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Advances in Cognitive Radio Networks: A Survey

March 2011
IEEE Journal of Selected Topics in Signal Processing 5(1):5 - 23

March 2011
5(1):5 - 23

DOI:10.1109/JSTSP.2010.2093210

Source
IEEE Xplore

Authors:

Beibei Wang

University of Maryland, College Park

K. J. Ray Liu

University of Maryland, College Park

With the rapid deployment of new wireless devices and applications, the last decade has witnessed a growing demand for wireless radio spectrum. However, the fixed spectrum assignment policy becomes a bottleneck for more efficient spectrum utilization, under which a great portion of the licensed spectrum is severely under-utilized. The inefficient usage of the limited spectrum resources urges the spectrum regulatory bodies to review their policy and start to seek for innovative communication technology that can exploit the wireless spectrum in a more intelligent and flexible way. The concept of cognitive radio is proposed to address the issue of spectrum efficiency and has been receiving an increasing attention in recent years, since it equips wireless users the capability to optimally adapt their operating parameters according to the interactions with the surrounding radio environment. There have been many significant developments in the past few years on cognitive radios. This paper surveys recent advances in research related to cognitive radios. The fundamentals of cognitive radio technology, architecture of a cognitive radio network and its applications are first introduced. The existing works in spectrum sensing are reviewed, and important issues in dynamic spectrum allocation and sharing are investigated in detail.

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Summary of main spectrum sensing techniques.

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, VOL. 5, NO. 1, FEBRUARY 2011 5

Advances in Cognitive Radio Networks: A Survey

Beibei Wang and K. J. Ray Liu

Abstract—With the rapid deployment of new wireless devices

and applications, the last decade has witnessed a growing demand

for wireless radio spectrum. However, the ﬁxed spectrum assign-

ment policy becomes a bottleneck for more efﬁcient spectrum uti-

lization, under which a great portion of the licensed spectrum is

severely under-utilized. The inefﬁcient usage of the limited spec-

trum resources urges the spectrum regulatory bodies to review

their policy and start to seek for innovative communication tech-

nology that can exploit the wireless spectrum in a more intelligent

and ﬂexible way. The concept of cognitive radio is proposed to ad-

dress the issue of spectrum efﬁciency and has been receiving an in-

creasing attention in recent years, since it equips wireless users the

capability to optimally adapt their operating parameters according

to the interactions with the surrounding radio environment. There

have been many signiﬁcant developments in the past few years on

cognitive radios. This paper surveys recent advances in research

related to cognitive radios. The fundamentals of cognitive radio

technology, architecture of a cognitive radio network and its appli-

cations are ﬁrst introduced. The existing works in spectrum sensing

are reviewed, and important issues in dynamic spectrum allocation

and sharing are investigated in detail.

Index Terms—Cognitive radio (CR), platforms and standards,

radio spectrum management, software radio, spectrum sensing,

wireless communication.

I. INTRODUCTION

THE usage of radio spectrum resources and the regulation

of radio emissions are coordinated by national regulatory

bodies like the Federal Communications Commission (FCC).

The FCC assigns spectrum to licensed holders, also known as

primary users, on a long-term basis for large geographical re-

gions. However, a large portion of the assigned spectrum re-

mains under utilized as illustrated in Fig. 1. The inefﬁcient usage

of the limited spectrum necessitates the development of dy-

namic spectrum access techniques, where users who have no

spectrum licenses, also known as secondary users, are allowed

to use the temporarily unused licensed spectrum. In recent years,

the FCC has been considering more ﬂexible and comprehensive

uses of the available spectrum [1], through the use of cognitive

radio technology [2].

Cognitive radio is the key enabling technology that enables

next generation communication networks, also known as dy-

Manuscript received October 30, 2009 accepted October 24, 2010. Date of

publication November 18, 2010; date of current version January 19, 2011. The

associate editor coordinating the review of this manuscript and approving it for

publication was Dr. Sastri Kota.

B. Wang is with Corporate Research and Development, Qualcomm, Inc., San

Diego, CA 92121 USA (e-mail: beibeiw@qualcomm.com).

K. J. R. Liu is with the Department of Electrical and Computer Engi-

neering, University of Maryland, College Park, MD 20742 USA (e-mail:

bebewang@umd.edu; kjrliu@umd.edu).

Color versions of one or more of the ﬁgures in this paper are available online

at http://ieeexplore.ieee.org.

Digital Object Identiﬁer 10.1109/JSTSP.2010.2093210

Fig. 1. Spectrum usage [5].

namic spectrum access (DSA) networks, to utilize the spec-

trum more efﬁciently in an opportunistic fashion without inter-

fering with the primary users. It is deﬁned as a radio that can

change its transmitter parameters according to the interactions

with the environment in which it operates [3]. It differs from

conventional radio devices in that a cognitive radio can equip

users with cognitive capability and reconﬁgurability [4], [5].

Cognitive capability refers to the ability to sense and gather in-

formation from the surrounding environment, such as informa-

tion about transmission frequency, bandwidth, power, modula-

tion, etc. With this capability, secondary users can identify the

best available spectrum. Reconﬁgurability refers to the ability

to rapidly adapt the operational parameters according to the

sensed information in order to achieve the optimal performance.

By exploiting the spectrum in an opportunistic fashion, cogni-

tive radio enables secondary users to sense which portion of the

spectrum are available, select the best available channel, coor-

dinate spectrum access with other users, and vacate the channel

when a primary user reclaims the spectrum usage right.

Considering the more ﬂexible and comprehensive use of the

spectrum resources, especially when secondary users coexist

with primary users, traditional spectrum allocation schemes [6]

and spectrum access protocols may no longer be applicable.

New spectrum management approaches need to be developed

to solve new challenges in research related to cognitive radio,

speciﬁcally, in spectrum sensing and dynamic spectrum sharing.

As primary users have priority in using the spectrum, when

secondary users coexist with primary users, they have to per-

form real-time wideband monitoring of the licensed spectrum

to be used. When secondary users are allowed to transmit data

simultaneously with a primary user, interference temperature

limit should not be violated [7]. If secondary users are only al-

lowed to transmit when the primary users are not using the spec-

trum, they need to be aware of the primary users’ reappearance

through various detection techniques, such as energy detection,

feature detection, matched ﬁltering and coherent detection. Due

to noise uncertainty, shadowing, and multipath effect, detection

performance of single user sensing is pretty limited. Coopera-

tive sensing has been considered effective in improving detec-

6 IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, VOL. 5, NO. 1, FEBRUARY 2011

tion accuracy by taking advantage of the spatial and multi-user

diversity. In cooperative spectrum sensing, how to select proper

users for sensing, how to fuse individual user’s decision and ex-

change information, and how to perform distributed spectrum

sensing are issues worth studying.

In order to fully utilize the spectrum resources, efﬁcient dy-

namic spectrum allocation and sharing schemes are very im-

portant. Novel spectrum access control protocols and control

channel management should be designed to accommodate the

dynamic spectrum environment while avoid collision with a pri-

mary user. When a primary user re-appears in a licensed band,

a good spectrum handoff mechanism is required to provide sec-

ondary users with smooth frequency transition with low latency.

In multi-hop cognitive wireless networks, intermediate cogni-

tive nodes should intelligently support relaying information and

routing through using a set of dynamically changing channels.

In order to manage the interference to the primary users and the

mutual interference among themselves, secondary users’ trans-

mission power should be carefully controlled, and their compe-

tition for the spectrum resources should also be addressed.

There have been many signiﬁcant developments in the past

few years on cognitive radios. This article surveys recent ad-

vances in research related to cognitive radios. In Section II, we

overview the fundamentals of cognitive radio technology, ar-

chitecture of a cognitive radio network and its applications. In

Section III, we review existing works in spectrum sensing, in-

cluding interference temperature, different types of detection

techniques, and cooperative spectrum sensing. In Section IV,

we discuss several important issues in dynamic spectrum allo-

cation and sharing.

II. FUNDAMENTALS

A. Cognitive Radio Characteristics

The dramatic increase of service quality and channel capacity

in wireless networks is severely limited by the scarcity of en-

ergy and bandwidth, which are the two fundamental resources

for communications. Therefore, researchers are currently fo-

cusing their attention on new communications and networking

paradigms that can intelligently and efﬁciently utilize these

scarce resources. Cognitive radio (CR) is one critical enabling

technology for future communications and networking that

can utilize the limited network resources in a more efﬁcient

and ﬂexible way. It differs from traditional communication

paradigms in that the radios/devices can adapt their operating

parameters, such as transmission power, frequency, modulation

type, etc., to the variations of the surrounding radio environment

[3]. Before CRs adjust their operating mode to environment

variations, they must ﬁrst gain necessary information from the

radio environment. This kind of characteristics is referred to as

cognitive capability [4], which enables CR devices to be aware

of the transmitted waveform, radio frequency (RF) spectrum,

communication network type/protocol, geographical infor-

mation, locally available resources and services, user needs,

security policy, and so on. After CR devices gather their needed

information from the radio environment, they can dynamically

change their transmission parameters according to the sensed

Fig. 2. Cognitive cycle.

Fig. 3. Illustration of spectrum white space [5].

environment variations and achieve optimal performance,

which is referred to as reconﬁgurability [4].

B. Cognitive Radio Functions

A typical duty cycle of CR, as illustrated in Fig. 2, includes

detecting spectrum white space, selecting the best frequency

bands, coordinating spectrum access with other users and va-

cating the frequency when a primary user appears. Such a cog-

nitive cycle is supported by the following functions:

• spectrum sensing and analysis;

• spectrum management and handoff;

• spectrum allocation and sharing.

Through spectrum sensing and analysis, CR can detect the

spectrum white space (see Fig. 3), i.e., a portion of frequency

band that is not being used by the primary users, and utilize the

spectrum. On the other hand, when primary users start using the

licensed spectrum again, CR can detect their activity through

sensing, so that no harmful interference is generated due to sec-

ondary users’ transmission.

After recognizing the spectrum white space by sensing,

spectrum management and handoff function of CR enables

secondary users to choose the best frequency band and hop

among multiple bands according to the time varying channel

characteristics to meet various Quality of Service (QoS) re-

quirements [5]. For instance, when a primary user reclaims

his/her frequency band, the secondary user that is using the

licensed band can direct his/her transmission to other available

frequencies, according to the channel capacity determined by

the noise and interference levels, path loss, channel error rate,

holding time, and etc.

In dynamic spectrum access, a secondary user may share

the spectrum resources with primary users, other secondary

users, or both. Hence, a good spectrum allocation and sharing

WANG AND LIU: ADVANCES IN COGNITIVE RADIO NETWORKS: A SURVEY 7

Fig. 4. Network architecture of dynamic spectrum sharing.

mechanism is critical to achieve high spectrum efﬁciency. Since

primary users own the spectrum rights, when secondary users

co-exist in a licensed band with primary users, the interference

level due to secondary spectrum usage should be limited by

a certain threshold. When multiple secondary users share a

frequency band, their access should be coordinated to alleviate

collisions and interference.

C. Network Architecture and Applications

With the development of CR technologies, secondary users

who are not authorized with spectrum usage rights can utilize

the temporally unused licensed bands owned by the primary

users. Therefore, in a CR network architecture, the components

include both a secondary network and a primary network, as

shown in Fig. 4.

A secondary network refers to a network composed of a

set of secondary users with/without a secondary base station.

