Spectrum sharing in Cognitive Radio Networks: A Survey

Anna WisniewskaComputer Science Department

City University of New York Graduate CenterNew York, NY 10016, U.S.A.

1

Abstract

Spectrum scarcity and underutilization in wireless networks, arising due to traditional staticspectrum allocation, have been of great concern since the past decade. The suboptimal use ofspectrum motivates new wireless concepts where unlicensed users opportunistically access licensedspectrum using cognitive radio technology. A cognitive radio can change its operating parameterintelligently in real time to account for dynamic changes in a wireless environment. Licenseduser (primary user) and cognitive radio users (secondary users) may coexist in a spectrum band,provided that secondary users operate without interfering with primary user’s transmission. Sec-ondary users have the ability to sense a primary user’s activity in a band employing varioussignal processing techniques. In addition to hierarchal coexistence with primary users, secondarynetworks compete for a limited number of resources with other cognitive radio networks in thesystem, i.e. self-coexistence. Both coexistence and self-coexistence in cognitive radio networksforce a secondary user to learn and adapt to its environment quickly. The complexity of dy-namic spectrum access due to fluctuating spectrum availability, heterogeneous secondary users,diverse quality of service requirements, and increasing network density indicates a need to lookbeyond traditional approaches. Parallels have been drawn between dynamic spectrum access andbiology inspired paradigms describing social and asocial interactions between agents. Proposedschemes using decision theory model complex secondary-secondary dynamics. Foraging insectsocieties have inspired schemes where self-organizing cognitive radio networks achieve spectrumaccess fairness. In this survey, although both spectrum sensing and spectrum sharing are criticalaspects of dynamic spectrum access, we focus primarily on dynamic spectrum sharing paradigmsdrawn from decision theory and the study of foraging societies. A general overview of cognitivenetwork architecture, cognitive radio duty cycle, and spectrum sensing is given to emphasize theunderlying limitations and challenges in cognitive radio networks.

2

Contents

1 Introduction 4

2 Background 52.1 Spectrum sharing techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.1.1 Underlay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.1.2 Overlay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.1.3 Interweave . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2 Spectrum opportunities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.3 Duty Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.3.1 Spectrum Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.3.2 Spectrum Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.3.3 Spectrum Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.3.4 Spectrum Mobility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.4 Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.4.1 PHY layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.4.2 MAC layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3 Spectrum Sensing 103.1 Transmitter Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3.1.1 Energy detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113.1.2 Coherent detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113.1.3 Matched filter detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123.1.4 Cyclostationary Feature detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123.1.5 Other detection techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.2 Cooperative sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133.2.1 Hypothesis testing in fading environments . . . . . . . . . . . . . . . . . . . . . . . . . 133.2.2 Data Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143.2.3 Cooperative sensing tradeoffs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163.2.4 Centralized cooperative sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173.2.5 Distributed cooperative sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

4 Spectrum Sharing 194.1 Multi-armed bandit problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194.2 Game theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

4.2.1 Static game . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224.2.2 Repeated game . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254.2.3 Stackelberg game . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

4.3 Swarm intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294.3.1 ACO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304.3.2 PSO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

5 IEEE 802.22 standard 345.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345.2 Topology, PHY, and MAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

5.2.1 Topology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345.2.2 PHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355.2.3 MAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

5.3 Incumbent Protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355.3.1 Sensing management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365.3.2 Geolocation management and Incumbent Database query . . . . . . . . . . . . . . . . 36

5.4 Self-coexistence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

6 Conclusion 36

3

1 Introduction

Proposed by Mitola and Meguire [1], Cognitive Radio (CR) users dynamically interacts with its environmentby adjusting communication parameters in real time. The inflexible allocation of spectrum space by regula-tory agencies have resulted in spectrum scarcity. Although, studies show a sporadic use of licensed spectrumresulting in underutilization of spectrum bands [2], [3]. To alleviate spectrum sacristy and underutilization,the suggested solution is a hierarchal approach to spectrum allocation where a spectrum license gives thelicense holder (primary user) access priority to a band but does not guarantee solitude. In the past decadethe focus has been on cognitive radio technology to ensure primary user’s protection. Cognitive radios havethe ability to detect spatial and temporal spectrum holes, i.e. detect the absence of priamry users. Sensingprimary user activity enables CR users to abandon a spectrum band upon primary user’s return and switchto a vacant spectrum band by adjusting its operating parameters. The four functions performed by cogni-tive radio users are: sensing, analysis, sharing, and mobility. The most critical aspects of the duty cycle arespectrum sensing and spectrum sharing.

Spectrum sensing must be performed with high accuracy. Various signal processing techniques have beenproposed to detect primary user’s transmitter. Cooperative sensing has been proposed to further enhanceprimary user detection accuracy. In cooperative sensing CR users exchange sensing information wherespacial diversity combats local sensing challenges such as shadowing and propagation delay. Cooperativesensing can be distributed or centralized. The downside of centralized approaches is the need for backboneinfrastructure which might not be feasible in large-scale networks. In a distributed system, CR users locallyexchange information between each other or send information to a dedicated CR user referred to as the fusioncenter. Using a fusion center minimizes communication overhead since signaling information is forwarded toone CR instead of between all CRs in the network. The objective in both centralized and distributed sensingis to minimize communication overhead while assuring primary user protection.

There are three spectrum sharing techniques in dynamic spectrum access (DSA): overlay, underlay, andinterweave. Out of the three sharing techniques, interweave sharing is the most studied one since it requirescognitive radio technology for primary user protection. While coexistence with primary users is critical inDSA, multiple cognitive radio networks competing over a limited number of resources emphasize the need tostudy self-coexistence. With a new set of challenges arising in DSA, proposed MAC layer sharing paradigmsincorporate ideas from many different fields. For example, the competition between cognitive radio usersover resources can be better understood using game theory. Stackelberg game captures the hierarchal natureof primary-secondary user where primary users receive a payoff as an incentive to share their spectrum. So-cial animal societies, such as, ant colonies or schools of fish, give insight to self-organizing systems followingsimple local rules to solve complex global tasks. The growing demand of wireless service has led to a needfor scalable uncoordinated wireless networks. Swarm intelligence, where foraging ants perform local tasks toachieve global optimization, are used in proposed sharing paradigms to achieve spectrum access fairness indistributed cognitive radio networks.

In this paper we focus on global optimizations spectrum sharing paradigms. The paradigms reviewedare inspired by decision theory and swarm intelligence. We have identified a key paper in each paradigmlisted in table 1 and secondary papers to which it can be contrasted 4. We will not be focusing on spectrum

Table 1: Spectrum sharing paradigms of focus (described in section 4 ).

Multi armed bandit problem UCB1 [103]Game Theory Static game [110]

Repeated game [112]Stackelberg game [114]

Swarm intelligence ACO [119]PSO [121]

sensing, general architecture, or standards which will only be presented as part of the background to thegeneral problem of spectrum sharing. The articles reviewed in section 4 are not meant to an exhaustivelist of approaches in the domain in dynamic spectrum allocation. We give a detailed description of selected

4

articles in selected fields motivated by their unique approaches to resolve a diverse set of spectrum sharingchallenges in cognitive radio networks.

We begin with cognitive radio fundamentals, such as sharing techniques, duty cycle, and architecture, insection 2. In section 3 an overview of spectrum sensing is given. Spectrum sensing is an important aspect ofdynamic spectrum access. Although sensing primary user’s signal is part of the physical layer, many arguethat cognitive radio networks require a cross layer architecture. Therefore, designing efficient MAC layersharing paradigms requires an understanding of PHY layer challenges and limitations. In section 4 selectedbehavioral theoretical and biology inspired spectrum sharing paradigms are described in detail. Finally, insection 5 the 802.22 standard is reviewed.

2 Background

2.1 Spectrum sharing techniques

The main objective of dynamic spectrum access is to assure primary users of interference free transmission.The three paradigms used to facilitate spectrum sharing are underlay, overlay, and interweave [4].

2.1.1 Underlay

The underlay paradigm allows for simultaneous secondary and primary transmission. To ensure primaryuser protection, secondary users may not cross the imposed interference noise level even while the primaryuser is idle. Secondary users most therefore know the aggregated interference from secondary transmitters tothe primary receiver. The interference temperature limit (section 2.3.1) can be used as the power threshold.Additionally, to minimize interference and increase power, secondary transmission can be directed away fromprimary receiver using beamforming techniques [5], [6]. Ultra-wide band (UWB) technology [7] can also beapplied in an environment with power constraints where secondary transmission power is spread over a rangeof spectrum to limit interference to primary narrowband channels [8]. Ultra-wide band technology is usuallysuited for short rang communication since secondary users will operate with low power. Although, in theunderlay scheme secondary users must operate with low power regardless of primary user activity, there isno requirement detecting primary user signal presence and absence.

2.1.2 Overlay

The overlay paradigm, similarly to underlay, allows for co-existence between secondary and primary usersin the same band. In this scheme there is no power limit for secondary transmission. In other words,secondary users can transmit with maximum power. As described in [4], [8], to avoid conflict with primaryusers, secondary users are assumed to have knowledge about certain primary transmission parameters, s.a.codebook and/or message. Channel information can be obtained in various ways, for example, uniformstandard or by broadcasting. This information can be used to enhance primary receivers power by relayingprimary transmission. Since each secondary user have primary codebook and message information, it can,for example, split the power to both send its own message and to relay the primary message as proposedin [4]. Thus, this scheme increase the SNR at the primary receiver. Additionally, techniques, s.a. dirtypaper coding [4], [9], [10], can be used to completely cancel the interference from primary signal at thesecondary receiver. As mentioned in [4], [8], although there is additional transmission overhead, out of thethree paradigms this is the only one where primary users have an incentive to cooperate with secondaryusers since their transmission can be improved by allowing co-existence.

2.1.3 Interweave

Interweave 1 was the original sharing technique proposed for cognitive radios [16] and the most studied ofthe three paradigms [8], [17], [15]. Interweaving differs from the other schemes in that licensed and unli-censed users may not transmit in a band simultaneously. Secondary users may access licensed frequencies

1Some literature [11], [12],[13] classify only two paradigms, overlay and underlay, where overlay corresponds to the interweaveparadigm [4], [8], [14], [15]. Classifying sharing using three paradigms is a more recent approach.

5

(a) Frequency and time domains [11]. (b) Code domain [18]. (c) Angle domain [18].

Figure 1: Multi-dimensional spectrum access.

only if the primary user is not transmitting. Secondary users use sensing techniques (section 3) to detectprimary users’ signals and can opportunistically access idle bands.. If primary transmission is reestablished,secondary users must yield as quickly as possible to minimize interference and can thereafter seek the nextopportunity for transmission. Thus, secondary user must obtain information about primary user activity byspectrum sensing and/or using incumbent activity database.

Throughout this survey, unless stated otherwise, interweaved sharing is assumed. In addition to coexis-tence with primary users in licensed bands, CR networks may operate in unlicensed bands where eachnetwork has the same opportunity to access the band. Hence, unlicensed sharing does not require cognitiveradio capabilities (sensing).

2.2 Spectrum opportunities

Cognitive radio can dynamically adjust its parameters to operate in different radio environments. In op-portunistic spectrum access (interweaved sharing), in order to avoid interference to the licensed user, CRusers must determine spectrum holes in a three dimensional space: frequency, time, and space. Althoughthese three dimensions usually define spectrum holes, [18], [19] emphasize additional radio spectrum spacedimensions for spectrum opportunities as described below:

• Frequency, time, and space: Each frequency spectrum is divided into spectrum bands. A CR user canaccess a band provided that the band is idle, i.e. licensed user is not transmitting. A spectrum bandmight not be used continuously by the licensed user. The temporal dimension refers to the time whenthe primary user is not operating in a band leaving it open for CR users to access. In addition, a bandmight be occupied by the licensed user in one geographical location, due to path loss and shadowing,the same frequency band during the same time might be available in a different geographical location.

• Code: Primary users may use spread code, time hopping, or frequency hoping when transmitting. CRusers use this information to simultaneously transmit in the code domain without interference to thelicensed user provided that orthogonal codes are used.

• Angle: Licensed user may use multiple access multiple output (MIMO) technology over widebandchannels to angle their transmission toward the licensed receiver as opposed to transmitting in alldirections. Given the location and direction (angel of arrival) of licensed transmission, CR users canuse, for example, beamforming technologies [5], [6] to direct the signal away from the licensed receiver.This allows CR users and licensed users to operate in the same frequency, time, space, and codedomains.

To find spectrum holes in the frequency, time, and space domains primary user detection techniques areused. Depending on the signal detection technology, knowledge of licensed signal characteristics may not benecessary. To find spectrum opportunities in the code and angle domain, a priori knowledge of the licensedsignal must be obtained. Moreover, the code and angle domain require sophisticated hardware and arecomputationally complex.

6

2.3 Duty Cycle

2.3.1 Spectrum Sensing

Cognitive radio (CR) users must be able to determine spectrum bands available for transmission. Spectrumsensing enables CR users to detect the primary user’s signal in licensed bands. In a opportunistic spectrumaccess paradigm, where CR users must avoid conflict with primary users by determining their transmissionactivity in a band, CR users periodically monitor spectrum bands to find spectrum holes. Energy detection,matched filter detection, and feature based detection are the three most commonly used transmitter detectiontechniques (section 3.1). Energy detection is easy to implement, but has low resolution compared to matchedfilter and feature based techniques. Matched filter and feature based detection are computationally complexand require a priori knowledge of primary signal characteristics. Since sensing is performed on the primarytransmitter, the primary receiver is vulnerable to interference. The hidden/exposed terminal problem refersto the hidden receiver problem, the hidden transmitter problem, or the exposed transmitter problem [20].In the hidden receiver problem a primary receiver (Rx), within range of a CR transmitter (Tx), experienceinterference if the CR Tx is out of range of the primary Tx (fading) or if some obstacle between the CRTx and the primary Tx obscures the primary signal (shadowing). The hidden transmitter problems occurif the primary Tx is in the range of CR Rx but not the CR Tx and the exposed transmitter problems occurif the primary Tx is in the range of the CR Tx but not the CR Rx. Transmitter detection can be enhancedby cooperative sensing where CR users exchange local sensing information. Cooperative sensing solve thehidden receiver problem since fading and shadowing of primary signal are resolved due to spatial diversity.Although the probability of primary signal detection can be enhanced through cooperation, exchange of localsensing data increase the communication overhead.

To protect the receiver from interference, the FCC proposed interference temperature metric and limit.Interference temperature is measured as the RF power per unit bandwidth at the receiver. CR users must staybelow the interference limit determined by the FCC in order to access licensed spectrum. The interferencetemperature has been shown hard to determine due to, for example, aggregate co-channel and adjacentchannel interference at the licensed receiver. Geolocation techniques have been proposed to enhance orreplace sensing, where the distance to the licensed user is known.

2.3.2 Spectrum Analysis

After finding an idle channel, cognitive radio users will analyze the channel characteristics in order todetermine if it satisfies the desired quality of service (QoS). The QoS for a channel will not only dependon the channel characteristics but also on licensed user activity in the channel and CR user contention inthe channel. CR users must determine the allowed power, path loss, etc. [21] in the channel. Since thelicensed user activity is often not known, CR transmission may be interrupted. Channel bonding, whereCR users select a set of channels for one transmission, can be used to ensure seamless transmission whenlicensed interruption occurs in channel since transmission can continue on the remaining subset of aggregatedchannels.

2.3.3 Spectrum Sharing

CR coexistence techniques with licensed users can be interference limit based (underlay), interference avoid-ance based (interweaved), or both (overlay). Interference limit based techniques allow CR users to access alicensed band simultaneously with licensed users as long as a predetermined temperature limit constraint ismet at all time. In interference avoidance based methods, CR user can only access a band if the licensed useris not currently transmitting in its band. Other access policies allow CR users to use licensed spectrum bothsimultaneously with the licensed user and when a spectrum hole is determined by adjusting their maximumtransmission power. In addition to coexisting with primary users in a spectrum band, CR users must coexistwith other CR users, i.e. self-coexistence.

