition,

natural language processing, and robotics [Dynamic Spectrum Access and

Management in Cognitive Radio Networks, EKRAM HOSSAIN].

2.5. Cognitive Radio scheme based on Artificial intelligence

Artificial intelligence (AI) techniques designs learning and decision-making process

that can be implemented in knowledgeable CR systems [Dynamic Spectrum Access and

Management in Cognitive Radio Networks, EKRAM HOSSAIN and C. Clancy et al. pp. 47–

52]. One example has been demonstrated in [C. Clancy et al. pp. 47–52], the idea of

machine learning useful to maximization of capacity and DSA for SU. The proposed

system architecture is shown in Figure 2.7. Here, the knowledge base maintains the

conditions of the system and the accessible actions [P. Jackson].

Figure 2.7 Cognitive radio architecture with machine learning.


The reasoning engine employs the knowledge base to select the optimal action. The

learning engine carries knowledge exploitation based on the observed information

(e.g. channel error rate and information on channel availability). In the knowledge

base, two data structures i.e. inference rule and action are defined. The inference rule

is used to represent the environmental state. According to this condition, an action can

be executed to change the condition so that the system objectives can be achieved. For

a example, inference rule can be defined as “SNR equal to 5dB and modulation equal

to QPSK ”, whereas the action defined as “decrease the modulation mode” with

condition “SNR less than and equal to 8dB” and post-condition “modulation equal to

BPSK”. The input, which is obtained from measurement, the reasoning engine

matches the existing state (modulation and SNR in this case) with the predicates and

find out the predicate results either true or false. Then, from the predicate results set, a

suitable action is taken. In this example, if the present SNR = = 5 dB and present

modulation = = QPSK, the precondition will be true and the predicate will be active.

As a result, the CR engine will choose to reduce the modulation mode. Here, the

modulation will be changed to BPSK, as declared in the post condition. A learning

algorithm is used to update the condition of the system as well as the available actions

according to the environment of radio. This update can be made using an objective

function (e.g. minimize the BER) with an objective to find out the optimal action

given the input (quality of channel) and the knowledge availability. Various learning

algorithms can be used in a CR network (hidden Markov model, expert system

[P. Jackson], neural network [S. Haykin, Neural Networks], genetic algorithm, particle

swarm optimization, DNA inspired algorithm). The two main components in the

architecture as shown in Figure 2.7 are the action and the reasoning. The action

component is used to observe the input from the environment and the condition of the


system, and to take the suitable decisions for the CR. The reasoning part stores the

knowledge and the rules. This is used by the reasoning engine to obtain the optimal

decision according to the defined objective. The full details of the realization of CR

based on the base-10 GA, PSO, DNA inspired algorithm used in this thesis, along

with the all parameter settings are explored in chapter 3,4 and 5.

2.6. Designing Cognitive Radio based on location-aware

Geo-location is an important CR-enabling technology because the large range of

usage that may result from a radio being aware of its present location and probably

being aware of its intended path and target. The global positioning system (GPS) is a

satellite-based system that utilizes the time difference of arrival (TDoA) to locate a

receiver [Cognitive Radio Technology by Bruce Fette]. GPS receivers typically consist of

a one-pulse-per-second signal as it appears at each radio from each source of satellite,

resulting in a computing of propagation delay from each source in spite of position. In

the nonexistence of GPS signals, triangulation method can be used to locate a radio

from non-cooperative or even cooperative emitters. Other approaches are time of

arrival (ToA), angle of arrival (AoA) and Received Signal Strength (RSS) explored in

[Cognitive Radio Technology by Bruce Fette, Wireless communications by Anderea

Goldsmith]. In the case of RSS, if the transmit power on a signal is precisely known,

the patterns of the antenna radiation gains are known accurately, and the receiver is

capable to measure receive signal strength accurately, then a propagation model may

be used to compute the distance to the transmitter and receiver as a function of RSS.

But propagation channels are varying dynamically, thus this approach is challenging.

This location finding approach is analogous to the ToA approach. If a process of


correlation based on a PU transmitter’s database, an RSS-based receiver application

can find out in which regulatory area it is located. For example, if a CR is receiving

particular TV channels and particular AM and FM stations all at the same time, it may

conclude its city location. If the location of the transmitters is built-in the database

along with levels of transmission, the RSS process might improve this computation

due to the fairly large number of measurements. The quality of RSS-based location

estimates is somewhat low. It is helpful to CRs for a few applications but not for

others.

There is an alternative method like radio frequency fingerprinting, which is

widely explained in the literature as a technique for identification for transmitter. The

received signal is extremely site specific because of its dependence on the terrine and

intervening obstacles. So the multipath structure of the channel is unique to every

location and can be considered as a fingerprint or signature of the location if same RF

signal is transmitted from location [Wireless communications by Anderea Goldsmith,

O. Leon et al.]. This property has been exploited in system to develop a “signature

database” of a location grid in specific service areas. The received signal is measured

as a CR moves along this grid and recorded in signature database. When another CR

moves in the same area, the signal received from it compared with the entry in the

database, thus its location is determined. Such a scheme may also be useful for indoor

application where the multipath structure in an area can be exploited. This work

exploits this principle for detection of legitimate PU by SUs in order to prevent

adversary attacks (denial of service (Dos)). The full details of the implementation of

the fingerprint method used in this research, along with the all parameters are

presented in chapter 6.


2.7. Summary

Due to the instruction-based approach used in traditional spectrum licensing, the radio

spectrum cannot be efficiently exploited. Thus, a new spectrum-licensing idea is

being developed that will improve the flexibility of spectrum access. This flexibility

will be obtained through the use of CR implemented as SDRs. In CR, a wireless

system can change the transmission parameters dynamically according to the

environmental change. A cognitive radio transceiver should have the ability to

observe, orient, plan, decide and act to improve the performance of wireless network.

With this capability, SUs can utilize the frequency spectrum that unused by PU.

However, an SU have to guarantee that the interference caused to the PU due to its

communication remains under the limit of interference temperature. This spectrum

sensing can be doing either in a cooperative or a non-cooperative manner. In the

cooperative spectrum sensing, multiple SUs cooperate by exchanging sensing results

among each other. In case of non-cooperative spectrum sensing, each SU senses the

radio spectrum separately. A number of approaches in designing CR based on AI have

been discussed. Also, several approaches for CR location awareness schemes are

explained.

background and history of cognitive...

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