Secondary users can only access the licensed spectrum when it

is not occupied by a primary user. The opportunistic spectrum

access of secondary users is usually coordinated by a secondary

base station, which is a ﬁxed infrastructure component serving

as a hub of the secondary network. Both secondary users and

secondary base stations are equipped with CR functions. If

several secondary networks share one common spectrum band,

their spectrum usage may be coordinated by a central network

entity, called spectrum broker [8]. The spectrum broker col-

lects operation information from each secondary network, and

allocates the network resources to achieve efﬁcient and fair

spectrum sharing.

A primary network is composed of a set of primary users and

one or more primary base stations. Primary users are authorized

to use certain licensed spectrum bands under the coordination of

primary base stations. Their transmission should not be inter-

fered by secondary networks. Primary users and primary base

stations are in general not equipped with CR functions. There-

fore, if a secondary network share a licensed spectrum band with

a primary network, besides detecting the spectrum white space

and utilizing the best spectrum band, the secondary network is

required to immediately detect the presence of a primary user

and direct the secondary transmission to another available band

so as to avoid interfering with primary transmission.

Because CRs are able to sense, detect, and monitor the sur-

rounding RF environment such as interference and access avail-

ability, and reconﬁgure their own operating characteristics to

best match outside situations, cognitive communications can in-

crease spectrum efﬁciency and support higher bandwidth ser-

vice. Moreover, the capability of real-time autonomous deci-

sions for efﬁcient spectrum sharing also reduces the burdens of

centralized spectrum management. As a result, CRs can be em-

ployed in many applications.

8 IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, VOL. 5, NO. 1, FEBRUARY 2011

First, the capacity of military communications is limited by

radio spectrum scarcity because static frequency assignments

freeze bandwidth into unproductive applications, where a large

amount of spectrum is idle. CR using dynamic spectrum access

can alleviate the spectrum congestion through efﬁcient alloca-

tion of bandwidth and ﬂexible spectrum access [2]. Therefore,

CR can provide military with adaptive, seamless, and secure

communications.

Moreover, a CR network can also be implemented to en-

hance public safety and homeland security. A natural disaster or

terrorist attack can destroy existing communication infrastruc-

ture, so an emergency network becomes indispensable to aid the

search and rescue. As a CR can recognize spectrum availability

and reconﬁgure itself for much more efﬁcient communication,

this provides public safety personnel with dynamic spectrum

selectivity and reliable broadband communication to minimize

information delay. Moreover, CR can facilitate interoperability

between various communication systems. Through adapting to

the requirements and conditions of another network, the CR de-

vices can support multiple service types, such as voice, data,

video, and etc.

Another very promising application of CR is in the com-

mercial markets for wireless technologies. Since CR can intel-

ligently determine which communication channels are in use

and automatically switches to an unoccupied channel, it pro-

vides additional bandwidth and versatility for rapidly growing

data applications. Moreover, the adaptive and dynamic channel

switching can help avoid spectrum conﬂict and expensive re-

deployment. As CR can utilize a wide range of frequencies,

some of which has excellent propagation characteristics, CR

devices are less susceptible to fading related to growing fo-

liage, buildings, terrain and weather. When frequency changes

are needed due to conﬂict or interference, the CR frequency

management software will change the operating frequency au-

tomatically even without human intervention. Additionally, the

radio software can change the service bandwidth remotely to ac-

commodate new applications. As long as no end-user hardware

needs to be updated, product upgrades or conﬁguration changes

can be completed simply by downloading newly released radio

management software. Thus, CR is viewed as the key enabling

technology for future mobile wireless services anywhere, any-

time and with any device.

III. SPECTRUM SENSING AND ANALYSIS

Through spectrum sensing, CR can obtain necessary obser-

vations about its surrounding radio environment, such as the

presence of primary users and appearance of spectrum holes.

Only with this information can CR adapt its transmitting and

receiving parameters, like transmission power, frequency, mod-

ulation schemes, and etc., in order to achieve efﬁcient spec-

trum utilization. Therefore, spectrum sensing and analysis is

the ﬁrst critical step towards dynamic spectrum management. In

this section, we will discuss three different aspects of spectrum

sensing. First is the interference temperature model, which mea-

sures the interference level observed at a receiver and is used to

protect licensed primary users from harmful interference due to

unlicensed secondary users. Then we will talk about the spec-

trum hole detection to determine additional available spectrum

resources and compare several detection techniques. Finally, we

will discuss cooperative sensing with multiple users’ help.

A. Interference Temperature

In opportunistic spectrum access, secondary users need to de-

tect primary users’ appearance and decide which portion of the

spectrum is available. Such a decision can be made according to

different metrics. Traditional approach is tolimit the transmitter

power of interfering devices, i.e., the transmitted power should

be no more than a prescribed noise ﬂoor at a certain distance

from the transmitter. However, due to the increased mobility

and variability of radio frequency (RF) emitters, constraining

the transmitter power becomes more problematic, since unpre-

dictable new source of interference may appear. To address this

issue, FCC Spectrum Policy Task Force [9] has proposed a new

metric on interference assessment, the interference temperature,

to enforce an interference limit perceived by receivers. The in-

terference temperature is a measure of the RF power available

at a receiving antenna to be delivered to a receiver, reﬂecting the

power generated by other emitters and noise sources [10]. More

speciﬁcally, it is deﬁned as the temperature equivalent to the RF

power available at a receiving antenna per unit bandwidth [11],

i.e.,

(1)

where is the average interference power in Watts cen-

tered at , covering bandwidth measured in Hertz, and Boltz-

mann’s constant is 1.38 Joules per degree Kelvin.

With the concept of interference temperature, FCC further

established an interference temperature limit, which provides

a maximum amount of tolerable interference for a given fre-

quency band at a particular location. Any unlicensed secondary

transmitter using this band must guarantee that their transmis-

sion plus the existing noise and interference must not exceed the

interference temperature limit at a licensed receiver.

Since any transmission in the licensed band is viewed to be

harmful if it would increase the noise ﬂoor above the interfer-

ence temperature limit, it is necessary that the receiver have a re-

liable spectral estimate of the interference temperature. This re-

quirement can be fulﬁlled by using the multitaper method to es-

timate the power spectrum of the interference temperature with

a large number of sensors [4]. The multitaper method can solve

the tradeoff between bias and variance of an estimator and pro-

vide a near-optimal estimation performance. The large number

of sensors can account for the spatial variation of the RF energy

from one location to another. Subspace-based method has also

been proposed to gain knowledge of the quality and usage of a

spectrum band [12], where information about the interference

temperature is obtained by eigenvalue decomposition.

If a regulatory body sets an interference temperature limit

for a particular frequency band with bandwidth , then the sec-

ondary transmitters has to keep the average interference below

. Therefore, the interference temperature serves as a cap

placed on potential RF energy that could appear on that band,

and there are some previous efforts about how to implement

efﬁcient spectrum allocation with the interference temperature

limit.

WANG AND LIU: ADVANCES IN COGNITIVE RADIO NETWORKS: A SURVEY 9

Fig. 5. Summary of main spectrum sensing techniques.

Spectrum shaping has been proposed to improve spectrum

efﬁciency [13] in CR networks. More speciﬁcally, using inter-

ference ﬁtting, a CR senses the shape of the interference power

spectrum and create spectra inversely shaped to the current in-

terference environment to take advantage of gaps between the

noise ﬂoor and the cap of the interference temperature limit.

Interference temperature dynamics in a CR network were

investigated in [14] using a hidden Markov model (HMM),

where the trained HMM can be used as a sequence generator

for secondary nodes to predict the interference temperature of

the channel in the future and aid their channel selection for

transmission.

A comprehensive analysis has been presented in [15], which

quantiﬁes how interference temperature limits should be se-

lected and how those choices affect the range of licensed sig-

nals. It is shown that the capacity achieved is a simple function

of the number of nodes, the average bandwidth, and the frac-

tional impact to the primary signal’s coverage area. However, as

observed by [15], the achievable capacity from the interference

temperature model is low, compared to the amount of interfer-

ence it can cause to primary users.1

B. Spectrum Sensing

Spectrum sensing enables the capability of a CR to measure,

learn, and be aware of the radio’s operating environment, such

as the spectrum availability and interference status. When a cer-

tain frequency band is detected as not being used by the primary

licensed user of the band at a particular time in a particular posi-

tion, secondary users can utilize the spectrum, i.e., there exists a

spectrum opportunity. Therefore, spectrum sensing can be per-

formed in the time, frequency, and spatial domains. With the

1It is also argued by other commenting parties of the FCC that the interference

temperature approach is not a workable concept and would result in increased

interference in the frequency bands where it would be used. Therefore, in May

2007 the FCC terminated the proceedings of rule making implementing the in-

terference temperature model.

recent development of beamforming technology, multiple users

can utilize the same channel/frequency at the same time in the

same geographical location. Thus, if a primary user does not

transmit in all the directions, extra spectrum opportunities can

be created for secondary users in the directions where the pri-

mary user is not operating, and spectrum sensing needs also to

take the angle of arrivals into account [16]. Primary users can

also use their assigned bands by means of spread spectrum or

frequency hopping, and then secondary users can transmit in

the same band simultaneously without severely interfering with

primary users as long as they adopt an orthogonal code with re-

spect to the codes adopted by primary users [17]. This creates

spectrum opportunities in code domain, but meanwhile requires

detection of the codes used by primary users as well as multi-

path parameters.

A wealth of literature on spectrum sensing focuses on pri-

mary transmitter detection based on the local measurements of

secondary users, since detecting the primary users that are re-

ceiving data is in general very difﬁcult. According to the a priori

information they require and the resulting complexity and ac-

curacy, spectrum sensing techniques can be categorized in the

following types, which are summarized in Fig. 5.

1) Energy Detector: Energy detection is the most common

type of spectrum sensing because it is easy to implement and

requires no prior knowledge about the primary signal.

Assume the hypothesis model of the received signal is

(2)

where is the primary user’s signal to be detected at the local

receiver of a secondary user, is the additive white Gaussian

noise, and is the channel gain from the primary user’s trans-

mitter to the secondary user’s receiver. is a null hypothesis,

meaning there is no primary user present in the band, while

means the primary user’s presence. The detection statistics of

10 IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, VOL. 5, NO. 1, FEBRUARY 2011

the energy detector can be deﬁned as the average (or total) en-

ergy of observed samples

(3)

The decision on whether the spectrum is being occupied by the

primary user is made by comparing the detection statistics

with a predetermined threshold . The performance of the de-

tector is characterized by two probabilities: the probability of

false alarm and the probability of detection . denotes

the probability that the hypothesis test decides while it is

actually , i.e.,

(4)

denotes the probability that the test correctly decides ,

i.e.,

(5)

A good detector should ensure a high detection probability

and a low false alarm , or it should optimize the spec-

trum usage efﬁciency (e.g., QoS of a secondary user network)

while guaranteeing a certain level of primary user protection. To

this end, various approaches have been proposed to improve the

efﬁciency of energy detector based spectrum sensing.