In a centralized network a central entity, usually referred to as the base station, makes the access decisions.A distributed network does not rely on a central entity instead each user makes its own spectrum accessdecisions. Distributed decisions often lead to less utilized spectrum and unfair access. In cooperativespectrum sharing for distributed systems, CRs exchange information to determine the best access policy.

7

Although cooperation often performs better, the information exchange between nodes may result in lesstime for data transmission. In a cooperative setting, a control channel is needed to for coordination betweennodes. Since CR networks operate in dynamic environment, a dedicated control channel is not always feasiblemaking control message exchange a challenging task.

2.3.4 Spectrum Mobility

Spectrum mobility refers to the CR users ability to quickly adapt and leave a channel in a changing envi-ronment. Once a CR users has chosen the best suited channel for its needs, it will access that channel andstart its transmission. Since the primary user might appear at any time, CR users must continue to monitorthe channel chosen for transmission. If primary signal is sensed, the CR user must abandon the channeland find a different channel for transmission, i.e. spectrum handoff. Spectrum handoff can also occur ifQoS requirement are not met due to interference from other CR users, spatial mobility (where movementmay change channel access opportunities), or any other change in the environment. Spectrum handoff mustbe accomplished without affecting transmission performance. Operation parameters must be reconfiguredwhere each network layer must adjust to the change according to its policy. Seamless communication canbe obtained by estimating the handoff duration and/or applying channel bonding.

2.4 Architecture

2.4.1 PHY layer

The primary task in the PHY layer is spectrum sensing. CR users must detect spectrum opportunitiesin multiple dimensions. CR users perform computationally demanding signal processing with short delay,therefore, the RF front end is designed with high resolution analog to digital converters with large dynamicrange, high sampling rate, and high speed signal processors [18], [22]. CR users can be equipped with single-radio or dual-radio architecture. Dual-radio technology allows for simultaneous sensing and transmission.With single-radio technology either sensing or transmission can be performed at any given time, although,the advantage lies in its simplicity and low cost where dual-radio technology requires high power consumptionand hardware cost. To detect spectrum holes, a CR user can either detect the primary transmitter (indirectspectrum sensing) or the primary receiver (direct spectrum sensing) [23]. To ensure primary users protection,all active primary receivers (Rx) must be outside the CR transmitter’s (Tx) range. Therefore, detectionprimary Rx ensures direct protection to the primary user. Detection the primary Rx is challenging sincethe Rx usually does not transmit signal when receiving Tx transmission. If the primary user system is adirect one way communication between the primary Tx and primary Rx then RF leakage at the primary Rxcan be detected using local oscillator (LO) detection, although, sensing this weak signal makes LO detectionsuitable for short range detection. Indirect sensing techniques are therefore more commonly used. Thedownside of indirect detection is the range at which the CR Tx must detect the primary Tx. The range iscalculated by adding the max primary Tx-Rx range (rP ) to the CR Tx interference range (rCR). Hence,indirect sensing requires a range rP longer than direct sensing, resulting in having to sense weaker primaryuser signal. Energy detection, measured based, and cyclostationary based detection (section 3.1) are used forprimary Tx detection. The most commonly used metric to determine primary signal activity is defined as abinary hypothesis test describing the presence or absence of the primary user in channel (section 3.1.1, 3.2.1).The hypothesis testing can be studied under perfect and imperfect channel conditions. In fading shadowingenvironments, primary signal detection can be enhanced using cooperative sensing (section 3.2). Dependingon the sharing scheme, CR users might not need to identify spectrum holes instead CR users can coexistwith active primary users using low transmission power (section 2.1). In this scenario, the transmissioninterference to primary Rx must be estimated accounting for the aggregate interference of all CR Tx in thearea.

In addition to spectrum sensing, the PHY layer is responsible for channel bonding. Channel bondingallows CR users to aggregate contiguous or non-contiguous channels in order to achieve a wider bandwidthand seamless transmission during spectrum hand-off.

8

2.4.2 MAC layer

The primary tasks in the MAC layer are spectrum access and sensing scheduling.

Spectrum sensing from a MAC layer perspective involves:

• Determine sensing duration - Primary users must be detected with high probability. This can beachieved if given a sufficient amount of time for sensing.

• Scheduling sensing time intervals - CR users must periodically look for primary users. Sensing primaryusers is not limited to finding spectrum holes, it must also be done while CR users are transmitting todetect a primary user returning to its band. The duration between sensing must be carefully chosento ensure primary user protection.

• Scheduling quiet periods - Depending on primary detection technique used, quiet periods are scheduledto limit self-interference during sensing.

• Control channel access - Cooperative sensing requires information exchange over a control channel.Access to the control channel and minimizing information exchange overhead is overseen by the MAClayer.

Determining, sensing duration, sensing intervals, quiet periods, and sensing data exchange, are challengingtasks since the time spent on sensing affects CR data transmission time.

A common control channel (CCC) pose additional MAC protocol design challenges. The CCC schemesare often categorized as in-band or out-of-band. In-band schemes use data channels to facilitate control mes-sage communication. Out-of-band refers to control channels allocated in unlicensed spectrum or spectrumlicensed to the CR network. Out-of-band CCCs are dedicated and most often global. If the dedicated CCCresides in a unlicensed band, each CR network must account for interference from other CR networks by coor-dinating access. There is a higher risk that a dedicated channel will suffer from the CCC saturation problemsince all control messages are exchanged over one channel. The main challenge of in-band CCC allocationis robustness against primary user interruption. Efficient schemes must be designed to account for controlmessage exchange interruption by the primary user where CR networks must be capable of establishing a newCCC with short delay. Hence, in-band paradigms are usually more complex than out-of-band paradigms.Although, an out-of-band channel might not always be available, or costly if a licensed channel has to beassigned. In an underlay scheme, the CCC is assumed to be dedicated since control message transmissionis done simultaneously with primary transmission using UWB communication. [24] give a comprehensivesurvey on CCC schemes and challenges.

Self-coexistence refers to CR user sharing bands with competing CR users. Medium access resolution formultiple users can be either fixed access, random access, or hybrid access. Fixed access protocols are usuallybased on time division multiple accesses (TDMA) or frequency division multiple access protocols (FDMA).These protocols allow for interference free access to CR users, but require system wide synchronization. Ran-dom access protocols are usually based in carrier sense multiple access with collision avoidance (CSMA/CA).Although synchronization is not required, in CSMA schemes CR users may experience interference from otherCR users. Hybrid accesses is a combination of fixed access and random access. There are different variationsof hybrid schemes, most often signaling is performed using fixed access while data transmission is based onrandom access, although, some schemes propose guaranteed data access while signaling is random access.Hybrid schemes lie in between fixed access and random access protocols in terms of efficiency and complexitywhere performance is better than random access and complexity is lower than fixed access.

A wireless network operating in multiple bands is susceptible to the multi-channel hidden terminal prob-lem. Signaling information between a pair of users on one channel c1, unheard by a pair of users operating ina different channel c2, could potentially result in collisions if both pairs negotiate to transmit in channel c3.The multi-channel hidden terminal problem is usually encountered while using single-radio technology sincea user operates in one channel at a time. Dual-radio technology, where one radio is listening to signaling dataon a CCC while the other radio is used for transmission, solve the multi-channel hidden terminal problem

9

at an additional cost.

The categorization of MAC protocols can be based on, underlying hardware, topology, spectrum accessmode, control channel scheme, etc., moreover, MAC protocols can be categorized as follows [25]:

1. Direct Access Based:

- Contention based: Communication channel is negotiated between a sender and a receiver by ex-changing sensing information using a handshake procedure.

- Coordination based: Channel sensing and access information is shared with all local neighbors.

2. Dynamic Spectrum Allocation:

- Game theory

- Stochastic theory

- Genetic algoritms

- Graph coloring theory

- Swarm intelligence

- etc.

Direct access based protocols examine a local optimization of spectrum access where sender-receiver pairsnegotiate resources using a more traditional handshake procedure while dynamic spectrum allocation refers toglobal optimization protocols where emphasis lies on allocation fairness and efficiency [25]. Predominately,MAC layer surveys focus on DAB paradigms [26], [27], [28], [29]. In this survey we focus on dynamicspectrum allocation (DSA) paradigms (section 4). We emphasize these approaches due to their capture ofthe complex and unique challenges of cognitive radio networks. In section 4 we review recent articles drawnfrom decision theory and swarm intelligence in detail. We evaluate the reviewed approaches on how wellthe proposed schemes capture self-coexistence, coexistence, spectrum utilization, operational overhead, andpractical implementation.

3 Spectrum Sensing

Cognitive radio users must be able to detect incumbent users before accessing a channel. Moreover, CRsneed the ability to obtain information about channel quality. Determining channel occupancy and qualitycan be done using transmitter detection, geolocation, or beacons [18]. Often a combination of these areused to maximize performance. Since geolocation and beacons do not require CR capabilities, the followingsections focus on transmitter detection.

3.1 Transmitter Detection

Transmitter detection techniques have been studied extensively. The emphasis on these techniques is im-portant since sensing primary users’ signals is a particularly challenging aspect of dynamic spectrum access.Within a short time limit, each secondary user must be able to detect a primary users signal with highprobability. Low signal-to-noise ratio (SNR), multi-path fading, and time dispersion are some factors thatadd to the complexity of primary user detection. There are many different PHY schemes used to senseprimary users’ signals. The most common techniques are energy detection, coherent detection, matchedfilter detection, and cyclostationary detection. These schemes are described in more detail in the followingsubsections. A few recent detection schemes such as measurement-based sensing, eigenvalue-based sensing,and wavelet based sensing are briefly described in the last subsection.

10

3.1.1 Energy detection

Since energy detection is easy to implement, it has become a widely used technique to sense primary usersignal. Energy detection does not require any knowledge of the primary user signal characteristics. Theenergy level is determined using signal sampling. Once enough samples are obtained, the signal statisticsare compared to a predetermined threshold. To protect licensed users, secondary networks must detectprimary user’s signal with high accuracy. Sensing primary user signal can be defined as a binary hypothesismodel [30], [12], [11]

H0 : s(n) = w(n)

H1 : s(n) = hp(n) + w(n)(1)

where s(n) is the signal received by the secondary user, w(n) is additive white Gaussian noise, p(n) is signalfrom the primary user, and h is the channel gain between the primary and secondary users. H0 is a nullhypothesis describing primary user absence in the channel, while H1 indicates the presence of some primaryuser signal. To achieve desired performance, probability of detection Pd and probability of false alarm Pf

are defined as [31]Pf = P(S > λ|H0) (2)

Pd = P(S > λ|H1) (3)

where the average total energy detected S, using N samples, is defined as

S =1

N

N∑i=1

|s(n)|2 (4)

and compared to a predefined threshold λ in (2) and (3) [12], [30]. Usually λ is computed based on Pf [32]where Pf is the probability that the hypothesis test incorrectly decides H1 instead of H0 i.e. false alarm. Pd

is the probability that H1 has been correctly determined. If Pf is high, the system becomes underutilizedsince secondary users will remain idle due to false alarm. If Pd is low, secondary users will interfere withthe primary user since the probability of not detecting primary users will be high. To ensure primary userprotection while maximizing channel utilization, Pd should be close to one and Pf should be closer to zero.The false alarm and detection rate imposed by standards are usually Pd = 0.9, Pf = 0.1. In [30] thehypothesis model is adjusted to account for shadowing and multi-path fading.

Since in this scheme primary user signal is treated as noise and detection is dependent on thresholdmeasurements [32], this sensing technique is sensitive to noise uncertainty [30]. In an environment where thesignal to noise ratio (SNR) is low, energy detection performs poorly [31]. Secondary users are not able todistinguish between primary user signal and other possible signals. An SNR-wall, defined as an SNR levellower bound under which primary signal can not be detected [33], exists for energy detection. If this lowerbound is reached, regardless of sample size, secondary users will fail to detect the desired signal.

3.1.2 Coherent detection

Some signal patterns, pilot tones, preambles, etc., can be used to detect the presence of primary transmission.If these primary signal patterns are known, coherent detection can result in better performance than energydetection because it will be more robust to noise level uncertainty [30]. To describe coherent detection thebinary hypothesis model is defined as [30]

H0 : s(n) = w(n)

H1 : s(n) =√ϵppt(n) +

√1− ϵp(n) + w(n)

(5)

where s(n) is the signal received by the secondary user, w(n) is additive white Gaussian noise, p(n) is thesignal from the primary user assumed to be orthogonal to the pilot tone, ϵ is the fraction of energy allocatedto the pilot tone, and ppt(n) is the pilot tone. The test statistics S, using N samples, is defined as [34]

S =1

N

N∑i=1

|s(n)|xp(n) (6)

11

where xp(n) is a unit vector in the direction of the pilot tone. In coherent detection there is no SNR-wall [34], [33]. Since (6) is obtained by projecting the received signal onto the pilot tone direction, when Napproaches infinity, noise in other orthogonal directions diminishes [34]. In other words, with enough samplesN , primary signal strength becomes greater than noise level uncertainty. In real systems the SNR-wall doesexist since N is finite. However, [33] proves that this scheme is more robust to noise uncertainty and canoperate in lower SNR regions compared to energy detection. The ability to differentiate between primarysignal and noise makes implementation of coherent detection highly complex.

3.1.3 Matched filter detection

If a precise map of primary user features, such as modulation type, pulse shape, operating frequency, packetformat, etc., exists and noise statistics are known, matched filter detection can result in optimal performancefor primary signal detection [30]. Since the features of the signal are known, signal sampling decreases andit takes less time to detect primary signal. The SNR is maximized since the receiver can more accuratelydetect a signal using the given features. Although, as SNR decreases more sensing samples are necessaryand, hence, there exists an SNR-wall [33]. The disadvantages of matched filter detection are high implemen-tation complexity, high power consumption, and poor performance if the feature map (waveform pattern) isincorrect.

3.1.4 Cyclostationary Feature detection

Cyclostationary feature detection is a feature based sensing technique [35]. Cyclostationary signal statisticsexhibit periodicity since modulated signals can be coupled with sine wave carriers, cyclic prefixes, hoppingsequences, etc [30]. Using this method secondary users can differentiate between primary signal, interference,and noise (which is not a cyclostationary signal). Since cyclostationary detection can separate signal fromnoise, it can be used in low SNR regions. The characteristics of the signal must be known a priori, therefore,cyclostationary detection is limited. Cognitive networks are expected to operate in many different frequen-cies with a diverse set of primary users. Some primary user signal features are known to the public, forexample, TV broadcasters, but some are not as easily obtained. Hence, acquiring primary signal character-istics adds additional complexity to the cyclostationary scheme. In OFDM environments, this scheme mayperform poorly since cyclostationary features may be close to identical [12], [36]. This issue is addressed bymanually inducing different cyclostationary features to each OFDM signal in the system [36]. Furthermore,cyclostationary detection performs poorly with channel fading where features are no longer distinguishablefrom noise [18].

3.1.5 Other detection techniques

If primary user features and noise power level are unknown, eigenvalue-based detection can be used insteadof the noise uncertainty sensitive energy detection. In [37] two algorithms are proposed based on the ratioof maximum-to-minimum eigenvalues and average-to-minimum eigenvalues. A sample covariance matrix isused to differentiate between primary signal and the noise power level. While energy detection comparessignal energy to noise power ratio with a pre-estimated threshold, in [37] signal energy is compared to theminimum eigenvalue of the sample covariance matrix computed from received signal. Hence, eigenvalue-based schemes perform better than energy detection when noise power level is unknown since they are lesssensitive to noise uncertainty. Other eigenvalue-based sensing techniques using multiple sensors are proposedin [38], [39], [40]. In [38] two eigenvalue-based detection methods, proposed in [39] and [40], are compared.The comparison variable is the knowledge of noise power level where in [40] it is unknown, while in [39] thenoise power level is required. It is shown that there is a significant performance improvement when noiselevels are know.