Since the detection performance is very sensitive to the noise

power estimate error [18], an adaptive noise level estimation

approach is proposed in [19], where multiple signal classiﬁ-

cation algorithm is used to decouple the noise and signal sub-

spaces and estimate the noise ﬂoor. A constant false alarm rate

threshold is further computed to study the spectrum occupancy

and its statistics. A well chosen detection threshold can min-

imize spectrum sensing error, provide the primary user with

enough protection, and fully enhance spectrum utilization. In

[20], the detection threshold is optimized iteratively to satisfy

the requirement on false alarm probability. Threshold optimiza-

tion subject to spectrum sensing constraints is investigated in

[21], where an optimal adaptive threshold level is developed by

utilizing the spectrum sensing error function. In [22], forward

methods for energy detection are proposed, where the noise

power is unknown and is adaptively estimated. In order to ﬁnd

and localize narrowband signals, a localization algorithm based

on double-thresholding (LAD) is proposed in [23], where the

usage of two thresholds can provide signal separation and lo-

calization. The LAD method is a blind narrowband signal de-

tection, and no information about the noise level nor narrow-

band signals are required. The LAD method with normalized

thresholds can reduce computational complexity without per-

formance loss, and the estimation of the number of narrowband

signals becomes more accurately by adjacent cluster combining.

The sensing throughput tradeoff of energy detection is studied

in [24], where the sensing period duration in a time slot is opti-

mized to maximize the achievable throughput for the secondary

users under the constraint that the primary users are sufﬁciently

protected. A novel wideband spectrum sensing technique based

on energy detection is introduced in [25], which jointly de-

tects the signal energy levels over multiple frequency bands in

order to improve the opportunistic throughput of CRs and re-

duce their interference to the primary systems. The analysis in

[26] shows that detection of narrowband transmission using en-

ergy detection over multi-band orthogonal frequency-division

multiplexing (OFDM) is feasible, and can be further extended

to cover more complex systems.

Besides its low computational and implementation com-

plexity and short detection time, there also exist some chal-

lenges in designing a good energy detector. First, the detection

threshold depends on the noise power, which may change over

time and hence is difﬁcult to measure precisely in real time.

In low signal-to-noise ratio (SNR) regimes where the noise

power is very high, reliable identiﬁcation of a primary user is

even impossible [27]. Moreover, an energy detector can only

decide the primary user’s presence by comparing the received

signal energy with a threshold; thus, it cannot differentiate the

primary user from other unknown signal sources. As such, it

can trigger false alarm frequently.

2) Feature Detector: There are speciﬁc features associated

with the information transmission of a primary user. For in-

stance, the statistics of the transmitted signals in many com-

munication paradigms are periodic because of the inherent pe-

riodicities such as the modulation rate, carrier frequency, etc.

Such features are usually viewed as the cyclostationary features,

based on which a detector can distinguish cyclostationary sig-

nals from stationary noise. In a more general sense, features can

refer to any intrinsic characteristics associated with a primary

user’s transmission, as well as the cyclostationary features. For

example, center frequencies and bandwidths [28] extracted from

energy detection can also be used as reference features for clas-

siﬁcation and determining a primary user’s presence. In this sec-

tion, we will introduce the cyclostationary feature detection fol-

lowed by a generalized feature detection.

Cyclostationary feature detection was ﬁrst introduced in

[29]. As in most communication systems, the transmitted

signals are modulated signals coupled with sine wave carriers,

pulse trains, hopping sequences, or cyclic preﬁxes, while the

additive noise is generally wide-sense stationary (WSS) with

no correlation, cyclostationary feature detectors can be utilized

to differentiate noise from primary users’ signal [30]–[32] and

distinguish among different types of transmissions and primary

systems [33].

Different from an energy detector which uses time-domain

signal energy as test statistics, a cyclostationary feature detector

performs a transformation from the time-domain into the fre-

quency feature domain and then conducts a hypothesis test in

the new domain. Speciﬁcally, deﬁne the cyclic autocorrelation

function (CAF) of the received signal by

(6)

where is the expectation operation, denotes complex con-

jugation, and is the cyclic frequency. Since periodicity is a

common property of wireless modulated signals, while noise is

WSS, the CAF of the received signal also demonstrates peri-

odicity when the primary signal is present. Thus, we can repre-

sent the CAF using its Fourier series expansion, called the cyclic

spectrum density (CSD) function, expressed as [29]

(7)

WANG AND LIU: ADVANCES IN COGNITIVE RADIO NETWORKS: A SURVEY 11

The CSD function have peaks when the cyclic frequency

equals to the fundamental frequencies of the transmitted signal

, i.e., with being the period of . Under

hypothesis , the CSD function does not have any peaks since

the noise is non-cyclostationary signals. A peak detector [34]

or a generalized likelihood ratio test [31], [33] can be further

used to distinguish among the two hypothesis. Different primary

communication systems using different air interfaces (modula-

tion, multiplexing, coding, etc.) can also be differentiated by

their different properties of cyclostationarity.

However, when OFDM becomes the air interface as sug-

gested by several wireless communication standards, identiﬁ-

cation of different systems may become problematic, since the

features due to the nature of OFDM signaling are likely to be

close or even identical. To address this issue, particular features

need to be introduced to OFDM-based communications. In

[35], methods that induce different properties of cyclostation-

arity to different systems are considered. The OFDM signal is

conﬁgured before transmission so that its CAF outputs peaks

at certain pre-chosen cycle frequencies, and the difference in

these frequencies is used to distinguish among several systems

under the same OFDM air interface. A similar approach is

considered in [36].

Compared to energy detectors that are prone to high false

alarm probability due to noise uncertainty and cannot detect

weak signals in noise, cyclostationary detectors become good

alternatives because they can differentiate noise from primary

users’ signal and have better detection robustness in low SNR

regime. A spectrum sensing method based on maximum cyclic

autocorrelation selection has been proposed in [37], where the

peak and non-peak values of the cyclic autocorrelation func-

tion are compared to determine whether the primary signal is

present or not. This method does not require noise variance es-

timation, and is robust against noise uncertainty and interference

signals. Frequency-selective fading and uncertain noise impair

the robustness of cyclostationary signal detection in low SNR

environments. Run time noise calibration has been considered

in [27] and [38], in order to improve detector robustness. The

method exploits the in-band measurements at frequencies where

a pilot is absent to calibrate the noise statistics at the pilot fre-

quencies. By combining neural network for signal classiﬁcation

with cyclic spectral analysis, a more efﬁcient and reliable clas-

siﬁer is developed in [39]. Since a large amount of processing

is performed ofﬂine using neural networks, the online computa-

tion for signal classiﬁcation is greatly reduced.

Generalized feature detection refers to detection and classiﬁ-

cation that extracts more feature information other than the cy-

clostationarity due to the modulated primary signals, such as the

transmission technologies used by a primary user, the amount

of energy and its distribution across different frequencies [40],

[41], channel bandwidth and its shape [28], [42], power spec-

trum density [43], center frequency [28], idle guard interval of

OFDM [44], FFT-type feature [45], etc. By matching the fea-

tures extracted from the received signal to the a priori informa-

tion about primary users’ transmission characteristics, primary

users can be identiﬁed.

Location information of the primary signal is also an impor-

tant feature that can be used to distinguish a primary user from

other signal sources. Under primary user emulation attack, a

malicious secondary user transmits signals whose characteris-

tics emulate those of the primary signals. A transmitter veriﬁca-

tion scheme is proposed in [46] to secure trustworthy spectrum

sensing based on the location veriﬁcation of the primary user.

3) Matched Filtering and Coherent Detection: If secondary

users know information about a primary user’ signal a priori,

then the optimal detection method is the matched ﬁltering [47],

since a matched ﬁlter can correlate the already known primary

signal with the received signal to detect the presence of the pri-

mary user and thus maximize the SNR in the presence of addi-

tive stochastic noise. The merit of matched ﬁltering is the short

time it requires to achieve a certain detection performance such

as a low probability of missed detection and false alarm [48],

since a matched ﬁlter needs less received signal samples. How-

ever, the required number of signal samples also grows as the

received SNR decreases, so there exists a SNR wall [27] for a

matched ﬁlter. In addition, its implementation complexity and

power consumption is too high [49], because the matched ﬁlter

needs receivers for all types of signals and corresponding re-

ceiver algorithms to be executed.

Matched ﬁltering requires perfect knowledge of the primary

user’s signal, such as the operating frequency, bandwidth, mod-

ulation type and order, pulse shape, packet format, etc. If wrong

information is used for matched ﬁltering, the detection perfor-

mance will be degraded a lot. On the other hand, most wireless

communication systems exhibit certain patterns, such as pilot

tones, preambles, midambles, spreading codes, and etc., which

are used to assist control, equalization, synchronization, conti-

nuity, or reference purposes. Even though perfect information

of a primary user’s signal may not be attainable, if a certain

pattern is known from the received signals, coherent detection

(a.k.a. waveform-based sensing) can be used to decide whether

a primary user is transmitting or not [50]. As an example, the

procedure of coherent detection using pilot pattern is explained

as follows [50].

There are two hypothesis in the coherent detection:

(8)

where is a known pilot tone, is the fraction of energy

allocated to the pilot tone, is the desired signals assumed

to be orthogonal to the pilot tone, and is additive white

noise. The test statistics of the coherent detection is deﬁned as

the projected received signal in the pilot direction, i.e.,

(9)

with is a normalized unit vector in the direction of the pilot

tone. As increases, the test statistics under hypothesis

is much greater than that under . By comparing with a

pre-determined detection threshold, one can decide the presence

of a primary user.

Coherent detection can also be performed in frequency do-

main [43]. One can express the binary hypothesis test using the

12 IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, VOL. 5, NO. 1, FEBRUARY 2011

power spectrum density of the received signal , and dis-

tinguish between and by exploiting the unique spectral

signature exhibited in .

Coherent detection is shown to be robust to noise uncertainty,

and not limited by the SNR wall [50] as is large enough.

Moreover, coherent detection outperforms energy detection in

the sensing convergence time [51], [52], because the sensing

time of energy detection increases quadratically with the SNR

reduction, while that of coherent detection only increases lin-

early [52]. However, information about the waveform patterns

is a prerequisite for implementing coherent detection; the more

precise information a coherent detector has, the better the

sensing performance will be.

4) Other Techniques: There are several other spectrum

sensing techniques proposed in recent literature, and some of

them are variations inspired by the above-mentioned sensing

techniques.

Statistical Covariance-Based Sensing: Since the statistical

covariance matrices of the received signal and noise are gener-

ally different, the difference is used in [53] and [54] to differen-

tiate the desired signal component from background noise. The

eigenvalues of the covariance matrix of the received signal can

also be used for primary detection [55]. Based on random ma-

trix theory [56], the ratio of the maximum eigenvalue to the min-

imum eigenvalue is quantized, and the detection threshold can

be found among them. From the simulation on detecting digital

TV signals, these methods based on statistical covariances are

shown to be more robust to noise uncertainty while requiring no

a priori information of the signal, the channel, and noise power.

Filter-Based Sensing: Application of a speciﬁc class of ﬁlter

banks is proposed in [57] for spectrum sensing in CR systems.

When ﬁlter banks are used for multicarrier communications in

CR networks, the spectrum sensing can be performed by only

measuring the signal power at the outputs of subcarrier channels

with virtually no computational cost. The multitaper method [4]

can also be thought as a ﬁlter bank spectrum estimation with

multiple ﬁlter banks.

Fast Sensing: By utilizing the theory of quickest detection,

which performs a statistical test to detect the change of distribu-

tion in spectrum usage observations as quickly as possible, an

agile and robust spectrum sensing is achieved in [58]. The un-

known parameters after a primary user appears can be estimated

using the proposed successive reﬁnement, which combines both

generalized likelihood ratio and parallel cumulative sum tests.

An efﬁcient sensing-sequence is developed in [59] to reduce the

delay due to spectrum opportunity discovery. The probability

that a frequency band is available at sensing, the sensing dura-

tion and the channel capacity are three factors that determine the

sensing sequence.

Learning/Reasoning-Based Sensing: An approach based

on reinforcement learning for the detection of spectral resources

in a multi-band CR scenario is investigated in [60], where the

optimal detection strategy is obtained by solving a Markov

decision process (MDP). A medium access control layer spec-

trum sensing algorithm using knowledge-based reasoning is

proposed in [61], where the optimal range of channels to ﬁnely

sense is determined through proactive fast sensing and channel

quality information.

Measurements-Based Sensing and Modeling: By col-

lecting data over a long period of time at many base stations,

[62] provides a unique analysis of cellular primary usage. The

collected data is dissected along different dimensions to charac-

terize the primary usage. With the aid of spectrum observatory,

[63] extends short-term spectrum usage measurements to study

the spectrum usage trend over long periods, observes spectrum

usage patterns, and detects the positions of spectrum white

space in time and spatial domains. Such information can be

greatly helpful in developing good dynamic access protocols

and governing secondary systems.

C. Cooperative Sensing

The performance of spectrum sensing is limited by noise un-

certainty, shadowing, and multi-path fading effect. When the re-

ceived primary SNR is too low, there exists a SNR wall, below

which reliable spectrum detection is impossible even with a very

long sensing time. If secondary users cannot detect the primary

transmitter, while the primary receiver is within the secondary

users’ transmission range, a hidden primary user problem will

occur, and the primary user’s transmission will be interfered.

By taking advantage of the independent fading channels

(i.e., spatial diversity) and multiuser diversity, cooperative

spectrum sensing is proposed to improve the reliability of

spectrum sensing, increase the detection probability to better

protect a primary user, and reduce false alarm to utilize the idle

spectrum more efﬁciently. In centralized cooperative spectrum

sensing, a central controller, e.g., a secondary base station,

collects local observations from multiple secondary users,

decides the available spectrum channels using some decision

fusion rule, and informs the secondary users which channels to

access. In distributed cooperative spectrum sensing, secondary

users exchange their local detection results among themselves

without requiring a backbone infrastructure with reduced cost.

Relays can also be used in cooperative spectrum sensing, such

as the cooperative sensing scheme proposed in [64], where

the cognitive users operating in the same band help each other

relay information using amplify-and-forward protocol. It is

shown that the inherent network asymmetry can be exploited to

increase the agility.

There also exist several challenges on cooperative spectrum

sensing. For instance, secondary users can be low-cost devices

only equipped with a limit amount of power, so they can not

afford very complicated detection hardware and high compu-

tational complexity. In wideband cooperative sensing, multiple

secondary users have to scan a wide range of spectrum channels

and share their detection results. This results in a large amount

of sensory data exchange, high energy consumption, and an in-

efﬁcient data throughput. If the spectrum environment is highly

dynamic, the sensed information may even be stale due to user

mobility, channel fading, etc.

1) User Selection: Due to secondary users’ different loca-

tions and channel conditions, it is shown in [65] that cooperating

all secondary users in spectrum sensing is not optimal, and the

optimum detection/false alarm probability is achieved by only

cooperating a group of users who have higher SNR of the re-

ceived primary signal.

WANG AND LIU: ADVANCES IN COGNITIVE RADIO NETWORKS: A SURVEY 13

Since detecting a primary user costs battery power of sec-

ondary users, and shadow fading may be correlated for nearby

secondary users, an optimal selection of secondary users for co-

operative spectrum sensing is desirable. In [66], different al-

gorithms based on different amount of available information

are proposed to select a proper set of sensors that experience

uncorrelated shadow fading. A joint spatial-temporal sensing

scheme for CR networks is proposed in [67], where secondary

users collaboratively estimate the location and transmit power

of the primary transmitter to determine their maximum allow-

able transmission power, and use the location information to de-

cide which users should participate in collaborative sensing in

order to minimize correlation among the secondary users. Per-

formance evaluation of cooperative spectrum sensing over real-

istic propagation environments, i.e., correlated log-normal shad-

owing in both sensing and reporting channels, is investigated in

[68]. This work also provides guidelines to select the optimal

number of users in order to guarantee a certain detecting perfor-

mance in a practical radio environment.

In a CR sensor network, individual sensor nodes may ex-

perience heterogeneous false alarm and detection probability

due to their different locations, making it harder to determine

the optimal number of cooperative nodes. Sensor clustering is

proposed in [69], where the optimal cluster size is derived so

as to upper-bound the variation of the average received signal

strength in a cluster of sensor nodes. Moreover, sensor density is

optimized so that average distance between neighboring nodes

is lower-bounded and their measurements are nearly indepen-

dent without much correlation.

If a secondary user can not distinguish between the transmis-

sions of a primary user and another secondary user, he will lose

the opportunity to use the spectrum. It is shown in [50] that

the presence/absence of possible interference from other sec-

ondary users is the main reason of the uncertainty in primary

user detection, and coordinating nearby secondary users can

greatly reduce the noise uncertainty due to shadowing, fading,

and multi-path effect. A good degree of coordination should be

chosen based on the channel coherent times, bandwidths, and

the complexity of the detectors.

2) Decision Fusion: Different decision fusion rules for co-

operative spectrum sensing have been studied in the literature.

Logic OR rule is used [70] for combining multiple users’ deci-

sions for spectrum sensing in fading environments. Cooperative

spectrum sensing using counting rule is studied in [71], where

sensing errors are minimized by choosing the optimal settings

for both matched ﬁltering and energy detection. It is shown in

[72] that half-voting rule is the optimal decision fusion rule in

cooperative sensing based on energy detection. Light-weight co-

operation based on hard decisions is proposed [73] for coop-

erative sensing to alleviate the sensitivity requirements on in-

dividual users. A liner-quadratic strategy is developed [74] to

combat the detriment effects of correlation between different

secondary users.

A good way to optimally combine the received primary signal

samples in space and time is to maximize the SNR of local en-

ergy detectors. However, optimal combination requires infor-

mation of the signal and channel. Blindly combined energy de-

tection is proposed in [75], without requiring such information

and noise power estimation, while performing much better than

energy detector and more robust to noise uncertainty. Hard deci-

sion combining with the logic AND rule and soft decision using

the likelihood ratio test are proposed in [76] in collaborative de-

tection of TV transmissions. It is shown that soft decision com-

bining for spectrum sensing yields more precise detection than

hard decision combining. Soft decision combination for cooper-

ative sensing based on energy detection is investigated in [77],

and maximal ratio combination is proved to be near optimal in

low SNR region and reduce the SNR wall.

In general, cooperative sensing is coordinated over a sepa-

rate control channel, so a good cooperation schemes should be

able to use a small bandwidth and power for exchanging local

detection results while maximizing the detection reliability. An

efﬁcient linear cooperation framework for spectrum sensing is

proposed in [78], where the global decision is a linear combina-

tion of the local statistics collected from individual nodes using

energy detection. Compared to the likelihood ratio test, the pro-

posed method has lower computational complexity, closed-form

expressions of detection and false alarm probabilities, and com-

parable detection performance.

Performance of cooperative spectrum sensing depends on the

correctness of the local sensing data reported by the secondary

users. If malicious users enter a legitimate secondary network

and compromise the secondary users, false detection results will

be reported to the fusion center, and this kind of attack is called

spectrum sensing data falsiﬁcation (SSDF) attack [79]. In order

to guarantee satisfying detection performance under the SSDF

attack, a weighted sequential probability ratio test (WSPRT) is

proposed in [79], which incorporates a reputation-based mecha-

nism to the sequential probability ratio test. If a secondary user’s

local detection result is identical to the ﬁnal result after decision

fusion, his/her reports will carry more weight in future decision

fusion. The proposed WSPRT approach is more robust against

the SSDF attack than commonly adopted decision fusion rules,

such as AND, OR, and majority rules [80].

3) Efﬁcient Information Sharing: In order to coordinate the

cooperation in spectrum sensing, a lot of information exchange

is needed among secondary users, such as their locations, esti-

mation of the primary user’s location and power, which users

should be clustered into a group, which users should perform

cooperative sensing at a particular time epoch, and so on. Such

a large amount of information exchange brings a lot of overhead

to the secondary users, which necessitates an efﬁcient informa-

tion sharing among the secondary users.

GUESS protocol, an incremental gossiping approach is

proposed in [81], to coordinate the dissemination of spectrum

sensing results with reduced overhead. In order to reduce the

bandwidth required by a large number of secondary users

for reporting their sensing results, a censoring method with

quantization is proposed in [82]. Only users with reliable infor-

mation will send their local observations, i.e., one bit decision

0 or 1, to the common receiver. A pipelined spectrum sensing

framework is proposed in [83], where spectrum sensing is

conducted concurrently with when secondary users are sending

their detection reports. The proposed method alleviates sensing

overhead by making use of the reporting time, provides more

time for spectrum sensing, and thus improves the detection

performance.

14 IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, VOL. 5, NO. 1, FEBRUARY 2011

Fig. 6. Classiﬁcation of spectrum allocation and sharing schemes.

4) Distributed Cooperative Sensing: Cooperative spectrum

sensing has been shown to be able to greatly improve the sensing

performance in CR networks. However, if cognitive users be-

long to different service providers, they tend to contribute less

in sensing in order to increase their own data throughput. A

distributed cooperating spectrum sensing scheme based on

evolutionary game theory is proposed in [84] to answer the

question of “how to collaborate” in multiuser de-centralized

CR networks. Using replicator dynamics, the evolutionary

game modeling provides an excellent means to address the

strategic uncertainty that a user may face by exploring different

actions, adaptively learning during the strategic interactions,

and approaching the best response strategy under changing

conditions and environments. The behavior dynamics and the

optimal cooperation strategy of the secondary users are charac-

terized. A distributed learning algorithm is further developed

so that the secondary users approach the optimal strategy solely

based on their own payoff observations. The proposed game is

demonstrated to achieve a higher system throughput than the

fully cooperative scenario, where all users contribute to sensing

in every time slot.

IV. DYNAMIC SPECTRUM ALLOCATION AND SHARING

In the previous section, we have discussed various detection

techniques and how to perform efﬁcient cooperative spectrum

sensing in order to obtain an accurate estimation of the inter-

ference temperature and spectrum occupancy status. With the

detection results, secondary users will have an idea on which

spectrum bands he/she could use. However, the availability and

quality of a spectrum band may change rapidly with time due to

primary users’ activity and competition from other secondary

users. In order to utilize the spectrum resources efﬁciently, sec-

ondary users need to be able to address issues such as when

and how to use a spectrum band, how to co-exist with primary

users and other secondary users, and which spectrum band they

should sense and access if the current one in use is not available.

Therefore, in this section, we will review the existing spectrum

allocation and sharing approaches that answer these questions.

Before going into details, we would like to ﬁrst brieﬂy discuss

the classiﬁcation of the current spectrum allocation and sharing

schemes. According to different criteria, existing spectrum allo-

cation and sharing schemes can be classiﬁed in different types,

as summarized in Fig. 6.