Measurement-Based sensing techniques are proposed in [41] and [42]. Primary users can be identifiedusing transmission data collected over a certain period of time. Once this data is process, primary user idleperiods are found statistically. In [41], WRAN network data is analyzed. Two sensing strategies are usedto verify that the proposed semi-Markov process with a Pareto distribution accurately captures the data.

12

In [42], cellular network data is studied. In this study the dataset consist of, for example, call start time andcall duration. As pointed out in [42], the call duration reflects the RF emission time which is important toDSA networks. Two models describing primary usage are proposed; one describing the call arrival processusing call duration and the other is based on the total number of calls. It is shown that, for example, thecall arrival process can help to dynamically estimate the maximum interference time, determined by the PU,allowing some channels to be sensed more frequently. This allows for a more accurate estimation of timeand resources needed by the secondary network to detect primary users.

Wavelet-based detection is used to identify edges between channels in order to detect spectrum whitespace. This can be achieved without the knowledge of primary users’ signal parameters, but this schemedoes requires information about the noise power. In [43], wavelet-based detection is used to identify irreg-ularities in the power spectral density (PSD). Irregularities appear between adjacent subbands and can beanalyzed using wavelet transform. Compared to multiple narrowband bandpass filters (PBF), where eachPBF can search one subband at a time, the wavelet approach results in lower implementation complexityand can dynamically estimate the number of subbands when the PSD structure is unknown. In [44], fastsensing is achieved using a wavelet-based scheme.

Other sensing techniques proposed are: filter banks [45], [46], fast sensing [47], [48], [49], neural net-work [50], etc.

Regardless of the sensing technique used, local sensing paradigms are limited in practice where shadowing,multi path fading, and receiver uncertainty compromises each CR user’s detection ability [11]. Cooperativesensing may be used to address the short comings of local sensing as described in the following section.

3.2 Cooperative sensing

The idea of cooperative sensing in cognitive radio networks was first introduced in [51], [52]. The shortcomingof local sensing techniques described in section 3.1 is that they do not solve the hidden terminal problemwhere shadowing and multipath fading might negatively effect the received signal strength [51]. To increasethe probability of detecting primary users, cooperative sensing techniques can be deployed to obtain statisticsampling from numerous location where secondary users experience different signal strengths, i.e. spatialdiversity. Additionally, [51] address the main cost factors of cooperative sensing; communication overhead oftransmitting statistical data and the need for a dedicated communication channel to facilitate cooperation.

3.2.1 Hypothesis testing in fading environments

Energy detection as described in section 3.1.1, does not incorporate shadowing and multipath fading. Thehypothesis test can be extended to include channel fading and shadowing as described in [53], [54], [31]

H0 : s(n) = w(n)

H1 : s(n) = hp(n) + w(n)(7)

where s(n) is the signal received by the secondary user, w(n) is additive white Gaussian noise (AWGN),p(n) is signal from the primary user, and h is the channel gain between the primary and secondary users asin section 3.1.1. The average total energy detected S is defined as [55]

S =

{χ22TW , H0

χ22TW (2γ), H1

(8)

where χ22TW is the central chi-square distribution with 2TW degrees of freedom, χ2

2TW (2γ) is a non-centralchi-square distributions with 2TW degrees of freedom and a non-centrality parameter of 2γ, and γ is thesignal-to-noise ratio (SNR). Probabilities Pd (detection) and Pf (false alarm) in a non-fading environmentwhere h is deterministic* [53], [31]? are defined as:

Pf = P(S > λ|H1) =Γ(TW,λ/2)

Γ(TW )(9)

13

Figure 2: ROC (Pm over Pf ) curve under Rayleighfading channels, where γ = 10 dB and m = 5, com-pared to the AWGN curve [53]

Figure 3: ROC (Pm over Pf ) curves under iidRayleigh fading channels for different number of CRusers n and the AWGN curve for comparison [53]

Pd = P(S > λ|H0) = QTW (√2γ,

√λ) (10)

where Qm is the generalized Marcum Q-function [56], Γ(.) is the gamma function, and Γ(., .) is the incompletegamma functions.

The formulation above correspond to a non-fading (AWGN) channel. Since Pf does not depend on theSNR level, it is only necessary to adjust Pd to include channel fading. In [53], the fading parameter isincluded in Pd

Pd =

∫x

QTW (√2γ,

√λ)fγ(x)dx (11)

where fγ(x) is the probability distribution function of SNR under fading [53]. The probability of missdetection is defined as Pm = 1− Pd.

In [53] γ is represented by a log-normal distribution to account for shadowing and an exponentialdistribution, more specifically Rayleigh, to represent multipath fading. To compare the performance of energydetection in fading and non-fading environments, the complementary Receiver Operation Characteristics(ROC) curve, showing the relationship between probability of missed detection Pm and probability of falsealarm Pf , is displayed in figure 2 [53]. It can be seen in figure 2 that energy detection performs poorly infading environments where Rayleigh fading is assumed [53]. The log-normal shadowing shows similar resultswhere energy detection performance degradedness with fading [53]. Moreover, [53] show that performanceof the hypothesis test can be enhanced by cooperative sensing as described in the following section.

3.2.2 Data Fusion

In cooperative sensing, data fusion is used for hypothesis testing [57] (Pf is the probability of false alarm andPd is the probability of detection as described in 3.1). Data fusion refers to a mechanism where data collectedfrom each secondary user is combined and processed by a dedicated fusion center (centralized sensing) orby each CR (distributed sensing) where a decision for channel access is obtained. The fusion center can beeither a base station (centralized network) or a secondary nodes with data fusion capabilities (distributednetwork). Data is sent over a control channel to facilitate communication. Two paradigms, soft combiningand hard combining, are used for detection of primary signal. Soft combining refers to a scheme wheresecondary users send unprocessed local statistical data for data fusion. In hard combining, each secondaryuser locally performs the hypothesis test and send a 1-bit decision to be combined. The choice between thetwo paradigms depends on the bandwidth of the control channel [57]; Soft combining results in increasedoverhead but more accurate detection; Hard combining results in lower overhead but possibly higher infor-mation loss.

Hard combining: [53], [58], [54], [57] To minimize control channel overhead, each secondary user in-dependently make a local binary decision and forwards a 1-bit result to the fusion center, where 1 denotes

14

presence of primary signal and 0 denotes absence of primary signal. The fusion center’s responsibility is tocombine data received, usually using a linear rule for hypothesis testing s.a. the k-out-of-N rule [59], [53].The k-out-of-N fusion rule will decide Pd if at least k secondary users detect the primary signal. Variationsof the k-out-of-N rule most commonly used are, OR, AND, and majority.

- OR (1-out-of-N) rule where the hypothesis for detection Pd is decided if at least one user detects theprimary signal.

- AND (n-out-of-N, n=N) rule requires that all secondary users detect the primary signal in order for thefusion center to determine Pd.

- Majority (⌈n/2⌉-out-of-N, n=N) rule where at least half of the secondary users must report primarysignal detection.

The hypothesis test is decided by the fusion center based on all N local observations as follows [60]

S =N∑i=1

Di

{< k, H0

≥ k, H1(12)

where Di is the 1-bit local decision by the ith secondary user (H0 denotes the absence of primary signal andH1 denotes the presence of primary signal as described in section 3.1). The probability of detection Pd andthe probability of false alarm Pf under the k-out-of-N rule is then [57], [60]

Qf = P{H1|H0} =

N∑i=k

(N

i

)P if (1− Pf )

N−i (13)

Qd = P{H1|H1} =N∑i=k

(N

i

)P id(1− Pd)

N−i (14)

assuming that the threshold is the same for each secondary user (further assumptions are made for bothAWGN and Rayleigh channels as described in [60]).

In [53] the performance of energy detection in fading channels with cooperation is evaluated. All secondaryusers use the same threshold λ and data is fused by the OR-rule [53]. Probabilities of false alarm and detectionare defined as [53]

Qf = 1− (1− Pf )n (15)

Qd = 1− (1− Pd)n (16)

where n is the number of secondary users, Pd is the probability of detection as defined in (11), Pf is theprobability of false alarm as defined in (9), and the probability for missed detection is defined as Qm = 1−Qd.In figure 3, the complementary ROC curve under Rayleigh fading is shown for different numbers n ofcollaborating secondary users [53]. It is clear that while using this scheme, collaboration increases detectionperformance where the required average SNR for detection is much lower for each user when collaborating [53].It should be noted that [53] do not consider fading in the communication channel. Additionally, [53] quantifythe effect of spatially correlated shadowing, which occurs if users are in close proximity of each other and,therefore, might experience the same shadowing effect. It is shown that correlated shadowing does affect thecooperative gain negatively. By placing users further apart, cooperative sensing performance improves.

In [58], [54] the performance of fusion rules AND, OR, and Majority in Rayleigh fading channels is in-vestigated. It is shown that the OR rule outperforms the AND and Majority rules in many cases since thedecision of primary signal presence is based on at least one observation, hence, users become more hesitantto access a channel (see figure 4). However, the OR-rule is not necessarily optimum as pointed out by [58].Moreover, [54] propose cooperative sensing schemes where communication channel fading/shadowing is con-sidered. In [61], the tradeoff between sensing time and the choice of k using the fusion k-out-of-N rule isstudied. An algorithm is proposed to maximize CR user throughput where optimal values for both sensingtime and k are obtained. It is shown that both parameters must be optimized to achieve maximum CR user

15

k

k

k

Figure 4: ROC curves representing different k-out-of-N fusion rules under Rayleigh fading channels with N = 10 number ofusers [54].

throughput.

Soft combining: Soft combining refers to a mechanism when unprocessed statistical observation datais sent from each secondary user to the fusion center. The fusion centre in turn will process the data forhypothesis testing. Soft combining will result in more accurate detection than hard combining since all datais sent instead of just 1-bit votes [62], [63], [64]. The optimal fusion paradigm is the weighted sum of thelog-likelihood ratios (LLRs) when energy detection is used for local sensing [65], [62]. Although, since allsensing data is sent by each secondary user, the overhead is larger than in hard combining. There are variousapproaches to minimize the overhead in the control channel where some local processing is done by eachsecondary node. For example, [62] propose a soften hard combining rule which require each user to onlysend two bits. It is shown that the performance of this scheme is comparable to soft combining schemesusing equal gain combination (EGC) [62].

In [66] spatial diversity is addressed. A distributed cooperative sensing algorithm is used where each CRperforms local energy detection. It is shown that in correlated fading environments, to improve PU signaldetection, obtaining information from a few independent user is more adequate than from many correlatedusers. Moreover, [66] show that hard combining can perform as well as soft combining in most practicalsituations where the number of CRs is substantial [18], since, in practice, there are finite number of samples.

3.2.3 Cooperative sensing tradeoffs

As discussed in section 3.2.1, cooperative sensing solves the hidden terminal problem but correlated shad-owing may occur which degrades sensing accuracy. Using spacial diversity schemes, where CRs are chosenfor sensing depending on their location, may cancel correlated shadowing. There are additional issues thataffect the performance of cooperative sensing. In [57], seven dominating factors of cooperative gain andcooperative overhead are categorized as follows: sensing time and delay, channel impairments, energy effi-ciency, cooperation efficiency, mobility, security, and wideband sensing. Below are brief descriptions of someof these cooperative sensing tradeoffs as described in [57].

- Sensing time and Control channel: Sensing time depends on the hardware and sensing techniquebeing used. There is a tradeoff between CR users throughput and sensing time, i.e more accuratesensing results are obtained at the expense of the amount of time left for data transmission. A dual-radio architecture [67], where data transmission and sensing can be performed simultaneously, canincrease spectral efficiency at the cost of increased power consumption and hardware complexity [18].In [68] an optimal sensing time is derived for energy detection. It is also shown that a distributednetwork in this setting can achieve a lower sensing time using AND hard combining. CR powerconsumption is investigated in [69] for both cooperative and non-cooperative networks. It is shown

16

that cooperation obtains lower power consumption. Moreover, a combination of sensing time togetherwith the power schemes proposed further improves average transmission rate and power consumption.

The need for a control channel to report to the FC or send information between CRs may result insensing delay. Control channel shadowing, fading, collisions, and bandwidth pose a limit on channelcommunication resulting in a sensing delay. Moreover, synchronization is most often required for com-munication between nodes. For example, CR systems using energy detection, where the RF front-endcan not distinguish PU signal from noise, must use synchronized quiet periods while sensing [57]. Syn-chronized communication issues may be solved using asynchronous communication [70], [71], although,both mechanisms cause some delay to the system.

- User selection and Censoring: In a cooperative setting CRs must send their sensing data to eachother or to a fusion center. Transmitting sensing data adds to the cooperative overhead. User selectionproposed in [72], [73], [74], can reduce transmission overhead in the control channel. In a centralizeddeployment the FC select CR users to perform sensing based on certain criteria. To future minimizecontrol channel overhead and reporting delay, cluster-based selection [75], [76], [77] may be used. Incluster-based selection, CRs form coalitions where, for example, a cluster head is chosen to forwardsensing result to the fusion center. Spatially diverse CR users can be chosen for sensing, not only toalleviate communication overhead, but also, to combat correlated shadowing. Since user selection canefficiently choose a few CR users for sensing based on their proximity to other CR user and the PU,battery power consumption in the system is minimize without degrading sensing accuracy. Moreover,user selection can be used to make the network more robust by censoring failed or malicious CRusers. Both data falsification and security attacks (s.a. denial of service) can be addressed usingcensoring usually with an overhead cost. Users reporting false sensing results can be detected beanalyzing sensing data inconsistencies using, for example, reputation-based schemes [78]. A dynamiccontrol channel allocation scheme can be used to fight control channel jamming attacks [79], [80]. Anencrypted predefined channel sequence is exchanged between users. If the control channel is jammed,CR users can switch to the next channel. If the channel sequence is compromised, a pseudo-randomchannel selection [81] decrease the probability of a malicious users access to the channel sequence.

- Mobility: In most literature, static CR users and PU are assumed. Since both CRs and PUs maybe mobile, cooperative sensing techniques may be used to address issues such as spatial correlation.Depending on the movement of CR users, correlates shadowing may increase or decrease in a systemdepending on if CR move closer or further away from each other. In [82], CR mobility is studied whereit is show that mobility may improve primary signal detection. The speed at which the CR are movingis shown to be a factor of spatial-temporal diversity. Moreover, to minimize overhead, an optimalcombination of sensing time and number of cooperating sensors is obtained. Primary user mobility isaddressed in [83], [84]. [84] investigate the optimal sensing time and transmission time in a mobile PUenvironment. In addition, it is shown that there exist a sensing time threshold in mobile environments,i.e. sensing for longer than the threshold does not improve sensing accuracy.

3.2.4 Centralized cooperative sensing

Centralized cooperative sensing can be performed in both centralized and distributed cognitive radio net-works [57]. In centralized cooperative sensing [57], [18] all cognitive radio users report to the fusion center(FC). The base station (BS) usually becomes the FC in a centralized CR network. In a distributed CRnetwork, one of the cognitive radio users will act as the FC. In this architecture, the FC makes spectrumaccess decisions by coordinating the local sensing performed by CRs. CR users are instructed to performlocal sensing based on the FC’s decision as to which channels should be sensed. Once the CRs finish localsensing, they send the information to the FC via a control channel. Lastly, the FC will use soft or hardcombining for hypothesis testing and broadcast the channel access decision to all CRs.

In [85], a centralized cooperative sensing is improved by letting CRs communicate with each other toreduce detection time, hence improve network agility. An extension of [85], considering CR power consump-tion, is proposed in [86]. Network agility is obtained by letting CRs cooperate where the CR who first detectsthe PU’s signal will inform other CR via a common BS.

17

In [73] CR censoring is used to minimize control channel overhead. A distributed network report localdecisions to a common fusion center over both perfect and imperfect control channels. Analytical resultconfirm insignificant performance loss while obtaining low overhead using the proposed censoring scheme.