The ﬁrst classiﬁcation is according to the spectrum bands that

secondary users are using. Spectrum sharing among the sec-

ondary users who access the unlicensed spectrum band is re-

ferred to as open spectrum sharing. One example is the open

spectrum sharing in the unlicensed industrial, scientiﬁc, and

medical band. In open spectrum sharing, since no users own

spectrum licenses, they all have the same rights in using the unli-

censed spectrum. Spectrum sharing among the secondary users

and primary users in licensed spectrum bands is referred to as hi-

erarchical access model [85] or licensed spectrum sharing. Pri-

mary users, usually not equipped with CR, do not need to per-

form dynamic/opportunistic spectrum access, since they have

priority in using the spectrum band. Whenever they reclaim the

spectrum usage, secondary users have to adjust their operating

parameters, such as power, frequency, and bandwidth, to avoid

interrupting the primary users.

Considering the access technology of the secondary users, li-

censed spectrum sharing can be further divided in two categories

[5], [85].

1) Spectrum underlay: In spectrum underlay secondary users

are allowed to transmit their data in the licensed spec-

trum band when primary users are also transmitting. The

interference temperature model is imposed on secondary

users’ transmission power so that the interference at a

primary user’s receiver is within the interference temper-

ature limit and primary users can deliver their packet to

the receiver successfully. Spread spectrum techniques are

usually adopted by secondary users to fully utilize the

wide range of spectrum. However, due to the constraints

on transmission power, secondary users can only achieve

short-range communication. If primary users transmit data

all the time in a constant mode, spectrum underlay does

not require secondary users to perform spectrum detection

to ﬁnd available spectrum band.

2) Spectrum overlay: Spectrum overlay is also referred to as

opportunistic spectrum access. Unlike spectrum underlay,

secondary users in spectrum overlay will only use the li-

censed spectrum when primary users are not transmitting,

so there is no interference temperature limit imposed on

secondary users’ transmission. Instead, secondary users

need to sense the licensed frequency band and detect the

spectrum white space, in order to avoid harmful interfer-

ence to primary users.

The second classiﬁcation is according to the network archi-

tecture [5]. When there exists a central entity that controls and

coordinates the spectrum allocation and access of secondary

WANG AND LIU: ADVANCES IN COGNITIVE RADIO NETWORKS: A SURVEY 15

users, then the spectrum allocation is centralized. If there is no

such a central controller, perhaps because of the high cost of

constructing an infrastructure or the ad-hoc nature of the net-

work such as for emergency or military use, that kind of spec-

trum sharing belongs to distributed spectrum sharing. In dis-

tributed spectrum sharing, each user makes his/her own decision

about his/her spectrum access strategy, mainly based on local

observation of the spectrum dynamics.

The third classiﬁcation is according to the access behavior

of secondary users [5]. If all secondary users work towards a

common goal, for instance they belong to the same operator or

service provider, they will coordinate their allocation and access

in order to maximize their social welfare. This is called coop-

erative spectrum sharing. Most centralized spectrum allocation

can be considered as cooperative. On the other hand, it is not

always the case that all secondary users belong to the same ser-

vice provider, such as those who access the open spectrum band.

Different users have different objectives, and hence they only

aim at maximizing their own beneﬁt from using the spectrum

resources. Since users are no longer cooperative in achieving

the same objective, this kind of spectrum sharing is a noncoop-

erative one, and secondary users are selﬁsh in that they pursue

their own beneﬁt.

In order to give the readers more insight on how to design

efﬁcient spectrum allocation and sharing schemes, we next dis-

cuss several important issues in dynamic spectrum allocation

and sharing.

A. Medium Access Control in Cognitive Radio Networks

Medium access control (MAC) refers to the policy that con-

trols how a secondary user should access a licensed spectrum

band. Various medium access control protocol have been pro-

posed in wireless networking such as carrier sense multiple ac-

cess and slotted ALOHA. Due to the new features of CR net-

works, such as the collision avoidance with a primary user and

dynamics in spectrum availability, new medium access proto-

cols need to be designed to address the new challenges in CR

networks.

A cognitive medium access protocol with stochastic mod-

eling is proposed in [86], which enhances the coexistence of

CR with WLAN systems based on sensing and prediction. A

primary-prioritized Markov approach for dynamic spectrum ac-

cess is proposed in [87], which models the interactions between

the primary users and the secondary users as continuous-time

Markov chains. By designing appropriate access probabilities

for the secondary users, a good tradeoff can be achievedbetween

spectrum efﬁciency and fairness. A cognitive MAC (C-MAC)

protocol for distributed multi-channel wireless networks is in-

troduced in [88]. Since the C-MAC operates in multiple chan-

nels, it is able to deal with the dynamics of channel availability

due to primary users’ activity. A stochastic channel selection al-

gorithm based on learning automata is proposed in [89], which

dynamically adapts the probability of access one channel in real

time. It is shown that the probability of successful transmissions

is maximized using the proposed selection algorithm.

A MultiMAC protocol that can dynamically reconﬁgure

MAC and physical layer properties based on per-node and

per-ﬂow statistics is proposed in [90]. Considering the lim-

ited capability of spectrum sensing and limited bandwidth,

a hardware-constrained cognitive MAC is proposed in [91],

which optimizes the spectrum sensing decision by formulating

sensing as an optimal stopping problem.

Secondary users also need to be aware of their surrounding

environment in allocating and accessing the spectrum. Consid-

ering that each node’s spectrum usage is unpredictable and un-

stable, the work in [92] proposes to integrate interference-aware

statistical admission control with stability-oriented spectrum al-

location. The nodes’ spectrum demand is regulated to allow ef-

ﬁcient statistical multiplexing while the outage is minimized.

Since secondary users operating in different frequency bands

at different locations are constrained by different interference

requirements, a distance-dependent MAC protocol is proposed

[93] to optimize the CR network throughput subject to a power

mask constraint to protect the primary user. The idea of how to

utilize location awareness to facilitate spectrum sharing between

secondary and primary users is illustrated in [94]. An aggre-

gation-aware spectrum assignment scheme is proposed in [95]

to optimize the spectrum assignment when the available spec-

trum band is not contiguous. Collision probability and overlap-

ping time are introduced in [96] to evaluate the protection of

a primary user. Different spectrum access schemes using dif-

ferent sensing, back-off, and transmission mechanism are con-

sidered, which reveal the impact of several important design cri-

teria, such as sensing, packet length distribution, back-off time,

packet overhead, and grouping.

B. Spectrum Handoff

When the current channel conditions become worse, or

the primary user appears and reclaims his assigned channel,

secondary users need to stop transmitting data and ﬁnd other

available channels to resume their transmission. This kind of

handoff in CR networks is termed as spectrum handoff [5].

Since the transmissions of secondary users are suspended

during a spectrum handoff, they will experience longer packet

delay. Therefore, a good spectrum handoff mechanism should

provide with secondary users with smooth frequency shift with

the least latency.

A good way to alleviate the performance degradation due

to long delay is to reserve a certain number of channels for

potential spectrum handoff [97]. When secondary users need

to switch to another frequency, they can immediately pick

one channel from the reserved bands. However, if a secondary

user reserves too much bandwidth for spectrum handoff, the

throughput may be unnecessarily low, because the primary user

may not reclaims his licensed band very frequently. Therefore,

there is a tradeoff in optimizing the channel reservation. By

optimizing the number of channels reserved for spectrum

handoff, the blocking probability can be minimized and the

secondary users’ throughput is maximized. A location-assisted

handover algorithm is proposed in [98], where the secondary

users equipped with the location estimation and sensing devices

can report their locations back to the secondary base station.

Whenever a handoff becomes a must, secondary users can

switch their frequency to one of the candidate channels de-

pending on their locations. A joint spectrum handoff scheduling

and routing protocol in multi-hop multi-radio CR networks is

proposed in [99], which can minimize the total handoff latency

under the constraint on network connectivity. The protocol

16 IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, VOL. 5, NO. 1, FEBRUARY 2011

extends the spectrum handoff of a single link to that of multiple

links.

In order to achieve a reliable continuous communication

among secondary users in the presence of random reclaims

from a primary user, secondary users should select their chan-

nels from different licensed bands owned by different primary

users [100], [101]. The multi-band spectrum diversity helps

to reduce the impact of the appearance of a primary user and

improve the reliability of secondary spectrum access.

C. Cognitive Relaying

Utilizing the broadcasting nature of wireless networks, co-

operative relaying is proposed in recent years [102] to improve

the network performance through spatial and multi-user diver-

sity. Combined with CR technology, cooperative relaying can

offer more signiﬁcant performance gain, because cognitive relay

nodes can forward a source node’s data by using the spectrum

white space they have detected.

A cognitive multiple access strategy in the presence of a

cooperating relay is proposed in [103]. Since the cognitive relay

only forwards data when the source is not transmitting, no extra

channel resources are allocated for cooperation at the relay,

and hence the proposed protocols provide signiﬁcant perfor-

mance gains over conventional relaying strategies. A frequency

sharing multi-hop CR network is studied in [104]. By recog-

nizing the radio environment in each relay node, the system can

autonomously avoid the transmission in an interference area. In

[105], an infrastructure-based secondary network architecture

is proposed to leverage relay-assisted discontiguous OFDM for

data transmission. A relay nodes which can bridge the source

and the destination using its common channels between the

two nodes will be selected, and relay selection and spectrum

allocation is jointly optimized.

D. Spectrum Sensing and Access

Due to energy and hardware constraints, a secondary user

may not be able to sense the entire spectrum space and can only

access a limited number of channels from those it has sensed.

To optimize spectrum access while considering physical layer

spectrum sensing and primary user’s trafﬁc statistics, a deci-

sion-theoretic approach based on partially observable Markov

decision process (POMDP) is proposed in [106], which can op-

timize secondary users’ performance, accommodate spectrum

sensing error, and protect primary users from harmful interfer-

ence. A separation principle [107] reveals the optimality of my-

opic policies for the spectrum sensor design and access strategy,

and reduces the complexity of the POMDP formulation by de-

coupling the design of sensing strategy from the design of the

access strategy. An extension of [106] is presented in [108]

which incorporates the secondary user’s residual energy and

buffer state in the POMDP formulation for spectrum sensing

and access.

Cognitive medium access has also been modeled as a multi-

armed bandit problem in [109], and an efﬁcient access strategy

is developed that achieves a good balance between exploring the

availability of other free bands and exploiting the opportunities

that have been identiﬁed. Multi-cognitive user scenario is also

considered, which is modeled as a game.

E. Power Control in a CR Network

In order to manage the interference among secondary users,

or avoid harmful interference to primary users due to secondary

spectrum usage, various power control schemes are considered

in CR networks to coordinate spectrum sharing.

Power control in opportunistic spectrum access (OSA) is

studied in [110], which models the packet transmission from

source to destination in OSA as crossing a multi-lane highway.

If a secondary user tries to use high transmission power to

reach the destination in one hop, it has to wait until the primary

user is inactive; on the other hand, it can take more advantage

of the spectrum opportunities with lower transmission while

relying on the intermediate users on the path to destination. The

impact of transmission power on the occurrence of spectrum

opportunities is investigated in [110], and it is shown that

the optimal transmission power of secondary users decreases

monotonically with the trafﬁc load of the primary network. Dy-

namic programming has been used in designing optimal power

and rate control strategy, in order to maximize the long-term

average rate for a secondary user [111]. An opportunistic

power control strategy is proposed in [112], which enables

the cognitive user to maximize its transmission rate while

guaranteeing that the outage probability of the primary user is

not degraded. A collaborative spectrum sensing scheme that

considers signal strength, localization and collaboration in the

presence of multiple co-channel primary and secondary trans-

mitters is proposed in [113]. The allowed maximum transmitter

power of a secondary user in a given channel is determined,

using a distributed database containing co-channel transmitter

information including location, error estimates, power, etc.