In [87], energy efficient distributed CR networks form cluster where a ”vote” is forwarded to a clusterhead by each CR with the local decision. The cluster head in turn send the combined local decision madeby the cluster to a fusion center to minimize overhead in the control channel resulting in lower total networkpower consumption.

Energy efficiency is addressed in [88] using sequential detection to minimize sensing time. Each CR sendsits log-likelihood statistics (LLR) to the fusion center. The FC will perform a sequential probability ratiotest (SPRT) [89] and decide if a new round of test statistics is needed or if CR can stop sensing. The SPRT isextended to handle signal uncertainties, such as fading and interference, making the paradigm more robust.

Power consumption is tackled in [90] using a combined sleeping and censoring mechanism in a distributednetwork. Sleep mode allows CRs to turn their sensing transceiver off randomly with some probability to savepower. CRs send a local decision to a fusion center if chosen by the censoring mechanism provided that theyare not in sleep mode [87]. A fusion center combines CR sensing information using hard decision. Closedform expressions for probability of detection and false alarm are obtained. Optimal sleeping probability andcensoring rates are used to maximize energy efficiency.

3.2.5 Distributed cooperative sensing

Distributed cooperative sensing does not require a dedicated fusion center [57], [18]. Instead, each cognitiveradio user can make its own decision for sensing and channel access. In general, iterative informationexchange among CRs converge to a channel availability decision [57]. Each CR user sends (receive) localsensing information to (from) all other CRs in the network. If a decision is not obtain after combining thereceived data, CRs will continue exchanging the combined result until the algorithm converges. Althoughcentralized cooperative sensing has been predominantly studied in literature, distributed schemes addresssome shortcomings of this approach, such as, scalability due to backbone infrastructure.

In [91], address spectrum heterogeneity, where spectrum holes changing with space and time may result indifficulties in maintain a common control channel for communication. In the proposed scheme, CR users formgroups and communicate through dynamically selected local channels. Since a dedicated communicationchannel is not needed, this scheme is scalable and more robust to jamming attacks in control channels.Although, this scheme requires synchronization among CR users resulting in overhead and limited mobility.

A distributed non-transferable coalition game is used to address centralized cooperation overhead andcomplexity in [92]. CR user diversity, where user belong to different service providers, makes it difficult toemploy a centralized structure. Simple split and merge rules are used by each CR to form coalitions. Thedecision to join/leave a group is determined by maximizing individual average utility in terms of probabilityof detection with a cost of false alarm. The proposed model is shown to outperform non-cooperative modelsand the scheme is robust in a network with changing topology.

An incremental gossiping algorithm is proposed in [93] to minimize communication overhead in a dis-tributed system. This approach eliminates the setup phase overhead required to create clusters, and, there-fore, allows for highly mobile strctures. A order and duplicate insensitive (ODI) aggregation technique -more specifically Flajolet and Martin (FM) aggregation - is used to combine sensing data. An FM bit vec-tor, representing local primary signal energy level, is exchanged between nodes and computed using bitwiseOR-ing to obtain the energy level in the system. An Incremental FM aggregate is proposed, which extendsthe FM aggregate by allowing updates to the FM bit vector. This results in lower overhead since it elimi-nated the requirement to recompute the FM bit vector when changes occur. Gossiping protocols, uniformgossiping and random walk, are used for communication between nodes due to their fast convergence time(O(logN) where N represents the number of CRs for uniform gossiping). At each time step, each CR senddata to a randomly chosen neighbor in uniform gossiping. In random walk, at each time step only a subsetof nodes send data to randomly chosen neighbors who in turn become the new subset to send data in thefollowing time step. Incremental variants are used for both uniform gossiping and random walk where onlynodes who experience a signal level change (update) initiate gossiping. Simulation results show that theincremental gossip protocols reduce overhead and execution time where the algorithm converges in O(logN)steps. In [94] the [93] paradigm is extended where weights are assigned to CRs for data exchange based on

18

distance between a source node and an observing node to additionally reduce communication overhead.Byzantine node failure in distributes systems is addressed in [95]. A weighted sequential probability ratio

test is proposed to combat nodes reporting false information. The algorithm is shown to perform with 95%accuracy in an environment with up to 20% of rogue nodes.

In [96] CR power consumption is addressed in distributed cooperative sensing using a partially observableMarkov decision process (POMDP) framework. The scheme considers channel fading and an optimal MACprotocol is designed. It is shown that conserving energy is critical for sensing and access decision only whileCR battery is low.

A consensus based approached is proposed in [97] to model a distributed sensing paradigm. The schemeis scalable since there is no communication with a centralized fusion center. Instead, an algorithm based onexisting consensus schemes, which stem from swarm intelligence, is used for local communication betweenCRs. The scheme is evaluated against existing fusion rules, s.a. the OR-rule to show a better performancein terms of probability of detection and false alarm. The shortcoming of the the proposed approach is therequired knowledge of the number of neighbors in the network which in practice might be hard to obtain. Aconsensus scheme which based on Metropolis weights [98] may be used to address this problem [97].

4 Spectrum Sharing

In addition to sensing capabilities, cognitive radio networks must be able to efficiently utilize spectrum toachieve desired quality of service (QoS). Cognitive radio users must determine which spectrum bands toaccess based on criteria, s.a. bandwidth requirements, probability of primary user absence in the channel,and secondary user contention level. Both primary user interference (coexistence) and interference from othercognitive radio network (self-coexistence) must be considered. In other words, in opportunistic spectrumaccess (OSA) CR users must avoid interference with primary users while finding spectrum holes which arenot exploited by other CR users. Depending on the spectrum sharing technique used, underlay, overlay, orinterweave, different challenges arise. In interweave sharing sensing is necessary while in underlay sharingthe main challenge is to determine the aggregate interference power level. In this section we explore proposedparadigms achieving global optimization for dynamic spectrum access. Selected work based on the multi-armed bandit problem, game theory, and swarm optimization are reviewed in detail. We evaluate eachparadigm based on how well it captures self-coexistence, coexistence, spectrum access fairness, spectrumutilization, switching cost, practical implementation, collision resolution, and imperfect sensing.

4.1 Multi-armed bandit problem

In the multi-armed bandit problem (MAB), agents try to maximize reward by estimating payoff at eachresource through a real-time learning process. The multi-armed bandit problem was originally formulatedas a gambler standing in front of several slot machines, to maximize sum reward, the gambler must decidewhich arm to pull, in what order the arms should be pulled, and the amount of times the arms shouldbe pulled. In cognitive radio networks, secondary users are most often unaware of channel availability andprimary user’s transmission pattern. To maximize throughput, secondary users can learn the transmissionpattern of primary users based on previous experience in a channel. Hence, MAB can be used to describehow cognitive radio networks can learn channel availability in real-time to maximize throughput. In a generaldescription of MAB there are N arms andM players whereM ≤ N , the arms are independent and identicallydistributed 2 over time where the distribution is unknown to the players. Each user chooses an arm whichyield a random reward. The policy of choosing an arm depends on the variant of MAB. The performancemetric most commonly used is regret defined as R = Uopt − U where Uopt is the optimal utility obtainedunder ideal conditions and U is the utility obtained under imperfect knowledge which depends on the chosenpolicy. In the upper confidence bound UBC1 [100] policy, players choose to transmit in a channel with thehighest index based on the sample mean g-statistics defined as

gi,j(n) = Xij(n) +

√2logn

Tij(n)(17)

2In [99] it is noted that i.i.d. representation of primary transmission is idealistic and is only a good approximation iftransmission is highly bursty or if transmission time slots are of sufficient length.

19

where X is the expected reward in arm i for player j and T is the number of times arm i has been pulled byplayer j. The first term in g corresponds to exploitation while the second corresponds to exploration [101].Choosing the channel with the highest g-statistic guarantees logarithmic regret. Since the arm that wouldgive maximum reward is not known a priori, players explore the environment trying to maximize reward.There is a balance between choosing an arm with a reward based on previous observation and searchingfor an arm that might give better results. Since primary user transmission behavior in a band is usuallyunknown and cognitive radios are most often not able to sense all bands at the same time [101], multi-armedbandit problem can be used to study opportunistic spectrum access behavior.

In [99] two distributed MAB spectrum sharing paradigm are proposed and compared. In the first scheme,secondary users use prioritized access to spectrum by establishing ranks within the community. The secondscheme, referred to as random access, secondary users have equal right to access each channel. In prioritizedaccess, communication between users is assumed where users exchange information in order to establish ranks.This communication is done initially and is assumed for the rest of the game. The pre-allocated rankingcan be applied to a heterogeneous cognitive radio network where users have different access priorities. Ifchannel statistics are known and each users knows the assigned user ranking in the system, there would beno collision since users would access the channel assigned to them based on their ranking among other users.It is assumed that channel statistics are unknown leading to a possibility of collisions among users sinceuser might wrongly estimate the channel order. The regret is obtained by collisions in the system as wellas users choosing a channel different from their assigned channel. A greedy learning schemes, where usersstrive to settle in their channels as quickly as possible, is used to obtain logarithmic regret. The shortcomingof the pre-rank paradigm is that a priori knowledge of the channel mean availability is needed since userscan not distinguish between two channels with the same mean resulting in finite probability of collision ineach slot. A distributed fair access paradigm, called ρRAND is proposed where no information is exchangebetween user and the channel means are unknown. Each user follow the UBC1 policy unless a collisionoccurs, where users will choose the next channel at random in the next time slot. In other words, randomchannel selection occurs only if there is a collision otherwise each user will access channels based on theirexploration experience. The random fair-access paradigm achieves logarithmic regret with respect to time. Itis pointed out in [99] that the i.i.d. stochastic reward with unknown means, capturing the overall* behaviorof primary users in the system, only represent bursty primary users or long transmission time slots.

An extension of [99] is investigated in [102] where a ranking paradigm without prior knowledge of meanavailability is shown to achieve logarithmic regret. In addition, a fair access paradigm is derived whichachieves order-optimal regret with respect to users and channels. A more practical representation of thecollision model is used. If collision occur, either exactly one of the users receive the reward (CSMA) ornone of the users receive reward. Assuming that users access a resource with the highest mean rewardcorresponding to their rank in the pre-rank paradigm. Simulation results show that fair access performsbetter than prioritized access model since in the fair model each user has an equal chance to use one of thebest channels and will explore the best channels equally resulting in less regret and less collisions.

In [103] show that a switching cost incorporated in the multi-armed bandit problem using a block basedchannel access policy (BCA) achieves logarithmic regret. The switching cost is motivated by operationalparameter configuration delay, packet loss, and protocol overhead for SU Tx-Rx coordination. Secondaryusers must find channels unoccupied by primary users while limiting the contention with other secondaryusers in the system and at the same time avoiding excessive channel switching. It is assumed that onesecondary user can transmit in an idle channel with a utility gain of 1. If a collision occurs between secondaryusers, neither player can transmit its data resulting in a utility of 0. Time slots are grouped into blocks. AnSU will remain in the same channel for the duration of the block, unless a collision occurs, forcing each SUinvolved to switch to another channel chosen at random. Total utility for the system with N channels andM SUs where M ≤ N is defined as

U(n) =

M∑i=1

Uj(n) (18)

where Uj(n) is the total utility for SU j after n synchronous time slots defined as

Uj(n) =

N∑i=1

µi E[Vi,j ]− Sj(n) (19)

20

where Vi,j is the number of time slots, up to n time slots, where SU j in channel i does not experiencecollisions, Sj(n) is the switching cost for SU j, and µi is the i.i.d. probability that channel i is idle. Theregret is defined as the reward loss after n slots due to uncertainty in channel availability, SU competition,and channel switching, i.e. R(n) = U∗(n)−E[U(n)] the expected utility loss with respect to an upper boundof U(n) defined as

U∗(n) = n

M∑j=1

µj (20)

assuming that expected probability of channel j = 1 being idle is larger than that of channel j = 2 and soon. To achieve logarithmic regret, i.e. regret of order O(log n), a block access mechanism is used to controlswitching cost and collision loss growth. Time slots are grouped into blocks. An SU commits to one channelduring each block. If a channel is idle but a collision occurs, SUs switch to another channel at random. Sincethere is no a priory information about PU channel access statistics, each SU maintain a history of channelavailability. The channels are accessed based on the sample-mean based g-statistics proposed by [100]. Thechannel with the highest g-statistics is chosen where the g-statistics depend on sample mean of the channeland the number of time the channel has been sensed. The regret of the block based policy

RB(n) ≤ µ1[N∑

i=M+1

M∑j=1

E[Ti,j(n)]] +M E[Y (n)] + E[S(n)] (21)

where T(n) is the number of slots that each SU chooses channel M+1 to N* (the time spent in the worstchannels), Y(n) is the number of collisions involving at most M SUs, and S(n) is the switching cost. Allthree terms are show to have an upper bound of order O(log n), hence the block based policy has logarithmicregret. To avoid synchronization overhead, the proposed block based access policy can be extended to anasynchronous policy while maintaing logarithmic regret. Simulation comparing three schemes, synchronous,asynchronous, and a random access policy without switching cost proposed in [99], show that the asyn-chronous block access policy outperforms both the synchronous and random access paradigms. It is shownthat the number of collisions effect the regret more than the time spent in the worst channels. Since in theblock based approach with switching cost SUs spend more time in the worst channels but experience lesscollisions, the paradigm outperforms the random access paradigm without switching cost. When switchingcost is increased, the proposed paradigm performs increasingly better than the paradigm without switchingcost since SU become less willing to switch bands when the cost is high. In addition, the asynchronousparadigm with switching cost outperforms the synchronous paradigm since blocks access is asynchronousresulting in fewer collisions. The proposed paradigm address estimating primary user activity in a chan-nel, distributed SU coordination to avoid collisions, and the costly action of switching bands. Althoughcoexistence is addressed, in practice, the number of channels will seldom exceed the number of competingsecondary users, i.e. self-coexistence is not well captured.

In [104], propose a multi-armed bandit problem with a time-devision fair sharing policy. Multiple SUsshare the same channel under two collision models, neither player gets an reward if collision occurs orall players sharing a channel receive a partial reward. The time-devision fair sharing scheme proposed isdecentralized and does not require information exchange between players. It is shown that the paradigmhas the same logarithmic regret as a centralized version of the paradigm. Collision history is used toorthogonalizing players into different time sharing offsets. This differs from the scheme proposed in [99]where players are orthogonalized to different channels [104].

The knowledge of the number of secondary users in the system is often assumed. In [105] an MAB problemis formulated where the number of secondary users is estimated during the learning phase. Although thenumber of users is assumed to be unknown, it stays fixed and does not exceed the number of channels. Theworst case, used to estimate regret, arise when the number of users is equal to the number of channels. Theestimate of number of users is based on the number of collisions experienced in each channel. After learningchannel statistics players tend to access only a subset of channels, i.e. the best channels. If the estimationof the number of users is below that actual number of players in the system, since other users will access asimilar subset of channels, the probability of collision will be high among users. If the collision count exceedsa threshold based on the number of estimated user and time, the estimated number of users is updated and

21

collision count set to zero. It is shown that the access policy with learning the number of players is nearlyorder optimal in terms of regret.

In the MAB, secondary users learn the behavior of primary users and select channels accordingly. Inthe approaches described above many challenges and important factors in OSA are addressed, for example,heterogeneous secondary users, fair access, switching cost, collision resolution, and secondary user contentionlevel. Although, the MAB problem may represent different seniors occurring in opportunistic spectrumaccess, it does not capture the competition between cognitive networks well [101]. In MAB the assumptionthat there are more channels than users is most often made, while in practice, the number of users willalmost always exceed the number of channels.

4.2 Game theory

In mathematics game theory is a well defined tool studying multi-user systems. Although first appliedin economics, it has been applied in many fields of study and more recently used to describe coexistenceand self-coexistence in cognitive radio networks. Comprehensive surveys on game theory in cognitive radionetworks are given by [106], [107], [101].