Conﬂict graph is commonly adopted to describe the interfer-

ence constraints among users, where the node in a graph rep-

resents a user, and an edge between a pair of nodes represents

the existence of interference. A systematic framework to pro-

duce conﬂict graphs based on physical interference model is

presented in [114], which characterizes the cumulative effect

of interference while making it possible to use graph theory to

solve spectrum allocation problems under physical interference

constraints.

F. Control Channel Management

The majority of the DSA systems use a dedicated global con-

trol channel to coordinate the spectrum allocation. However,

this assumption is not realistic in opportunistic spectrum access

since there may be no permanent channel available for sec-

ondary users. A distributed group coordination solution is pro-

posed in [115], where a common control channel is only re-

quired locally by the neighbor nodes sharing common channels.

A cluster based approach is presented in [116], where a dynamic

one-hop cluster is formed by users sharing common channels

and the spectrum is managed by cluster heads. A distributed

swarm intelligence based control channel assignment scheme is

proposed in [117], which selects local common control channels

among a local group of secondary users according to the quality

of the detected spectrum holes and the choice of the neighboring

users.

WANG AND LIU: ADVANCES IN COGNITIVE RADIO NETWORKS: A SURVEY 17

Potential control channel saturation will degrade the network

performance severely. An alternative MAC protocol without re-

quiring a common control channel for multi-hop CR networks

is proposed in [118]. By dividing the time into ﬁxed-time inter-

vals and have all users listen to a channel at the beginning of

each slot, the proposed protocol ensures that control signals can

be exchanged among users.

G. Distributed Spectrum Sharing

In centralized spectrum allocation, a lot of information needs

to be exchanged among the central controller and network users

to coordinate their spectrum usage, and this results in a large

amount of signaling overhead. Therefore, distributed spectrum

sharing is preferred where users can make their decision on how

to use the spectrum solely based on local information.

A distributed spectrum management scheme is proposed in

[119], where nodes take independent actions and share spectrum

resource fairly. An adaptive approach to manage spectrum usage

in dynamic spectrum access networks is investigated in [120].

Considering the frequency agility and adaptive bandwidth, the

concept of time-spectrum block is introduced in [121], and a

distributed protocol is developed to solve the spectrum alloca-

tion problem which enables each node to dynamically choose

the best time-spectrum block based only on local information.

Based on the adaptive task allocation model in insect colonies, a

biologically-inspired spectrum sharing algorithm is introduced

in [122]. The proposed algorithm enables secondary users to

distributively determine the appropriate channels to use with no

spectrum handoff latency due to coordination, and achieves efﬁ-

cient spectrum sharing. A distributed resource-management al-

gorithm that allows network nodes to exchange information and

learn the actions of interfering nodes using multi-agent learning

approach is proposed in [123].

H. Spectrum Sharing Game

Game theory is a well-developed mathematical tool that

studies the intelligent behaviors of rational decision makers in

strategic interactions, such as cooperation and competition. In

dynamic spectrum sharing, secondary users compete for the

limited spectrum resources. If they do not belong to the same

network entity, secondary users only aim at maximizing their

own beneﬁt from utilizing the spectrum resources. Therefore,

their strategies in dynamic spectrum sharing can be well ana-

lyzed via game theoretical approaches [124].

A game theoretic modeling is presented in [125] that analyzes

the behavior of cognitive users in distributed adaptive channel

allocation. Both cooperative and non-cooperative scenarios are

considered, and a no-regret learning approach is proposed. In

[126], a repeated game approach for spectrum allocations is

proposed, in which the spectrum sharing strategy could be en-

forced using the Nash Equilibrium of dynamic games. Mecha-

nism design is proposed in [127], [128] to suppress the cheating

behavior of secondary users in open spectrum sharing by in-

troducing a transfer function to user’s utility. Spectrum pricing

problem with analysis of the market equilibrium is studied in

[129] and [130]. Correlated equilibrium concept is used in [131]

that can achieve better spectrum sharing performance than non-

cooperative Nash equilibrium in terms of spectrum utilization

efﬁciency and fairness. A game-theoretic overview for dynamic

spectrum sharing is provided in [132].

Auction mechanisms for spectrum sharing have also been

proposed in [133], where the utility of each user is deﬁned

as a function of the received signal-to-noise-and-interference

ratio (SINR). Considering the potential price of anarchy due

to the non-cooperative nature of selﬁsh users, the spectrum

manager charges each user a unit price for their received SINR

or power, so that the auction mechanism achieves the maximum

social utility as well as maximal individual utility. A real-time

spectrum auction framework is proposed in [134] to assign

spectrum packages to proper wireless users under interfer-

ence constraints. In [135] and [136], a belief-assisted double

auction mechanism is proposed to achieve efﬁcient dynamic

spectrum allocation, with collusion-resistant strategies that

combat possible user collusive behavior using optimal reserve

prices. A scalable multi-winner spectrum auction scheme is

proposed in [137] that awards one spectrum band to multiple

secondary users with negligible mutual interference. Effective

mechanisms to suppress dishonest/collusive behaviors are also

considered, in case secondary users distort their valuations

about spectrum resources and interference relationships. Other

truthful and efﬁcient spectrum auction mechanisms have been

studied in [138] and [139].

I. Routing in a CR Network

In traditional wireless networks, all network nodes will be

provided with a certain ﬁxed spectrum band for use. For in-

stance, WLAN uses 2.4 and 5 GHz bands, and GSM uses 900

and 1800 MHz bands. In DSA networks, however, there may be

no such pre-allocated spectrum that can be used by every node

at any time, and the frequency spectrum that can be used for

communication may vary from node to node. This new feature

of DSA network imposes even greater challenges on wireless

networking, especially on routing. If two neighboring nodes do

not have a common channel, or they have common channels

but do not tune to the same frequency, then multi-hop commu-

nication will be infeasible. Thus, new routing algorithms are

needed to accommodate the spectrum dynamics and ensure sat-

isfying network performance such as high network capacity and

throughput, short latency and low packet loss.

Due to the heterogeneity of spectrum availability among

nodes, routing problem can not be well solved without consid-

ering the spectrum allocation. In [140], the inter-dependence

between route selection and spectrum management is studied,

where the network layer selects the packet route as well as de-

cides a time schedule of a conﬂict-free channel usage. In [141],

the topology formation and routing in DSA networks is studied.

DSA network nodes ﬁrst identify spectrum opportunities by

detection, and then the detected spectrum opportunities are

associated to the radio interfaces of each node. A layered graph

model is proposed to help assign the spectrum opportunities to

the radio interfaces.

A MAC-layer conﬁguration algorithm is proposed [142],

which enables nodes to dynamically discover the global

network topology and node location, and identify common

channels for communication. New routing metrics are intro-

duced [142], [143], such as the number of channel switches

18 IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, VOL. 5, NO. 1, FEBRUARY 2011

along a path, frequency of channel switches on a link, and

switching delay [142].

Spectrum-aware on-demand routing protocol is proposed in

[144], which select routes according to the switching delay be-

tween channels and the backoff delay within a channel. A prob-

abilistic path selection approach is proposed for a multi-channel

CR network [145]. The source node ﬁrst computes the route that

has the highest probability to satisfying a required demand, and

then veriﬁes whether the capacity of the potential path indeed

meets the demand. If not, extra channels are judiciously added

to the links of the route until the augmented route satisﬁes the

demand at a conﬁdence level.

Considering the channel switching constraints due to primary

users’ activity, [146] proposes analytical models for channel as-

signment in a general multi-hop CR network, studies the impact

of the constraints on network performance, and investigates the

connectivity and transport capacity of the network.

J. Cooperation Stimulation and Enforcement

In the last decade there has been tremendous developments in

cooperative communications. A thorough and detailed discus-

sion about various issues on cooperative communications and

networking can be found in [147], in which the underlying as-

sumption is that all nodes are unconditionally cooperative. How-

ever, this assumption is often not valid because a rational node is

considered selﬁsh with a tendency to maximize its own utility.

Cooperation is very important when a cognitive user cannot

complete his/her task by him/herself alone. Often a cognitive

radio network is not centralized but autonomous, and users are

either individuals or belong to different authorities and pursue

different goals, so cooperation among these selﬁsh users cannot

be taken for granted. In [148], cooperation stimulation and en-

forcement among cognitive nodes on routing a packet in au-

tonomous networks is investigated. Each node keeps a record

on the cooperation degree between any other nodes in the net-

work, which represents the difference between the contributions

of two nodes to each other. When the cooperation degree is

very low, no cooperation could happen among the nodes. By

maintaining a proper positive cooperation degree, cooperation

among selﬁsh nodes can be enforced. A credit mechanism is

further introduced in [149], where the number of packets for-

warded by one node for another node is bounded by a threshold

(credit line), and the threshold is adapted to the number of the

forwarding requests between the pair of nodes in real time. Co-

operation enforcement under noise and imperfect observation is

further studied in [150]. Since the nodes need to infer the future

actions of others based on their own imperfect observation in a

noisy environment, a belief evaluation framework is considered,

where Bayes’ rule is used to assign and update each node’s be-

lief values.

K. Security in CR Networks

Due to their new characteristics, such as the requirement on

the awareness of the surrounding environment and internal state,

reasoning and learning from observations and previous experi-

ence to recognize environment variations, adaptation to the en-

vironment, and coordination with other users/devices for better

operation, CR networks face their unique security challenges. In

[151], awareness spooﬁng and its impact on different phases of

a cognitive cycle has been studied. Through spooﬁng, the ma-

licious attackers can cause an erroneously perceived environ-

ment, introduce biases to CR decision-making process, and ma-

nipulate secondary users’ adaptation. In [46], the authors have

investigated the primary user emulation attack, where the cog-

nitive attackers mimic the primary signal to prevent secondary

users from accessing the licensed spectrum. Localization-based

defense mechanism is proposed, which veriﬁes the source of the

detected signals by observing the signal characteristics and es-

timating its location from the received signal energy. The work

in [79] has investigated the spectrum sensing data falsiﬁcation

attack, and proposed a weighted sequential probability ratio test

to alleviate the performance degradation due to sensing error.

In the proposed approach, individual sensing reports are com-

pared with the ﬁnal decision. Users whose reports are identical

to the ﬁnal decision will have high reputation values, and their

reports will then carry more weight in future decision fusion.

Several types of denial of service attacks in CR networks have

been discussed in [152], such as spectrum occupancy failures

where secondary users are induced to interfere with primary

users, policy failures that affect spectrum coordination, location

failures, sensor failures, transmitter/receiver failures, compro-

mised cooperative CR, and common control channel attacks.

Simple countermeasures are also discussed. How to secure a

CR network by understanding identity, earning and using trust

for individual devices, and extending the usage of trust to net-

working has been discussed in [153].

V. COGNITIVE RADIO PLATFORMS AND STANDARDS

Although many works have been proposed to improve the

performance of spectrum sensing and dynamic spectrum access

and sharing, most of them only focus on the theoretical mod-

eling and analysis and few of them have been veriﬁed in a prac-

tical system. Therefore, CR platforms need to be developed as a

real-world testbed that can verify the theoretical analysis. In this

section, we will ﬁrst review some existing testbed/platformsand

their features, followed by a brief discussion about standardiza-

tion of CR techniques.