4.2.1 Static game

In [108] a modified minority game (MMG) is proposed to study the dynamics between 802.22 networks (seesection 5) in opportunistic spectrum access. It is assumed that overlapping CR networks belong to differentservice providers competing over a limited number of channels. Each network seek a channel free frominterference, where the dynamic channel switching is modeled using MMG. Although CR do not exchangestrategy information, MMG requires knowledge of the number of players in the system. Broadcasting beaconsis proposed to obtain this information. Modeling CR self-coexistence using MMG is advantageous in that thegame works for a distributed network and is therefore scalable. Minority game theory is a non-cooperativegame, where players compete over resources without negotiation. In the El Farol bar problem, the classicalminority game defined in [109], n people simultaneously and independently decide whether to attend the ElFarol bar on a given night. Players will only enjoy going to the bar if it is not too crowded. Each playerhave decide whether to go or not to go, without the knowledge of other users strategies. If all players decidenot to go, the bar will be empty. If all players decide to go, the bar will be overcrowded. A“always greedyand profit seeking” model is assumed. Both pure and mixed strategies are investigated. Pure strategy set isa binary choice defined as

S = {switch, stay}. (22)

A CR network may choose to stay in a channel hoping that the other networks will switch or a networkmay choose to switch hoping that the other networks will stay in the channel. The game is played until allnetworks find channels free from interference. At each desecrate time step if a network i strategy decisions = “stay” in channel j one of three situations occurs:

1. All other network in channel j choose strategy s = “switch”, leaving network i as the sole occupant ofchannel j.

2. All other network in channel j choose strategy s = “stay”, resulting in no advancement in the gameand thus repeating the original stage.

3. Some network in channel j choose strategy s = “switch” and some choose strategy s = “stay”, resultingin a subgame of the original stage since the game continues until all networks find an empty channelfor transmission.

The objective is to minimize cost, define in terms of time units until a clear band is found by a network, andto possibly find Nash equilibrium. Nash equilibrium will occur when there is no gain in changing strategy fora network if other networks keep their strategies assuming rational players. To investigate Nash equilibriumtwo networks are assumed. The four possibilities that occur depending on the strategy chosen by eachnetwork are shown in table...

22

i/j switch stayswitch (c,c) (c,0)stay (0,c) repeat original game

Hence, the pure strategy minority game does not achieve Nash equilibrium resulting in a sub-optimal solutionsince the dominant strategy for both networks is to stay and the original game has to be repeated at acost. Additionally, a mixed mixed strategy game is formulated to evaluate the performance of the proposedspectrum sharing scheme. In a mixed strategy game choosing to stay or switch is done with some probabilityp

Sm = {(switch = p), (stay = 1− p)}. (23)

A closed form expression for p is defined as

p =

(1

1 + cf(N,M)

) 1N−1

(24)

where N is the number of networks, M is the number of channels, and cf(N,M) is the cost function growingat a multiplicative rate justified since cost varies with the ratio of channels and bands. Equation (24) isobtained assuming that rational players and an equilibrium point can be obtained if the expected cost ofswitching is equal to the expected cost of staying for a network i

E[Ciswitch] = E[Ci

stay]. (25)

where the expected value for network i to switch is reduces to

E[Ciswitch] = cf(N,M) (26)

since

E[Ciswitch] =

N−1∑j=0

Qjcf(N,M). (27)

where

Qj =

(N − 1

j

)pj(1− p)N−1−j (28)

The expected cost of staying in a band is defined as

E[Cistay] =

N−2∑j=0

Qj(1 + E[Ci(G′(N−j))]) +Q(N−1) · 0 (29)

where E[Ci(G′(N−j))] is the expected cost during subgame G′

(N−j). The value of p is found to be finiteand non-zero, hence, there exist a equilibrium point for the mixed strategy game. The authors go on toobtain a value for p in a generalized MMG model where networks start the game in different channels. Nashequilibrium is shown to exists for the generalized MMG model. Simulation result show that there in factexist a Nash equilibrium when varying the number of networks and bands. In addition, it is shown thatmixed strategy always outperforms pure strategy and that the cost, time to find a band free of interference,follows a multiplicative behavior depending on the number of networks compared to the number of bands.In [108], the behavior of competing networks is captured where each SU chooses a strategy based on thestrategies chosen by other SUs. Although, a shortcoming of this approach is that the number of networksN is assumed to be less than the number of channels M in order for the paradigm to converge. This isseldom the case in practice. Most often, to achieve a steady state, information about SUs in the systemis required resulting in overhead communication. Moreover, nodes deviating from the strategy set is notaddressed in [108].

Moreover, in [110] the proposed paradigm allows a greater number of secondary users compared to chan-nels. In [110], inspired by human societies, coalitions are formed and individuals in the group (insiders)works together to achieve greater benefit where the groups wellbeing is evaluated higher than that of indi-viduals that are not part of the group (outsiders). This concept is applied to DSA networks in [110] where

23

profit seeking secondary users, based on some commonality, for example, belonging to different manufac-turers, form coalition to achieve higher transmission rate. Secondary users not part of the coalition areunaware of this collaboration, assuming that all secondary user on the system are playing fair. To study thisconcept, [110] begin by defining a non-cooperative game with selfish rational users where NE is achieved.Thereafter, a set of greedy secondary user agree to deviate from the strategy set and form a coalition. Sinceforming coalitions requires collaboration, the game becomes cooperative. Similarly to [108], secondary usermake an independent stochastic decision whether to stay in the current band or switch to a different band.Each SU has a choice to stay in the current channel with probability (1− p) or switch to a different channelwith probability p. There are n homogeneous SUs competing over M homogeneous spectrum bands. In thismodel, spectrum bands are orthogonal and secondary users can share a band. The reward function R(K)for a SU s is the transmission rate obtained when sharing the spectrum band with K other SUs

R(K) = Blog2

(1 +

GsPs∑Ki=1 Gsi Pi + ω

)(30)

where Ps and Pi is the transmission power for SU s and i respectively, B is the channel bandwidth, Gs

represents the channel gain for the transmission of s, Gsi represents the channel gain between transmissionsfor SU s and another SU i, and ω is the white Gaussian noise. The expected payoff for a SU s if s choosesto stay in the current channel is defined as

U(stay) =n−1∑i=0

Qstay(i) · R(i)

where R(i) is the reward function and Qstay follow a binomial distribution that i out of n− 1 other SUs alsochoose the strategy to stay. If a SU s decides to switch to another channel the expected payoff is defined as

U(switch) =

n−1∑i=0

i∑j=0

Qswitch(i) · T (j ) · R(j )− c

where Qswitch follow a binomial distribution that i other SUs choose to switch, T (j) is the probability thatj out of the i that switched, switch to the same spectrum band as SU s, and c is the cost for switching todifferent channel. Nash equilibrium is obtained when U(stay) = U(switch). At this point, all secondaryusers know that all other secondary users in the system follow the equilibrium strategy, hence, there is noincentive to change strategies. In the next part of the game a group of users agree to simultaneously changetheir strategy, without notifying other users, in order to increase their profit. This divides the secondaryusers society into two groups: secondary users following NE (outsiders) and secondary users deviating fromNE (insiders). Insiders, the parochial community, do not share information with outsider, i.e. outsiders areunaware of the existence of a parochial community. Since the parochial community collaborate, the gamecan no longer be defined as non-cooperative. The utility of switching U(switch) and the utility of stayingU(stay) are adjusted to incorporate parochialism. Assuming m out of n secondary users forming a parochialsociety. Insiders switch with probability q and outsiders switch using the NE strategy with probability p∗where q = p∗. The expected reward for staying in the current band for a SU s′ in the parochial communityis defined as

U ′(stay) =

n−m∑l=0

m−1∑k=0

Qstay(l, k) ·R(l + k), 0 ≤ l ≤ n−m, 0 ≤ k ≤ m− 1. (31)

where Qstay(l, k) follow the binomial distribution that k out of m insiders stay and l out of n−m outsidersstay in the current band

Q′stay(l, k) =

(n−m

l

)(1− p∗)lp∗n−m−l ·

(m− 1

k

)(1− q)kqm−1−k (32)

and R(l + k) is the reward function ?? where SU s′ share the current band with l + k secondary users. If aSU s′ decides to switch channels, the expected payoff is defined as

U ′(switch) =n−m∑l=0

m−1∑k=0

l+k∑j=0

Q′switch(l, k) · T ′(j) ·R(j)− c (33)

24

where Q′switch follow a binomial distribution that l + k other SUs choose to switch,

Q′switch(l, k) =

(n−m

l

)p∗l(1− p∗)n−m−l ·

(m− 1

k

)qk(1− q)m−1−k, (34)

T (j) is the probability that j out of the SUs that choose to switch, switch to the same spectrum band asSU s′,

T (j ) =

(l + k

j

)1

(M − 1 )j· (M − 2

M − 1)l+k−j , (35)

and c is the cost for switching channels. The maximum payoff for SU s′ is defined as

Ue(q) = (1− q·U′(stay) + q · U ′(switch). (36)

with optimal strategy for the parochial society q∗ is defined as

q∗ = argmaxq∈[0,1]

Ue(q). (37)

Depending on the size of the parochial society, simulation results show a significant payoff increase (10%-40%)for insiders. Moreover, the payoff for outsiders decreases with the increase in payoff for insiders governedby the size of the parochial community. The formation of a parochial society breaks NE and outsidersare negatively affected with respect to payoff. Hence, the larger the size of the parochial community, themore influence the group has on the outsider society. The scheme in [110] captures self-coexistence inDSA networks with an added level of complexity inspired by human societies. In practice, it is not alwayspossible to assume that heterogeneous secondary users belonging to different “societies” will follow the sameset of rules. Moreover, a more realistic representation of DSA networks where the number of secondaryusers exceed the number of channels is assumed, compared to [108] where channels exceed secondary users.Forming groups and following the same strategy implies coordination between node with a communicationoverhead. In [110], how secondary users communication, in-band CCC, out-of-band CCC, random CCC etc,is not addressed. Although, to minimize overhead, signaling in the parochial community is to establish thestrategy set but there is no communication between node in regards to channel selection.

In the schemes described above interweave sharing is defined while addressing secondary-secondary dy-namics, non-cooperative game, switching cost, underlying architecture, and secondary users deviating fromNE strategy. The main shortcoming of a static game is that it is played for one step. In opportunisticspectrum access, self-coexistence dynamics and coexistence dynamics change over time. Hence, it is difficultto capture the evolution of user dynamics using static games.

4.2.2 Repeated game

Repeated game theory allows for the game to continue over time as opposed to a static game where the gameis played for one step. Players have an ability to learn from past experience. In repeated games, a balancebetween future reward and immediate reward determines the player’s strategy. Players are assumed to beselfish. Although, since a players current strategy might affect the future strategies of the other players,there is an incentive to establish trust and cooperation [106]. Cooperation strategies, s.a. grim trigger,punish-and-forgive, tit-for-tat, fictitious play, etc., are commonly used to encourage selfish players to teamup [106]

- Grim trigger: In this approach the game starts in a cooperative state. This state is maintained by theplayer as long as all players continue cooperating. The game enters a punishment state if a player breaksthe trust and deviates from the strategy set. In this state, the deviating player will be punished by theother players. The game will stay in the punishment state and players will not resume cooperation.

- Punish-and-forgive: This variation of the game differs from “grim trigger” in that it allows players toresume cooperation once the punishment state has been applied for some time.

- Tit-for-tat: Players learn from previous actions. Cooperation will be adopted by a player if all the otherplayers cooperated in the previous step.

25

- Fictitious play: Similar to “Tit-for-tat” players will choose their strategies based on previous experience.In this scheme, players combine the history to make strategy decisions as opposed to just looking atthe previous state.

In [111], a repeated game is compared to a one step game. It is shown that the repeated game, withpunishment using the grim trigger etiquette, outperforms the static game (Gaussian Interference Game -GIG) assuming perfect and complete information for both. It is shown that the static game only achievesNash equilibrium using pure strategy. The repeated GIG game is advantageous since it result in manyequilibriums where either one can be used in order to set the access policy regarding power limit. It isfurther shown that it is possible to detect a deviating player using various techniques. Although, this resultsin an increased information exchange overhead. The fair policy, achieved using punishment, is comparable toa cooperative scenario depending on the SNR level. The grim trigger paradigm might result in performanceloss in systems with imperfect information since the punishment stage might be entered due to measurementerrors resulting in QoS degradation for all secondary users, as mentioned in [111].

In [112] a repeated game with imperfect monitoring is investigated assuming the less harsh punish-and-forgive strategy. Self-coexistence is formulated in a opportunistic setting where secondary user have tostay within a temperature limit not to interfere with primary users. Imperfect monitoring refers to limitedand erroneous observation of the aggregated interference temperature. Using time devision multiple access(TDMA), time-varying power levels are achieved. Time-varying power levels are important in a multi-usersystem sensitive to change in power levels where one user transmitting at a higher power might degrade theQoS of other users. Since the power levels are often not static in practice, where there will exist opportunitieswhere users may increase their transmitting power, the dynamics of power levels must be incorporated inthe sharing etiquette. In addition, a deviation-proof sharing etiquette is obtained. The game assumes selfishrational players. Players’ QoS will not improve when deviating from the proposed etiquette. In other words,it is in the players interest to obey the rules. If a player deviates form the sharing policy or if there is an errorin the temperature limit information, all players will enter a punishment state where players transmit athigher temperature levels degrading the QoS for all players in the system. It is shown that proposed scheme,being deviation-proof with time-varying power levels, can achieve some Pareto optimal points of time-varyingschemes but outperforms schemes with constant power levels and perfect information. Moreover, it is shownthat the paradigm with time-varying power levels is Pareto dominant compared to paradigms with constantpower levels. In this sharing scheme the aggregated temperature level is assumed to be regulated by oneentity in the system, a local spectrum server (LSS), which will send a binary decision informing to the otherplayers of any deviations both due to deviant nodes and errors. The overhead of the information exchange isshown to be predetermined. The paradigm follows the punish and forgive etiquette. Due to imperfect powerinformation, the punishing phase is carried out with some positive probability.

In [113] a punishment game is formulated for the current 802.22 standard assuming dense secondary userdeployment. The difference to the above mentioned approaches is the assumption of a centralized network.The focus in this work is to account for both throughput and contention, i.e. aspects of two separate layers.

Repeated game captures secondary-secondary dynamics over time. The aggregate power level is ad-dressed where secondary users transmission power depends on their cooperation with other secondary users.Cooperation requires information exchange resulting in an additional communication overhead and delay.It is not clear wether there is a in-band or out-of-band communication channel. Hence, often practicalimplementation challenges are not addressed.

4.2.3 Stackelberg game

The Stackelberg game is a hierarchical model applied in economics describing a game with leader and followers.The leader of the game will make a move knowing that the followers are observing. The followers will choosetheir strategy after the leader’s decision competing with other followers to maximize utility. If the Stackelbergequilibrium is reached, neither the leader nor followers will have an incentive to deviate from their strategyset.

In [114] a model in which primary users, instead of being unaware of secondary operation in their channel,improve their transmission by cooperating with secondary users. The model is described using a Stackelberggame framework where primary users are the leaders and secondary users the followers. Primary users leasetheir spectrum to secondary users in exchange for improving their QoS by choosing secondary users for

26

relaying the transmission to primary receiver. Secondary users may choose whether to cooperate with theprimary user. A distributed competition arise between secondary users in the system since secondary usersdo not cooperate with each other. In the assumed topology there is one primary transmitter PT , one primaryreceiver PR, and an ad hoc secondary user network ST consisting of K transmitters and K receivers. Ineach time slot the primary user decides whether to use the slot for its own transmission or cooperate with asubset of secondary users S ⊆ ST to possibly improve transmission rate where |S| = k ≤ |ST |. A time slot(0 ≤ α ≤ 1) is divided into two parts, α is used for data transmission and (1−α) is used for communicationbetween the primary user and secondary users in order for the primary user to establish a potential subset ofcooperation secondary users. The α portion of time is further divided up using parameter (0 ≤ β ≤ 1). Oneportion αβ is used by the chosen subset of secondary users S to relay primary data transmission. The otherportion α(1− β) is used for secondary data transmission where no cooperation or coordination is assumed.It is assumed that relaying is done using distributed space-time coding (DSTC) [115]. Two different casesare investigated based on the channel state information (CSI). In the base line case (instantaneous CSI)it is assumed that the primary user is aware of all power gains over fading channels and secondary usersare aware of power gains in the secondary network. In a more realistic case (long-term CSI), primary andsecondary users only have statistical information of the fading channels.