Researchers at the University of California at Berkeley have

proposed an experimental setup based on the Berkeley Emu-

lation Engine 2 (BEE2) platform [154] to compare different

sensing techniques and develop metrics and test cases so as

to measure the sensing performance. In speciﬁc, a good CR

system should provide sufﬁcient protection to the PU, which

casts certain requirements on a CR testbed, including the ca-

pability to support multiple radios, connect various different

front-ends to support different frequency ranges, the capability

for physical/link layer adaptation and fast information exchange

for sensing and cooperation, and the capability to perform rapid

prototyping. The BEE2 can meet these requirements, and sup-

port the features for a CR testbed. Using the BEE2 platform, re-

search on spectrum sensing using energy detection and sensing

with cooperation is tested by experiments [155], which shows

the feasibility and practical performance limits of energy detec-

tion under real noise and interference in wireless environments.

In [156], the feasibility of cyclostationary feature detection is

further investigated.

WANG AND LIU: ADVANCES IN COGNITIVE RADIO NETWORKS: A SURVEY 19

A distributed genetic algorithm based CR engine is proposed

in the center for wireless telecommunications at Virginia Tech

[157], [158]. The cognitive engine focuses on how to provide

CR capability to the physical and MAC data link layers. The

cognitive system monitor enables cross layer cognition and

adaptation by classifying the observed channel, matching

channel behavior with operational goals, and passing the goals

to a wireless system genetic algorithm adaptive controller

module to gradually optimize radio operation. Researchers at

Rutgers University have constructed an Open Access Research

Testbed for Next-Generation Wireless Networks (ORBIT)

[159] to perform experimentation on CR research. Based on the

architectural foundation [160], high performance CR platform

with integrated physical and network layer capabilities [161] is

under development using the ORBIT testbed.

IEEE 802.22 [162] is proposed to reuse the fallow TV

spectrum without harmful interference to TV incumbents.

A CR based PHY and MAC for dynamic spectrum sharing

of vacant TV channels is evaluated in [163], which studies

spectrum sensing, coexistence of primary and secondary users,

spectrum management, reliability and QoS, and their impact on

the overall network performance. Dynamic frequency hopping

(DFH) is recently proposed in IEEE 802.22 [162], where

sensing is performed on the intended next working channels in

parallel to data transmission in current working channel and no

interruption is required for sensing. Efﬁcient and mutual inter-

ference-free spectrum usage can only be achieved if multiple

users operating in DFH can coordinate their hopping behavior.

In [164], DFH communities are proposed so that neighboring

secondary users form cooperating communities and coordinate

their hopping patterns in DFH. The analysis in [165], quantiﬁes

the idle bandwidth in the current TV band assignments, and

the statistical analysis shows that secondary users can operate

on the discontiguous idle spectrum using OFDM. A feature

detector design for TV bands is studied in [166].

The IEEE P1900 [167] is a new standard series focusing on

next generation radio and spectrum management. One impor-

tant focus of the standard is to provide reconﬁgurable networks

and terminals in a heterogeneous wireless environment, where

the multi-homing capable terminals enable users to operate mul-

tiple links simultaneously. The architectural building blocks in-

clude a network reconﬁguration management (NRM) module

that provides information about the environment, a terminal re-

conﬁguration management (TRM) module that takes informa-

tion from the NRM and determines the optimal radio resource

usage strategies, a radio enabler of reconﬁguration management

that acts as a link between the NRM and the TRM.

VI. CONCLUSION

Cognitive radio technology has been proposed in recent years

as a revolutionary solution towards more efﬁcient utilization of

the scarce spectrum resources in an adaptive and intelligent way.

By tuning the frequency to the temporarily unused licensed band

and adapting operating parameters to environment variations,

cognitive radio technology provides future wireless devices with

additional bandwidth, reliable broadband communications, and

versatility for rapidly growing data applications. In this survey,

the fundamental concept about cognitive radio characteristics,

functions, network architecture and applications are presented,

and then various research topics on cognitive radio networks

are discussed. We start with the prerequisite requirement on

deploying cognitive radio, i.e., spectrum sensing, and review

different types of detection techniques and cooperative spec-

trum sensing protocols. In addition, recently proposed dynamic

spectrum management and sharing schemes are reviewed, such

as medium access control, spectrum handoff, power control,

routing, and cooperation enforcement.

The reviews provided in this survey article demonstrate the

promising future of cognitive radio technology in terms of

dynamic spectrum selectivity, high-speed seamless commu-

nications, and low deployment cost. Meanwhile, the intrinsic

features of the new communication technology impose new

challenges in the design of efﬁcient spectrum management and

sharing schemes [168]. Researchers are expected to come up

with novel solutions to higher spectrum efﬁciency enlightened

by this survey.

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2010.

Beibei Wang (S’07) received the B.S. degree in

electrical engineering (with the highest honors) from

the University of Science and Technology of China,

Hefei, in 2004, and the Ph.D. degree in electrical en-

gineering from the University of Maryland, College

Park, in 2009.

From 2009 to 2010, she was a Research Associate

at the University of Maryland. Currently, she is a Se-

nior Systems Engineer with Corporate Research and

Development, Qualcomm Inc., San Diego, CA. Her

research interests include wireless communications

and networking with a focus on cognitive radios, dynamic spectrum allocation,

and network security. She is a coauthor of Cognitive Radio Networking and Se-

curity: A Game-Theoretic View (Cambridge Univ. Press, 2010).

Dr. Wang was the recipient of the Graduate School Fellowship, the Future

Faculty Fellowship, and the Dean’s Doctoral Research Award from the Univer-

sity of Maryland.

K. J. Ray Liu (F’03) was named a Distinguished

Scholar-Teacher at the University of Maryland, Col-

lege Park, in 2007, where he is Cynthia Kim Eminent

Professor of Information Technology. He serves as

an Associate Chair of Graduate Studies and Research

of the Electrical and Computer Engineering Depart-

ment and leads the Maryland Signals and Informa-

tion Group conducting research encompassing broad

aspects of wireless communications and networking,

information forensics and security, multimedia signal

processing, and biomedical engineering. His recent

books include Cognitive Radio Networking and Security: A Game Theoretical

View, Cambridge University Press, 2010; Cooperative Communications and

Networking (Cambridge University Press, 2008), Resource Allocation for Wire-

less Networks: Basics, Techniques, and Applications (Cambridge University

Press, 2008) Ultra-Wideband Communication Systems: The Multiband OFDM

Approach (IEEE-Wiley, 2007), Network-Aware Security for Group Communica-

tions (Springer, 2007), Multimedia Fingerprinting Forensics for Traitor Tracing

(Hindawi, 2005), Handbook on Array Processing and Sensor Networks (IEEE-

Wiley, 2009).

Dr. Liu is the recipient of numerous honors and awards including IEEE Signal

Processing Society Technical Achievement Award and Distinguished Lecturer.

He also received various teaching and research recognitions from University of

Maryland including university-level Invention of the Year Award, and the Poole

and Kent Senior Faculty Teaching Award and the Outstanding Faculty Research

Award, both from A. James Clark School of Engineering. He is a Fellow of

the AAAS. He is President-Elect and was Vice President–Publications of IEEE

Signal Processing Society. He was the Editor-in-Chief of the IEEE Signal Pro-

cessing Magazine and the founding Editor-in-Chief of the EURASIP JOURNAL

ON ADVANCES IN SIGNAL PROCESSING.

Reconstruction of Radio Environment Map Based on Multi-Source Domain Adaptive of Graph Neural Network for Regression

Article

Full-text available

Apr 2024
SENSORS-BASEL

The graph neural network (GNN) has shown outstanding performance in processing unstructured data. However, the downstream task performance of GNN strongly depends on the accuracy of data graph structural features and, as a type of deep learning (DL) model, the size of the training dataset is equally crucial to its performance. This paper is based on graph neural networks to predict and complete the target radio environment map (REM) through multiple complete REMs and sparse spectrum monitoring data in the target domain. Due to the complexity of radio wave propagation in space, it is difficult to accurately and explicitly construct the spatial graph structure of the spectral data. In response to the two above issues, we propose a multi-source domain adaptive of GNN for regression (GNN-MDAR) model, which includes two key modules: (1) graph structure alignment modules are used to capture and learn graph structure information shared by cross-domain radio propagation and extract reliable graph structure information for downstream reference signal receiving power (RSRP) prediction task; and (2) a spatial distribution matching module is used to reduce the feature distribution mismatch across spatial grids and improve the model’s ability to remain domain invariant. Based on the measured REMs dataset, the comparative results of simulation experiments show that the GNN-MDAR outperforms the other four benchmark methods in accuracy when there is less RSRP label data in the target domain.

Efficient Hardware Design of DNN for RF Signal Modulation Recognition Employing Ternary Weights

Article

Full-text available

Jan 2024

This paper presents an efficient deep neural network (DNN) accelerator designed for radio frequency (RF) signal modulation recognition. A novel DNN design optimized for mobile applications is demonstrated by combining MobileNetV3-based DNN with a ternary weight quantization.We also propose a new training method called decaying weight training to overcome the performance degradation due to quantization. The effect of the ternary weight quantization is demonstrated with a co-analysis of the classification accuracy and the physical design. The physical design analysis is based on the Application Specific Integrated Circuit (ASIC), and the results show that the ternary weight quantization with the proposed training method minimizes the impact of the quantization while increasing the allowable clock frequency and reducing hardware cost significantly.We also implement the hardware design dedicated to the ternary weight networks to reduce the required number of the multiply and accumulate (MAC) engines. The hardware design is verified on FPGA and the ternary weight-based DNN shows the feasibility of reducing the hardware cost significantly.

MAC approaches to communication efficiency and reliability under dynamic network traffic in wireless body area networks: a review

Article

Full-text available

Jun 2024
COMPUTING

Wireless Body Area Networks (WBANs) are wireless sensor networks that monitor the physiological and contextual data of the human body. Nodes in a WBAN communicate using short-range and low-power transmissions to minimize any impact on the human body’s health and mobility. These transmissions thus become subject to failures caused by radiofrequency interference or body mobility. Additionally, WBAN applications typically have timing constraints and carry dynamic traffic, which can change depending on the physiological conditions of the human body. Several approaches for the Medium Access Control (MAC) sublayer have been proposed to improve the reliability and efficiency of the WBANs. This paper proposes and uses a systematic literature review (SLR) method to identify, classify, and statistically analyze the published works with MAC approaches for WBAN efficiency and reliability under dynamic network traffic, radiofrequency interference, and body mobility. In particular, we extend a traditional SLR method by adding a new step to select publications based on qualitative parameters. As a result, we identify the challenges and proposed solutions, highlight advantages and disadvantages, and suggest future works.