In the base case, the primary user will choose α, β, and S ⊆ ST in order to maximize its transmissionrate R, i.e. maxα,β,SR(α, β, S) where S ∈ ST ,0 ≤ α, β ≤ 1, defined as

R(α, β, S) =

{RD for α = 0RC for α > 0

(38)

Hence, the primary user can choose whether to cooperate and thereby leasing portion of its spectrum forsecondary transmission. If the primary user choose not to cooperate with secondary users, i.e. α = 0 (thereis no spectrum time leased to the secondary users), S = ∅, and there is a direct link between primary usertransmitter and receiver, the transmission rate in bits/symbol is defined as

RD = log2

(1 +

|hP |2PN0

)(39)

where P is the primary transmission power, hP is the known power gain from primary user transmitter tothe primary receiver over a Rayleigh fading channel, and N0 is the spectral density of the independent whiteGaussian noise at the primary receiver. The primary user decision that its transmission rate will benefitfrom cooperation, i.e. α > 0 for S out of k active users, depends on the minimum between two terms wherethe first term is the transmission between the primary transmitter and the k secondary transmitters and thesecond term is the transmission rate between k secondary users and the primary receiver where a decodeand forward multi-hop space time coding [116], [115] is assumed

RC(α, β, S) = min{(1− α)RPS(S), αβRSP (α, β, S)} (40)

where RPS is defined as

RPS(S) = log2

(1 +

mini∈S |hPS,i|2PN0

)(41)

where |hPS,i|2 is the channel gain between the primary transmitter and the worst channel in subset i ∈ S.Assuming orthogonal channels the transmission between subset S of secondary users and the primary receiveris defined as

RSP (α, β, S) = log2

(1 +

∑i∈S

|hSP,i|2Pi(α, β, S)

N0

)(42)

where |hSP,i|2 is the channel gains between secondary transmitters and primary receiver and P is the powersused by secondary users for transmission. It is assumed the transmission powers of secondary users owndata transmission is the same as the transmission powers for the transmission between secondary users andthe primary receiver. Hence, the cooperation power rate P depends on the non-cooperative game betweensecondary users where Nash equilibrium is reached. The primary users will choose a strategy by optimizingparameters (α, β, S) to maximize its reward while being aware of how this will affect the secondary users

27

strategies or the set of transmitting powers Pi(α, β, S). Hence, in a Stackelberg game the primary user isthe leader and secondary users are the followers. Once the secondary users have sent the primary users datato the primary receiver, the secondary users can transmit their own data. The secondary network consistof secondary transmitters and receivers. It is assumed that the secondary network is non-cooperative. Eachsecondary user tries to maximize its own reward knowing α, β, and S by acting rationally and selfishly. Theutility Ui for a secondary users i is defined as the transmission rate between secondary user’s i transmitterand receiver α(1 − β)Ri(Pi,P−i) and the cost for transmitted energy c · αPi. Since each term in Ui ismultiplied by α the utility can be written as

Ui(Pi,P−i) = (1− β)Ri(Pi,P−i)− c · Pi (43)

where Pi is the transmission power of secondary user i, P−i is the transmission power of all user in set Sother than i, and Ri is

Ri(Pi,P−i) = log2

(1 +

|hs,ii|2Pi

N0 +∑k

j=1,j =i |hs,ij |2Pj

). (44)

It is shown that Nash equilibrium exists in the non-cooperative secondary user game ⟨S, P, Ui(Pi,P−i)⟩ andit is unique provided that the following condition holds∑

j∈S,j =i

|hs,ij |2

|hs,ii|2< 1. (45)

This upper bound on interference is set to reach a unique NE since the transmission between secondaryusers transmitter and receiver are assumed to be one to one links and each utility Ui versus Pi has a uniquesolution [114]. It is shown that the a near-optimal outcome can be obtained when selection the subset s ∈ Susing linear complexity in K instead of 2K considering every subset. Numerical results show that the optimalvalue of α decreases with increased distance between the primary user transmitter and the secondary userssince it takes more time (1 − α) for the primary user to communicate with secondary users. It is furthershown that multiuser space diversity is beneficial when the distance is large since it is better to cooperatewith a few secondary users with the best channel gain between the primary transmitter and secondary user.If the distance between the primary transmitter and secondary user is small it is better to exploit largerpower gain by cooperating with a larger number of secondary users. The base case model is extended to amore practical model, the long-term CSI. The channel gain is no longer known resulting in a probability ofoutage POUT . Only a random number of secondary user in S are able to decode the primary user message.The primary user is not able to choose the space-time codebook with specific codeword to be transmitted byeach secondary user. A randomized DSTC is used to overcome this problem. The primary user will send acodebook to all secondary user and each secondary user belonging to the cooperation subset s will choose acodeword independently at random. The space-time codebook Ci has three dimensions of codewords, spatial,temporal, and rate, chosen from a predefined set C which enables the primary user to choose a codebookwhere the codeword options are much larger than the the number of secondary user and the probabilityfor two secondary user to choose the same codeword is smaller. The primary user objective is to minimizethe outage probability POUT (α, β,Ci) which depends on the estimation of channel fading statistics and thecodebook chosen by the primary user. Numeric results show that the optimal choices for α and β dependson the secondary user distance to the primary transmitter which turn out to have the same behavior inboth models, instantaneous and time-varying, where leasing time decreases with increasing distance. Theadvantage of this leasing model, as noted in [114], is that the incentive for primary user to lease spectrumto secondary users only depends on the improvement of primary user QoS and does not involve any leasingfees. One shortcoming of the model is that there most exist a dedicated communication channel in order tocoordinate relaying primary user data.

The paradigm in [114] is extended in [117] where the authors argue that the primary user might notalways have data that needs relaying and therefore the primary user loose its incentive to cooperate withsecondary user. In the proposed model, primary users will choose a subset of secondary user to relay itsdata as in the scheme proposed in [117] . The subset of secondary user may choose to cooperate and inreturn receive a fraction of spectrum time for a payment made to the primary user proportional to theaccess time. Primary users objective is to both maximize its transmission rate and the revenue obtained

28

from secondary users. Secondary users must balance the desired maximum transmission against the leasingpayment to the primary user. Both the primary user and secondary users are assumed to be rational andselfish. There is no cooperation between secondary users, and, therefore, secondary users must competewith each other. A non-cooperative game is formulated which is shown to have a unique NE under certainconditions. Using the Stackelberg game framework, primary users will determine the subset of secondaryusers s, time for relaying α, and left over time for secondary transmission β knowing that the secondary userswill observe these choices and decide whether to cooperate. Rayleigh block fading channels are assumed.The primary transmitter will periodically probe the primary receiver and secondary transmitters for channelconditions. Once the optimal parameters s, α, and β are calculated, the primary transmitter will broadcastthe parameters, piggybacked on data packets, to all secondary users in order for them to calculate theirexpected utility. Secondary users access is based on TDMA as opposed to [114] where secondary users canaccess the channel at the same time using power constraints. Numerical result show the utility function forthe primary user over the distance d between primary user transmitter and secondary users transmitters.With a fixed number of cooperating secondary users, the cooperating scenario with optimal parameters UC

outperforms both the scenario where the primary user decides to use the entire time slot for its own directcommunication with the primary receiver UD and when the primary decides to lease the entire time slot tosecondary users for a fee instead its own data transmission U0. Additionally, it is shown that primary user’sutilities UC and U0 increase simultaneously when the subset of relays increases and the distance is constant.Utility UD is independent of the subset size since more relays does not increase the direct transmission rate.

In [117]a similar formulation of the cooperative relaying model to both [114] and [117]. What differentiatethis approach to the other two approaches is that the pricing model is based on an exchange between powerand spectrum. In [117] the incentive for primary users, in addition to transmission rate, is based on a leasingfee paid by secondary users. Although, in [114] the incentive for primary users to cooperate is not based ona leasing fee, secondary users’ power levels are assumed to be constant. In [118] as opposed to [114], [117]secondary users’ power levels are varying where each user chooses its level and a maximum aggregated powerlevel constraint is met. In the proposed paradigm, the primary users objective is to maximize its transmissiontime slot, i.e. not give too much time for secondary transmission, but at the same time encourage secondaryusers to use a higher power level in order to enhance its own transmission rate. Secondary users aim tobalance the profit in terms of its transmission rate and the cost in terms of power consumption where eachsecondary user can choose to cooperate and decide the appropriate power level for cooperation. It is shownthat Stackelberg equilibrium exists for the proposed access etiquette and that the cooperative model is shownto outperform models without cooperation.

Stackelberg formulation captures the hierarchal nature of dynamic spectrum access. Allowing secondaryusers to transmit alongside primary users, if chosen for cooperation, indicates potential use in overlay spec-trum sharing. One of the shortcomings of the schemes is that the coordination between primary user andsecondary users results in communication overhead. The proposed paradigms do not address secondary userspacial mobility where a subset of secondary users might never be chosen by the primary user for relaying.Moreover, similarly to a static game, Stackelberg game does not represent the evolution of network dynamicsin that it is played for two steps.

4.3 Swarm intelligence

In many proposed cognitive radio paradigms is the need for a central infrastructure or base station. Inaddition, coordination often needed between cognitive radio users result in communication overhead andtransmission delay. These challenges can be addressed using bio-inspired paradigms, such as, swarm in-telligence. Swarm intelligence optimization has been used in many branches of computer science. It hassuccessfully been applied to routing in computer networks. The two main swarm intelligence paradigmsare ant colony optimization (ACO) and particle swarm optimization (PSO). These paradigms represent acollective behavior of self-organizing social insects. Ant colony optimization captures the dynamics of for-aging ants and is the most commonly used paradigm. Global complex tasks are solved using simple localrules applied by each ant. Except for indirect communication, in the form of pheromone trails, there isno coordination amongst the population. Ants perform a random search for food. If an ant finds a foodsource, it will return to the nest using its own path and mark it by emitting pheromone. Other ants willstochastically follow a pheromone trail. An increased number of ants following the same path intensifies the

29

pheromone trail. Since pheromone evaporate over time, ants will not follow the first path found to the foodsource, but rather, the shortest path to the food source. Particle swarm optimization similarly provides adistributed society where local interactions solve a global problem. In PSO, each particle randomly movesthrough a search space with a velocity which is updated based on the particles best performance as well asthe leaders best performance. In the following two section, proposed swarm intelligence based algorithms aredescribed that capture primary-secondary dynamics and secondary-secondary dynamics for both interweaveand underlay spectrum sharing.

4.3.1 ACO

The heterogeneity of cognitive radio users can be captured using swarm intelligence where each individual inthe swarm will performing tasks which they are better equipped for, i.e. division of labor. This is referred toas adaptive task allocation. In [119] spectrum sharing is addressed using a adaptive task allocation model ofan insect colony. The proposed biologically-inspired algorithm (BIOSS) is distributed and therefore scalable.In addition, there is no coordination or information exchange between nodes, i.e. no information exchangeoverhead or need for an dedicated control channel. The algorithm is based on collective intelligence of anant colony where each ant perform a task most suitable to its ability. If a task stimuli s reaches a thresholdθ an ant is triggered to perform the task. The stimuli can be pheromone deposited by other ants indirectlycommunication that a task has to be performed. The probability of performing a task is defined using thethreshold response function

Tθ(s) =sn

sn + θn, n > 1. (46)

The following mapping is used to apply adaptive task allocation optimization for foraging ant societies tocognitive radio networks

- Insect → Cognitive radio user

- Task → Channel not occupied by primary user

- Stimuli → Power level allowed in channel Pj

- Response Threshold → Required transmission power pij

The probability of performing a certain task, in this case selecting a channel for transmission, is adjusted asfollows:

Tij =Pj

n

Pjn + αpijn + βLij

n , n > 1. (47)

where Lij is the learning factor of channel QoS based on previous experience by the cognitive radio, and αand β are positive constants. The BIOSS algorithm is defined as follows:

- It is assumed that the primary users can start/stop transmission at any desecrate time step. In each timestep, sensing is performed by cognitive radio users to find available channels. The sensing techniqueused depends on the environment, CR capabilities, etc.

- Each CR makes an estimate of permissible power level in each available channel Pj , ∀j

- Each CR initiates the learning factor for all channels Lij , ∀j to some initial value s, where learning coeffi-cients ξ0, ξ1 are applied to the learning factor depending on the QoS requirements of the CR comparedto QoS estimate for channel j, i.e. CRs will learn or forget a channel depending on the QoS. If channelj does not satisfy the QoS requirement for CR i then the learning factor is set to (Lij + ξ1). If channelj is satisfactory to CR i then the learning factor is adjusted to (Lij − ξ0).

- Each CR computes the probability of channel selection for all channels Tij , ∀j.

- The channel with maximum probability for desired QoS Tij is selected. The learning factor is updated,(Lij = Lij − ξ0), if the channel selected meets the required QoS. If the selected channel does notmeet the QoS for CR i, the channel is forgotten by updating the learning factor (Lij = Lij + ξ1).It is assumed that CRs can transmit in multiple noncontiguous channels (channel bonding). This

30

is beneficial in that it allows for uninterrupted transmission if the PU reappears in the channel, i.e.channel hand-off. In this case, CRs choose the channels with maximum Tij and increase the numberof channels until QoS requirements are met.

- If there is any change in the environment, for example if the PU start transmitting, the allowed power ina channel is adjusted Pj = 0 and the learning factors are reinitialized Lij = s, ∀j.

- If a change, other than spectrum handoff, occurs in the environment, the channel selection probabilitiesare recomputed Tij ,∀i, j.

The algorithm permits cognitive radio users to select channels based on their QoS needs in a dynamicallychanging environment without coordination. The power level allowed in a channel decreases with increasedinterference and will be chosen by CRs who only need low power levels for their transmission. This results ina fair distribution of channels in a heterogeneous environment. Simulation are performed for single channeland channel bonding separately. Primary user are assumed to start transmission in their channel at random.The number of channels changes between 5 and 25 while the number of secondary users changes between 5and 50. Hence, the maximum ratio of user per channels is 10:1. It is shown that the power level at eachchannel converges after t = 25s. It is also noted that the power level will not fully converge due to thedynamic nature of primary transmission. Although, secondary users will quickly learn which channels aremost appropriate for their power level shortly after each PU interruption. Furthermore, it is shown thatoverall spectrum utilization is not affected when the number of available channels increases and the numbersecondary users is constant. The steady channel utilization rate, at above 50%, shows that the proposedspectrum sharing etiquette allow secondary users maintain uninterrupted communication. In the multi chan-nels case, secondary user power level in each channel converges after t = 15s compared to single channelwhere the convergence happened after t = 25s. Oscillation due to spectrum handoff is minimal using channelbonding. Multi channel outperforms single channel in overall channel utilization. However, it is shown thatthe single channel algorithm outperforms some proposed distributed algorithms where coordination amongsecondary user is required with respect to spectrum handoff delay.

In [120] an enhanced BIOSS algorithm is proposed extending the BIOSS algorithm in [119] to improveoverall channel utilization. This is achieved by addressing one of the shortcomings of BIOSS, namely thatall CRs choose channels with maximum power resulting in excessive conflicts. Due to the nature of thethreshold response function Tθ(s), insects rush to the task with highest stimuli and only move on to thenext task once this task is near finished. Since the threshold response function Tθ(s) is applied directly inthe BIOSS paradigm, spectrum allocation fairness and convergence time suffers. Instead, [120] modify taskprobability Tij in [119] where each cognitive user selects channels with minimum power excess over its powerrequirements. The channel selection probability function is redefined as

Tij =

{0 , Pj < pi1− Pj

n

Pjn+αpn

ij, Pj ≥ pi

(48)

where if the power Pj in a channel j meets the QoS requirements for CR i with power requirement pi, Tij iscalculate using parameter defined in [119]. In contrast, if the power requirement pi for CR i is not satisfiedby channel j with power limit Pj the probability of transmitting in channel j is set to 0. Moreover, thelearning factor Lij is modified to improve the reaction time in a dynamic environment. This is achievedusing a binary variable to update Lij , learning coefficient ξ0 and forgetting coefficient ξ1, as opposed toletting ξ take on any value as in [119]. Instead of slowly learning the environment as in [119], CRs updatethe their learning factor Lij by setting it to the learning factor ξ0 if it meets QoS requirements or to theforgetting factor ξ0 if the channel does not meet transmission requirements. Apart form the above changesto probability of transmission Tij for CR i on channel j and learning factor Lji, the algorithm is preciselythe same as in [119]. For comparison, the same simulation parameters are used. The two paradigms areevaluated based on network recovery after environmental changes and network utilization. The learning timeof e-BIOSS is 10 time units compared to BIOSS where the algorithm takes 25 time units to converge. Thedecreased convergence time for e-BIOSS is a result of the binary approach for the learning coefficient whereCRs learn and forget channels faster. It is noted that e-BIOSS provides a more even distribution of used

31

channels where both low and high channels are utilized instead of just high power channels as in BIOSS.Channel utilization for both schemes are evaluated for an increasing number of users. For single channeltransmission, e-BIOSS shows up to 20% increase in utility compared to BIOSS as the number of CRs increaseto 30. When channel bonding is allowed in the system, e-BIOSS achieves up to 25% more utility comparedto BIOSS as the number of users increases. The significant increase in spectrum utilization for e-BIOSS isbecause each CR chooses a channel that just satisfies its needs instead of going for channels with highestpower. Hence, e-BIOSS provides a society with fair channel distribution.

Both BIOSS and e-BIOSS captures CR-CR dynamics well. Furthermore, both paradigms take intoaccount the arrival and departure of primary users, i.e. coexistence with primary users. This is achievedusing a fully distributed scalable system that does not require coordination between cognitive radio userswith an additional communication overhead. Although, it is assumed that each CR will determine its channelof communication by observing all channels before making its decision. This might not be feasible in a realsystem. In addition, a prior knowledge of power allowed in each channel might be difficult to obtain. Theauthors for both schemes do not address communication in the CR society or the underlying architecture.It is unclear if the CRs communicate with each other, and, in that case, is there a rendezvous channel, if itis in-band or out-of-band, or if secondary users establish communicate using an access point.

4.3.2 PSO

Particle swarm optimization (PSO) algorithms are used to describe the social behavior of fish schools orbird flocks. In PSO the best solution so far, called the leader of the flock, guide solutions called particlesmoving around in a search space. Each particle evolves by memorizing its personal best performance (localposition) in addition to the leaders position (global position). To achieve global optimization, an iterativeprocess lets particles change their velocity toward the best solution. In [121] the socio-cognitive particleswarm optimization (SCPSO) algorithm is used to address spectrum sharing in an underlay system. TheSCPSO is based on the binary particle swarm optimization algorithm and was first defined by [122]. Theproposed spectrum etiquette is assumed for cellular bands, such as LTE, where the focus lies on the uplinkdirection. This scheme is meant for use by network operator to analyze heterogeneous networks and quantifythe capacity in terms of number of secondary links allowed in the system and throughput obtained. Theobjective is to maximize the system transmission rate for an estimated number of secondary users coexistingwith primary users in a spectrum underlay model. There are a number of secondary links Sl and a numberof primary links Pl where a link consist of a transmitter and receiver. The set of primary channels is fixedPC = Pl. The objective function is defined as

MaxSl∑j=1

cjxj +Pl∑i=1

ci (49)

such thatSINRi ≥ SINRL (50)

SINRj ≥ SINRL (51)

cj > 0, ci > 0 (52)

xj ∈ {0, 1}, 1 ≤ j ≤ Sl (53)

The SINRj is the interference experienced by the jth secondary receiver from transmitters in the samechannel while SNIRi is the interference experienced by the ith primary receiver from secondary transmittersin the same channel. The predefined threshold SINRL is used to constrain the interference level. The datarates cj , ci are for the secondary link and primary link respectively measured in Mbps (Blog2(1 + SINR)where B is the bandwidth). The binary variable xj indicate whether a secondary user is part of the overallthroughput for the system. The primary users and secondary users are deployed randomly in an area. ThreeNxD size matrices are used to represent the particles X, the velocity V and the best position P where Nis the swarm size and D is the number of secondary links. Two additional NxD matrices P ′ and X ′ areused to store the allocated primary channel for each secondary user. Matrix X stores a binary decision if asecondary link has been chosen. Matrix X ′ stores the number of the primary channel from the set PC at

32

random that will be accessed by the corresponding secondary user in X. Similarly, P is a bitmap indicatingchosen links and matrix P ′ stores the best channel found so far. A spectrum status vector is used to indicateprimary user channel selection where each channel is occupied by precisely one primary user. The followingis the pseudo code for the SCPSO algorithm:

1. Randomly initialize matrices X,X ′, P, P ′, V and the spectrum status vector.

2. Loop.

3. Compute particle fitness f(X ′i) and f(P ′

i ) where f(X ′i) is the current fitness and f(P ′

i ) is the bestfitness for particle i.

4. If current fitness f(X ′i) is better than the local best f(P ′

i ), update f(P ′i ) by setting it to f(X ′

i).

5. Find the globally best solution f(P ′g) by looping through the swarm’s local best solutions f(P ′

j).

6. Update the velocity Vi and bitmap Xi for each secondary link where velocity is based on the distanceof both the local and global best positions.

7. Based on the outcome of the velocity, update bitmap X ′i by randomly allocating channels from PC if

xid = 1 in Xi.

8. Repeat step 3 until end of swarm.

9. Repeat step 2 until stopping criterion is met.

Particle fitness in found by computing the SNIR value for both secondary and primary users. Once theSINR values are computed, the particle fitness steps is performed if the SINR threshold criteria is met bythe secondary users (eq-40) and the primary users (eq-41). If SINR criteria is met by all secondary andall primary users the corresponding evaluation vectors are set to 1, otherwise 0. If the both primary andsecondary evaluation vectors are set to 1, the particles fitness stage is performed. The data rate is calculatedfor secondary and primary links, the results added, and the value stored. Finally, primary fitness results areadded to the secondary fitness results to obtain the total particle fitness. If the SINR level criteria is notmet by either the secondary users, primary users, or both, the solution is considered invalid. To avoid usinginvalid solutions toward the selection process, a penalty function is used to facilitate constraint handling.In this case, since the objective is to maximize the total data rate in the system, the data rates are notcalculated and total particle fitness is set to zero. Numeric results are obtained by deploying secondary linksand primary links in a 5km by 5km area where the maximum distance between transmitter and receiverpair is 1km and all links operate using the same power. The stopping criterion for the SCPSO algorithmis determined to be 4000 iteration. Secondary links are gradually added one by one up to 20 altogetherwhile primary links are kept at a fixed size of 6. Simulations are performed for SNIR limit of 6dB to 14dB.The results are compared to a scheme using a more computational complex Monte Carlo (MC) simulationproposed by []. It is shown that the proposed scheme outperforms the MC scheme in that the number ofsecondary user coexisting with primary users is greater and better SNIR values resulting in better QoS forboth secondary and primary users. The sum throughput is maximized when 12 secondary users (comparedto 8 using MC) share bands with 6 primary users at a SNIR level of 6dB. The proposed algorithm managesto determine the maximum data rate for the system, determine the set of secondary links that can coexistwith primary links, allocate channels to secondary and primary links, and evaluate the SNIR levels. Themain advantage of SCPSO over MC is the computational time where SCPSO does not require evaluation ofthe all possible solutions, i.e. works with a reduced search space size. On the other hand, SCPSO, therefore,does not guarantee an optimal solution.

In [123] two variations of the particle swarm optimization algorithm, binary particle swarm optimization(BPSO) and Derivation 0, are evaluated against SCPSO. These three schemes differs in the way the velocityof a particle is updated. Each scheme is evaluated comparing the sum throughput, number of secondarylinks sharing primary channels, and convergence speed. Simulation results show that Derivation 0 is out-performed by both BPSO and SCPSO. No significant difference is recorded between SCPSO and BPSO forsum throughput and maximum coexisting secondary links. The difference between the two schemes is in thecomputational time where SCPSO outperforms BPSO. Although, since this approach is not suitable for real

33

time computation, the difference in computational time is not critical.

One of the challenges in underlay sharing is estimating the aggregate interference in order not to exceedthe interference temperature limit. This is also true in other sharing techniques where co-channel interfer-ence is obtained but adjacent channel interference is seldom addressed.

5 IEEE 802.22 standard

5.1 Background

Some existing standards, s.a. WiMAX (IEEE 802.16) and Zigbee (IEEE 802.15.4), include a certain degreeof cognitive radio technology [124]. Sherman et.al. presents a comprehensive survey explaining the evolutionof standards that allow for coexistence of different network types in the same band. Dynamic frequencyselection and power control are techniques used to facilitate network coexistence. These techniques can beseen as first steps towards developing cognitive radio network standards.

IEEE 802.22 is the first worldwide standard that incorporates cognitive radio technology. Since WiMAXis the “predecessor” of 802.22, many 802.16 PHY layer and MAC layer features are used in 802.22 [124].The two main differences between the 802.16 standard and the 802.22 standard are:

1. Propagation range:

- 802.16 is a metropolitan area network (MAN) spanning up to 5km.

- 802.22 is a wireless regional area network (WRAN) spanning up to 100 km.

2. Incumbent protection:

- 802.16 does not support dynamic spectrum access since these type of networks do not operate ina opportunistic manner in licensed frequency bands (802.16h, which is under development, willincorporate some DSA features [124]).

- 802.22 operate in TV spectrum and must therefore support incumbent protection. To ensure licenseduser protection with a certain level of accuracy, 802.22 incorporates three detection techniques:(a) spectrum sensing, (b) geolocation and incumbent database query (these techniques are dis-cussed in sections 5.3.1 and 5.3.2 respectively).

In addition to the two main differences mentioned above, 802.22 networks must actively address self-coexistence since co-channel interference is more likely, compared to other 802 wireless standards, due towide range operation [124].

An overview of the 802.22 standard is presented in [125], [126]. While Stevenson et al. give a moredetailed description of the PHY layer and Cordeiro et al. focus on the MAC layer, both surveys capturethe main features of the standard as described in the following sections. For precise IEEE 802.22 standardspecifications see [127], [128], [129].

5.2 Topology, PHY, and MAC

The IEEE 802.22 wireless regional area networks (WRAN) is the first worldwide standard that takes ad-vantage of cognitive radio techniques. Developed by the IEEE 802.22 Working Group, this standard allowssecondary users to opportunistically utilize UHF/VHF TV bands between 54 MHz and 862 MHz on non-interference basis to incumbent operation. The main propose of the 802.22 WRAN standard is to bringbroadband access to rural and remote areas where population density is low and spectrum availability ishigh.

5.2.1 Topology

The 802.22 standard specifies a fixed point-to-multipoint (P2MP) network topology. 802.22 is a centralizednetwork where customer premises equipments (CPEs) are connected to a base station (BS) via wireless

34

links. Each BS can manage up to 255 CPEs, although, due to interference and QOS (comparable to DSL)requirements, 255 CPEs in a WRAN network is currently not feasible [125]. The radius of the WRANsystem is typically around 30 km but can span up to 100 km. Both the BS and CPEs support cognitive radiocapabilities and can perform spectrum sensing. Each CPE is equipped with two antennas, one directionaland one omni-directional. In order to limit energy radiation in all directions, the directional antenna isused for communication with the base station. The omni-directional antenna is used for sensing incumbentoperation and is usually placed outdoors using installation similar to TV receiving antennas. Currently the802.22 standard does not support multiple antennas for data transmission s.a. MIMO technology.

In addition to normal operation at the PHY and MAC layers in a wireless system, the 802.22 must, forexample, account for a longer propagation delay and incumbent protection. Both the PHY and MAC layersare used to deal with these issues as described in the following two subsections

5.2.2 PHY

The primary functions at the physical layer are spectrum sensing, geolocation, and the main data communi-cation (spectrum sensing and geolocation are described in section 5.3.1 and 5.3.2 respectively). To ensure theminimum throughput requirements the PHY must absorb multi-path excess delays arising due to long rangeoperation. The PHY layer depends on orthogonal frequency-division multiplexing (OFDMA) modulation inorder to absorb this delay. To facilitate OFDMA and allow frequency and path diversity, 802.22 supports:a) four different cyclic prefixes lengths (1/4, 1/8, 1/16, and 1/32), b) a combination of three modulationschemes (QPSK, 16-QAM, 64-QMA) and four code rates (1/2 ,2/3, 3/4, 5/6), c) sub-carrier spacing (3.3kHz for 6MHz TV bands).

Channel bonding is a technique where aggregation of channels is used to further improve transmissionrate. Both contiguous and non-contiguous channel bonding schemes are supported. In the US some restrictorsarise when aggregating channels since TV channel must be separated by at least two empty channels in orderto minimize interference from neighboring TV broadcasters. This restriction limits the contiguous channelbonding to three TV channels [125]. Since each channel in the US is 6 MHz, a bandwidth of 18 MHz isobtained using this technique.

5.2.3 MAC

Since the PHY layer absorbs propagation delays up to 30 km, the MAC layer is designed to absorbthe additional delay (up to 100 km) using scheduling algorithms based on time devision multiplexing(TDMA) [125], [126].

In addition to a MAC frame, the 802.22 provides a superframe structure used to facilitate communicationand incumbent protection. The superframe, which starts with a preamble and a superframe control header(SCH), is sent by the BS in all available channels. It contains all necessary information for a CPE to establishcommunication with the BS as described below.

Network initialization differs in the 802.22 system compared to other wireless systems since the CPEsdo not have a priori information about the BSs’ channels of operation. Upon network entry each CPE willlook for vacant channels in order to find SCH transmission by the BS. During this scanning process, theCPEs will store information about each channel. Once the CPE connects to the BS, it will pass the channelinformation and receive channel availability instructions from the BS.

In addition to sensing TV broadcasting, the WRAN system must be able to detect auxiliary devices s.a.wireless microphones. Wireless microphones are low power devices. Sensing techniques alone might not besufficient to detect these devices. To solve this problem, the wireless microphone network transmit beaconsto facilitate more accurate detection by the WRAN system.

5.3 Incumbent Protection

To ensure incumbent protection each BS will coordinate distributed sensing performed by the CPEs (the BSis also equipped with sensing capabilities and can retrieve channel information when needed). The 802.22networks must detect licensed users with certain accuracy. Therefore, in addition to spectrum sensing the802.22 system uses geolocation and incumbent database query techniques.

35

5.3.1 Sensing management

In order to protect incumbent users, sensing is performed on the operating TV channel and its two adjacentchannels (US regulation to limit interference between TV broadcasters). Sensing can be performed byboth the BS and CPEs. Each unit must be able to sense analog TV, digital TV, and auxiliary devices.Although there is no requirement as to which sensing technology the units should use, sensing must be donewithin a probability of 90% for detection and 10% for false alarm. The BS manages spectrum sensing andassigns a sensing channel priority list to each CPE based on their specifications s.a. geolocation. This isdone for efficiency and to limit interference to the system while sensing. Both in-band sensing (BS-CPEcommunication channel) and out-of-band sensing (all channels not used for BS-CPE communication) mustbe performed. To limit interference by the WRAN system during in-band sensing, scheduled quiet periodsare provided by the MAC layer. Two sensing types are used during the quiet periods: fast sensing and finesensing. Fine sensing takes more time and causes more interference, but it is more accurate then fast sensing.Fine sensing will be performed if deemed necessary by the BS depending on the information obtained duringthe fast sensing phases. Although CPEs have sensing capabilities, the final decision for channel access isdetermined by the BS.