Low-Complexity Design and Implementation of a Software-Defined 5G Energy Detector for Fast Initial Cell Selection

Article

Full-text available

Jan 2024

Energy detection is crucial during initial cell selection as it effectively assists the User Equipment (UE) in swiftly identifying a suitable Radio Frequency (RF) channel from numerous candidates. Implementing a Software-Defined Modem (SDM) presents a critical challenge of enabling energy detection, which traditionally requires high computational complexity in conventional hardware modems. This paper introduces a software-defined energy detector designed with low complexity, specifically tailored for an SDM. We design and implement an off-the-shelf energy detector that is fully software-based, with the goal of significantly reducing the time required for initial cell selection in an SDM. The software-defined energy detector is specialized for measuring 5G Synchronization Signal Blocks (SSBs). It emphasizes the key feature of the moving average filter which enables efficient computation of signal energy for an SSB. To enable real-time operation, the algorithm for the software-defined energy detector is designed to seamlessly utilize Single Instruction and Multiple Data (SIMD) functions. By implementing the software-defined energy detector, we optimize the detection parameters, thereby enhancing the practical performance of energy detection. The experimental results demonstrate that the software-defined energy detector can accurately evaluate the signal energy of the 5G SSBs with reasonable computational complexity.

Beyond 5G: A Comprehensive Exploration of 6G Wireless Communication Technologies

Preprint

Full-text available

May 2024

As the telecommunications landscape braces for the post-5G era, this paper embarks on delineating the foundational pillars and pioneering visions that define the trajectory towards 6G wireless communication systems. Recognizing the insatiable demand for higher data rates, enhanced connectivity, and broader network coverage, we unravel the evolution from the existing 5G infrastructure to the nascent 6G framework, setting the stage for transformative advancements anticipated in the 2030s. Our discourse navigates through the intricate architecture of 6G, highlighting the paradigm shifts towards super convergence, non-IP-based networking protocols, and information-centric net-works, all underpinned by a robust 360-degree cybersecurity and privacy-by-engineering design. Delving into the core of 6G, we articulate a systematic exploration of the key technologies earmarked to revolutionize wireless communication including terahertz (THz) waves, optical wireless technology, and dynamic spectrum management,while elucidating the intricate tradeoffs necessitated by the integration of such innovations. This paper not only lays out a comprehensive 6G vision accentuated by high security, affordability, and intelligence but also charts the course for addressing the pivotal challenges of spectrum efficiency, energy consumption, and the seamless integration of emerging technologies. In this study, our goal is to enrich the existing discussions and research efforts by providing comprehen-sive insights into the development of 6G technology, ultimately supporting the creation of a thoroughly connected future world that meets evolving demands.

Double-Faced Active Intelligent Reflecting Surfaces-Assisted Symbiotic Radio Communications

Article

Full-text available

Jan 2024

With the extensive deployment of Internet-of-Things (IoT) in next generation wireless systems, the problems of energy efficiency and battery life become exacerbated, highlighting the pressing need for innovative solutions. Symbiotic radio (SR) is considered one of the emerging technologies that aims at providing an energy-efficient solution for the ubiquitous IoT applications. In this paper, we propose an SR system that is assisted with a double-faced active intelligent reflecting surface (DFA-IRS). The proposed system consists of an active transmitter (AT), an active receiver (AR), a backscatter receiver (BR), a DFA-IRS, and an IoT device that is connected to the DFA-IRS. We formulated a BR spectral efficiency maximization problem via optimizing the active beamforming vector at the AT, the power amplification factors of the IRS active elements, and the IRS phase shift at each active element under the constraints of a maximum power budget and the AR spectral efficiency requirements. The formulated problem is non-convex due to the coupling between different variables. Hence, we divided the main problems into three sub-problems and utilized the successive convex approximation (SCA) and the semidefinite relaxation (SDR) techniques to obtain a convex equivalent problem that can be solved using conventional optimization tools such as CVX. Simulation results show that the proposed algorithm converges in few number of iterations. Moreover, the proposed scheme achieves better BR spectral efficiency when compared to the case where a single-faced active IRS or a simultaneously transmitting and reflecting IRS (STAR-IRS) counterpart is used.

Unequal Clustering Energy Hole Avoidance (UCEHA) algorithm in Cognitive Radio Wireless Sensor Networks (CRWSNs)

Article

Full-text available

Jun 2024
WIREL NETW

Cognitive Radio Wireless Sensor Networks (CRWSNs) promise optimized spectrum utilization but face challenges in sustaining energy balance, particularly due to the emergence of “hot spots.” In CRWSNs, Cluster Heads (CHs) closer to the sink experience higher traffic as compared to those farther away, primarily due to their role in data collaboration and relaying to the sink. This leads to early depletion of their energy reserves and potentially causing the network to partition creating hot spots or energy holes. Effective clustering algorithms are needed to mitigate these hot spots. The main objective of the paper is to propose a novel clustering scheme titled “Unequal Clustering Energy Hole Avoidance (UCEHA) algorithm” to address hot spot issues in CRWSNs. UCEHA partitions the network into clusters based on sink proximity, selecting CHs considering node energy, communication channels, neighbors, and sink distance. An enhanced spectrum-aware AODV mechanism facilitates efficient data routing. To test and validate the proposed methodology, extensive experimentations were conducted and the results demonstrate UCEHA’s superiority over existing methods, exhibiting reduced energy consumption (average 19%), improved network load balance (average 26%), increased network lifetime (average 40%), and enhanced throughput (average 8%). These results highlight the effectiveness of UCEHA algorithm in addressing energy imbalance and hot spot issues in CRWSNs, ultimately leading to enhanced network performance and longevity.

Empty Frequency Band Detection using Simulation Tool

Article

May 2024

Mr. Ohol R. D.

Cognitive radio (CR) technology could be a promising thanks to improve the information measure efﬁciency of underutilized radio-frequency spectrum. Spectrum sensing is a vital thought in psychological feature radio. During a real time spectrum sensing energy detection is simplest technique of user detection; however its performance degrades with increase in SNR. The Match Filter Detection needs previous info concerning User for its detection. Therefore to beat noise downside and security problems Cyclostationary Spectrum Sensing technique is employed. Cyclostationary Detection (CD) uses the cyclic property of the signal for the user detection. These cyclic property area unit time variables as there mean and autocorrelation changes with time. These signals area unit referred to as Cyclostationary Signal. The noise is non-cyclic in nature therefore at the time of detection noise is eliminated. To beat the one node sensing issue and hidden node downside that arises because of channel impediments, cooperative sensing is getting used. This paper performs Cyclostationary spectrum sensing that is optimized by Frequency Accumulation technique

A STUDY ON REINFORCEMENT LEARNING TO KEEP DEVICES SECURED IN COGNITIVE RADIO NETWORKS Abstract

Chapter

Nov 2023

Galiveeti Poornima

A major objective of this book series is to drive innovation in every aspect of Artificial Intelligent. It offers researchers, educators and students the opportunity to discuss and share ideas on topics, trends and developments in the fields of artificial intelligence, machine learning, deep learning and more, big data and computer science, computer intelligence and Technology. The content of the book is as follows

Consistent Information Broadcast Over Cognitive

Article

Full-text available

Apr 2024

Ramesh PALANISAMY

One of the primary factors contributing to cognitive radio's reputation as a very promising strategy for 5G wireless networks is its ability to significantly increase spectrum consumption efficiency, which is really fairly considerable. Primary (or licensed) users (PUs) are aware of the necessity for range access to the groups for which they have licenses, and secondary users (SUs), sometimes known as cognitive users, are permitted to access the same range in an unobtrusive manner. These two different kinds of clients provide different cognitive radio networks (CRN) concepts a typical range.

Wideband Spectrum Sensing in Cognitive Radio Networks

Article

Full-text available

Feb 2008

Spectrum sensing is an essential enabling functionality for cognitive radio networks to detect spectrum holes and opportunistically use the under-utilized frequency bands without causing harmful interference to legacy networks. This paper introduces a novel wideband spectrum sensing technique, called multiband joint detection, which jointly detects the signal energy levels over multiple frequency bands rather than consider one band at a time. The proposed strategy is efficient in improving the dynamic spectrum utilization and reducing interference to the primary users. The spectrum sensing problem is formulated as a class of optimization problems in interference limited cognitive radio networks. By exploiting the hidden convexity in the seemingly non-convex problem formulations, optimal solutions for multiband joint detection are obtained under practical conditions. Simulation results show that the proposed spectrum sensing schemes can considerably improve the system performance. This paper establishes important principles for the design of wideband spectrum sensing algorithms in cognitive radio networks.

Fundamental limits on detection in low SNR under noise uncertainty

Article

Jan 2005

Cognitive radio: An integrated agent architecture for software defined radio, Doctor of Technology

Article

Jan 2000

J. Mitola

Spectrum policy task force

Article

Resource Allocation for Wireless Networks: Basics, Techniques, and Applications

Article

Jan 2008

Merging the fundamental principles of resource allocation with the state-of-the-art in research and application examples, Han and Liu present a novel and comprehensive perspective for improving wireless systems performance. Cross-layer multiuser optimization in wireless networks is described systematically. Starting from the basic principles, such as power control and multiple access, coverage moves to the optimization techniques for resource allocation, including formulation and analysis, and game theory. Advanced topics such as dynamic resource allocation and resource allocation in antenna array processing, and in cooperative, sensor, personal area, and ultrawideband networks, are then discussed. Unique in its scope, timeliness, and innovative author insights, this invaluable work will help graduate students and researchers to understand the basics of wireless resource allocation whilst highlighting modern research topics, and will help industrial engineers to improve system optimization.

Cognitive radio: Brain-empowered wireless communications selected areas in communications

Article

Jan 2005

S. Haykin

Cognitive Radio Networking and Security: A Game-Theoretic View

Article

Dec 2010

With the rapid growth of new wireless devices and applications over the past decade, the demand for wireless radio spectrum is increasing relentlessly. The development of cognitive radio networking provides a framework for making the best possible use of limited spectrum resources, and it is revolutionising the telecommunications industry. This book presents the fundamentals of designing, implementing, and deploying cognitive radio communication and networking systems. Uniquely, it focuses on game theory and its applications to various aspects of cognitive networking. It covers in detail the core aspects of cognitive radio, including cooperation, situational awareness, learning, and security mechanisms and strategies. In addition, it provides novel, state-of-the-art concepts and recent results. This is an ideal reference for researchers, students and professionals in industry who need to learn the applications of game theory to cognitive networking.

Random matrix theory and wireless communications. Print version of Foundations and Trends in Communications and Information Theory Vol. 1, No. 1 (2004)

Article

Jan 2004

Pipelined Cooperative Spectrum Sensing in Cognitive Radio Networks

Conference Paper

Apr 2009

Cooperation can improve the performance of spectrum sensing. However, the sensing overhead is generally increasing with the number of cooperating users as more data needs to be reported to the fusion center. Most existing works assume a general time frame structure in which spectrum observing and sensing results reporting are conducted sequentially. We argue that this frame structure is inefficient, since the time consumed by reporting contributes little to the performance of spectrum sensing. In this paper, we propose a pipelined spectrum sensing framework, in which spectrum observing is conducted concurrently with results reporting in a pipelined way. By making use of the reporting time for sensing, the new framework provides a much wider observing window for spectrum measurement, which results in a performance improvement of spectrum sensing. Besides, we also present a multi-threaded sequential probability ratio test method (MTSPRT) which is very suitable for the pipelined framework as the data fusion technique. The MTSPRT method can improve the sensing speed significantly. Numerical results indicate that our pipelined sensing scheme incorporating with MTSPRT shows a better performance than the cooperative sensing based on the general frame structure.

Spectrum Policy Task Force

Article

Jan 2002

Paul Kolodzy

Advances in Cognitive Radio Networks: A Survey

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