5.3.2 Geolocation management and Incumbent Database query

Geolocation: Each CPE in the network must be installed in a fixed location and report its location tobe able to associate with the local BS during the initialization process. Therefore, each CPE is equippedgeolocation technology. Once the CPE determines its location, it sends the coordinates to the local BS. TheBS in turn determines the channels that a CPE can potentially operate in based on the information received.

Incumbent database: An up-to-date database service of incumbent operations is assumed to be avail-able to each BS. This database should contain information about TV operation, auxiliary operation, andoverlapping 802.22 operation in the area. During the initialization process, each BS will receive all CPEspecification in addition to location coordinates. Based on this information the BS will query the databasein order to determine which channels the CPE can operate in without causing interference. The CPE willreceive a list of channels and other specifications, s.a. maximum operational power for each channel, andcan thereafter choose the channel best suited for its operation.

5.4 Self-coexistence

Self-coexistence is an important issue in WRAN systems since wireless systems with long range operationare more susceptible to self-interference. Interference from adjacent WRAN system can make a networkinoperable [125]. In order to synchronize operation between WRAN systems, the MAC layer coexistencebeacon protocol (CPB) allow BSs and CPEs to transmit time stamped beacons to neighboring WRANs. Thesynchronization of WRAN operation is done in a distributed manner using a simple set of rules which resultsin relatively fast convergence [125]. The BSs will adjust the superframe (used to managed communication andcognitive functions) start time based on the coexistence beacon in order to facilitate intercell communication.This is necessary to, for example, managed quiet periods during in-band sensing as described in section 5.3.1.

6 Conclusion

In this survey we have reviewed recent decision theoretical and bio-inspired spectrum sharing paradigms indynamic spectrum access. We have given a general overview cognitive radio architecture, duty cycle, localsensing, and cooperative sensing to emphasize challenges and limitations that impact MAC layer design.We have focused on sharing paradigms drawn from three fields: multi-armed bandit problem, game theory,and swarm intelligence. Although all schemes address efficiency using global optimization, each one solve adiverse set of challenges in cognitive radio networks. Paradigms defined using multi-armed bandit problemcapture secondary-primary coexistence where secondary users learn primary user behavior and can betterutilize the available spectrum bands. Three branches of game theory have been reviewed: static game,repeated game, and Stackelberg game. Game theory captures self-coexistence and coexistence in dynamic

36

spectrum access. Global optimization is achieved by finding NE. We have reviewed paradigms using gametheory that address switching cost, heterogeneous secondary users, secondary user interactions over time,detecting deviating nodes, and primary-secondary cooperation. Finally, swarm intelligence has been pro-posed to solve spectrum access fairness in distributed dynamic spectrum access. Ant colony optimizationand particle swarm optimization has inspired paradigms addressing fair channel selection in heterogeneoussystems, aggregate power estimation in underlay sharing, and primary-secondary interactions. The urgentneed to solve spectrum scarcity and underutilization has lead to development of the 802.22 standard. Wereview of 802.22 standard where the difficulties to achieve primary users protection is clearly seen. Forexample, the 802.22 standard does not rely solely on sensing techniques since they are often not accurateenough in practice. Furthermore, 802.22 it is not suitable for large-scale wireless networks. Deployment ofefficient dynamic spectrum access in urban areas without substansial infrastructure costs is currently notfeasible. Therefore, it is imperative to look beyond traditional approaches to address the complex challengesarising dynamic spectrum access.

References

[1] J. Mitola and J. Maguire, G.Q., “Cognitive radio: making software radios more personal,” IEEEPersonal Communications, vol. 6, no. 4, pp. 13–18, Aug. 1999.

[2] M. A. McHenry, P. A. Tenhula, D. McCloskey, D. A. Roberson, and C. S. Hood, “Chicago spectrumoccupancy measurements & analysis and a long-term studies proposal,” in Proceedings of the FirstInternational Workshop on Technology and Policy for Accessing Spectrum, ser. TAPAS ’06. NewYork, NY, USA: ACM, 2006. [Online]. Available: http://doi.acm.org/10.1145/1234388.1234389

[3] M. Wellens and P. Mahonen, “Lessons learned from an extensive spectrum occupancy measurementcampaign and a stochastic duty cycle model,” Mobile Networks and Applications, vol. 15, no. 3, pp.461–474, Jun. 2010.

[4] A. Goldsmith, S. A. Jafar, I. Maric, and S. Srinivasa, “Breaking spectrum gridlock with cognitiveradios: An information theoretic perspective,” Proceedings of the IEEE, vol. 97, no. 5, pp. 894–914,2009.

[5] J. Liu, W. Chen, Z. Cao, and Y. J. Zhang, “Delay optimal scheduling for cognitive radios with coopera-tive beamforming: A structured matrix-geometric method,” IEEE Transactions on Mobile Computing,vol. 11, no. 8, pp. 1412–1423, 2012.

[6] M. H. Hassan and M. Hossain, “Cooperative beamforming for cognitive radio systems with asyn-chronous interference to primary user,” IEEE Transactions on Wireless Communications, vol. 12,no. 11, pp. 5468–5479, 2013.

[7] H. Arslan and M. E. SShin, “UWB cognitive radio,” in Cognitive Radio, Software Defined Radio, andAdaptive Wireless Systems, H. Arslan, Ed. Springer Netherlands, Jan. 2007, pp. 355–381.

[8] M. Song, C. Xin, Y. Zhao, and X. Cheng, “Dynamic spectrum access: from cognitive radio to networkradio,” IEEE Wireless Communications, vol. 19, no. 1, pp. 23–29, 2012.

[9] N. Jindal and A. Goldsmith, “Dirty-paper coding versus TDMA for MIMO broadcast channels,” IEEETransactions on Information Theory, vol. 51, no. 5, pp. 1783–1794, May 2005.

[10] A. Khina and U. Erez, “On the robustness of dirty paper coding,” IEEE Transactions on Communi-cations, vol. 58, no. 5, pp. 1437–1446, May 2010.

[11] I. F. Akyildiz, W.-Y. Lee, M. C. Vuran, and S. Mohanty, “NeXt generation/dynamic spectrum ac-cess/cognitive radio wireless networks: A survey,” Computer Networks, vol. 50, no. 13, pp. 2127–2159,Sep. 2006.

37

[12] B. Wang and K. Liu, “Advances in cognitive radio networks: A survey,” IEEE Journal of SelectedTopics in Signal Processing, vol. 5, no. 1, pp. 5–23, 2011.

[13] D. B. Rawat and G. Yan, “Spectrum sensing methods and dynamic spectrum sharing in cognitive radionetworks: A survey,” International Journal of Research and Reviews in Wireless Sensor Networks,vol. 1, no. 1, 2011.

[14] S. Srinivasa and S. Jafar, “COGNITIVE RADIOS FOR DYNAMIC SPECTRUM ACCESS - thethroughput potential of cognitive radio: A theoretical perspective,” IEEE Communications Magazine,vol. 45, no. 5, pp. 73–79, May 2007.

[15] E. Axell, G. Leus, E. Larsson, and H. Poor, “Spectrum sensing for cognitive radio : State-of-the-artand recent advances,” IEEE Signal Processing Magazine, vol. 29, no. 3, pp. 101–116, 2012.

[16] J. Mitola, “Cognitive radio,” Licentiate proposal, KTH, Stockholm, Sweden, 2000.

[17] M. T. Mushtaq, M. S. Khan, M. R. Naqvi, R. D. Khan, M. A. Khan, and O. F. Koudelka, “Cognitiveradios and cognitive networks: A short introduction,” 2013.

[18] T. Yucek and H. Arslan, “A survey of spectrum sensing algorithms for cognitive radio applications,”IEEE Communications Surveys Tutorials, vol. 11, no. 1, pp. 116–130, 2009.

[19] R. Umar and A. U. Sheikh, “A comparative study of spectrum awareness techniques for cognitive radiooriented wireless networks,” Physical Communication, vol. 9, pp. 148–170, Dec. 2013.

[20] A. De Domenico, E. Calvanese Strinati, and M.-G. Di Benedetto, “A survey on MAC strategies forcognitive radio networks,” IEEE Communications Surveys & Tutorials, vol. 14, no. 1, pp. 21–44, 2012.

[21] I. F. Akyildiz, W.-Y. Lee, M. C. Vuran, and S. Mohanty, “A survey on spectrum management incognitive radio networks,” Communications Magazine, IEEE, vol. 46, no. 4, pp. 40–48, 2008.

[22] E. Hossain, D. Niyato, and D. I. Kim, “Evolution and future trends of research in cognitive radio: acontemporary survey: Evolution and future trends of research in cognitive radio,” Wireless Commu-nications and Mobile Computing, pp. n/a–n/a, Oct. 2013.

[23] Y.-C. Liang, K.-C. Chen, G. Y. Li, and P. Mahonen, “Cognitive radio networking and communications:an overview,” IEEE Transactions on Vehicular Technology, vol. 60, no. 7, pp. 3386–3407, Sep. 2011.

[24] B. F. Lo, “A survey of common control channel design in cognitive radio networks,” Physical Commu-nication, vol. 4, no. 1, pp. 26–39, Mar. 2011.

[25] A. De Domenico, E. Strinati, and M. Di Benedetto, “A survey on MAC strategies for cognitive radionetworks,” IEEE Communications Surveys Tutorials, vol. 14, no. 1, pp. 21–44, 2012.

[26] C. Cormio and K. R. Chowdhury, “A survey on MAC protocols for cognitive radio networks,” Ad HocNetworks, vol. 7, no. 7, pp. 1315–1329, Sep. 2009.

[27] P. Ren, Y. Wang, Q. Du, and J. Xu, “A survey on dynamic spectrum access protocols for distributedcognitive wireless networks.” EURASIP J. Wireless Comm. and Networking, vol. 2012, p. 60, 2012.

[28] T. Vamsi Krishna and A. Das, “A survey on MAC protocols in OSA networks,” Computer Networks,vol. 53, no. 9, pp. 1377–1394, Jun. 2009.

[29] H. Wang, H. Qin, and L. Zhu, “A survey on MAC protocols for opportunistic spectrum access in cog-nitive radio networks,” in Computer Science and Software Engineering, 2008 International Conferenceon, vol. 1. IEEE, 2008, pp. 214–218.

[30] A. Sahai, R. Tandra, S. M. Mishra, and N. Hoven, “Fundamental design tradeoffs in cognitive radiosystems,” in Proceedings of the first international workshop on Technology and policy for accessingspectrum. ACM, 2006, p. 2.

38

[31] F. Digham, M.-S. Alouini, and M. K. Simon, “On the energy detection of unknown signals over fadingchannels,” in IEEE International Conference on Communications, 2003. ICC ’03, vol. 5, May 2003,pp. 3575–3579 vol.5.

[32] J. Ma, G. Li, and B. H. Juang, “Signal processing in cognitive radio,” Proceedings of the IEEE, vol. 97,no. 5, pp. 805–823, May 2009.

[33] R. Tandra and A. Sahai, “SNR walls for signal detection,” IEEE Journal of Selected Topics in SignalProcessing, vol. 2, no. 1, pp. 4–17, 2008.

[34] A. Sahai, N. Hoven, S. M. Mishra, and R. Tandra, “Fundamental tradeoffs in robust spectrum sensingfor opportunistic frequency reuse,” submitted to IEEE J. Select. Areas Commun, vol. 1, 2006.

[35] M. Oner and F. Jondral, “Cyclostationarity based air interface recognition for software radio systems,”in 2004 IEEE Radio and Wireless Conference, Sep. 2004, pp. 263–266.

[36] K. Maeda, A. Benjebbour, T. Asai, T. Furuno, and T. Ohya, “Recognition among OFDM-Basedsystems utilizing cyclostationarity-inducing transmission,” in 2nd IEEE International Symposium onNew Frontiers in Dynamic Spectrum Access Networks, 2007. DySPAN 2007, Apr. 2007, pp. 516–523.

[37] Y. Zeng and Y.-C. Liang, “Eigenvalue-based spectrum sensing algorithms for cognitive radio,” Com-munications, IEEE Transactions on, vol. 57, no. 6, pp. 1784–1793, 2009.

[38] B. Nadler, F. Penna, and R. Garello, “Performance of eigenvalue-based signal detectors with knownand unknown noise level,” in Communications (ICC), 2011 IEEE International Conference on. IEEE,2011, pp. 1–5.

[39] P. Bianchi, J. Najim, G. Alfano, and M. Debbah, “Asymptotics of eigenbased collaborative sensing,”in IEEE Information Theory Workshop, 2009. ITW 2009, Oct. 2009, pp. 515–519.

[40] Y. Zeng, Y. C. Liang, and R. Zhang, “Blindly combined energy detection for spectrum sensing incognitive radio,” IEEE Signal Processing Letters, vol. 15, pp. 649–652, 2008.

[41] S. Geirhofer, L. Tong, and B. M. Sadler, “A measurement-based model for dynamic spectrum accessin WLAN channels,” in Military Communications Conference, 2006. MILCOM 2006. IEEE. IEEE,2006, pp. 1–7.

[42] D. Willkomm, S. Machiraju, J. Bolot, and A. Wolisz, “Primary users in cellular networks: A large-scalemeasurement study,” in New frontiers in dynamic spectrum access networks, 2008. DySPAN 2008. 3rdIEEE symposium on. IEEE, 2008, pp. 1–11.

[43] Z. Tian and G. Giannakis, “A wavelet approach to wideband spectrum sensing for cognitive radios,”in 1st International Conference on Cognitive Radio Oriented Wireless Networks and Communications,2006, Jun. 2006, pp. 1–5.

[44] Y. Youn, H. Jeon, H. Jung, and H. Lee, “Discrete wavelet packet transform based energy detector forcognitive radios,” in Vehicular Technology Conference, 2007. VTC2007-Spring. IEEE 65th, Apr. 2007,pp. 2641–2645.

[45] B. Farhang-Boroujeny, “Filter bank spectrum sensing for cognitive radios,” IEEE Transactions onSignal Processing, vol. 56, no. 5, pp. 1801–1811, May 2008.

[46] P. Amini, R. Kempter, and B. Farhang-Boroujeny, “A comparison of alternative filterbank multicarriermethods for cognitive radio systems,” in 2006 Software Defined Radio Technical Conference, 2006.

[47] H. Li, C. Li, and H. Dai, “Quickest spectrum sensing in cognitive radio,” in 42nd Annual Conferenceon Information Sciences and Systems, 2008. CISS 2008, Mar. 2008, pp. 203–208.

[48] L. Lai, Y. Fan, and H. Poor, “Quickest detection in cognitive radio: A sequential change detectionframework,” in IEEE Global Telecommunications Conference, 2008. IEEE GLOBECOM 2008, Nov.2008, pp. 1–5.

39

[49] E. Hanafi, P. Martin, P. Smith, and A. Coulson, “Extension of quickest spectrum sensing to multipleantennas and rayleigh channels,” IEEE Communications Letters, vol. 17, no. 4, pp. 625–628, Apr.2013.

[50] A. Fehske, J. Gaeddert, and J. Reed, “A new approach to signal classification using spectral correlationand neural networks,” in 2005 First IEEE International Symposium on New Frontiers in DynamicSpectrum Access Networks, 2005. DySPAN 2005, Nov. 2005, pp. 144–150.

[51] D. Cabric, S. Mishra, and R. Brodersen, “Implementation issues in spectrum sensing for cognitiveradios,” in Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems andComputers, 2004, vol. 1, Nov. 2004, pp. 772–776 Vol.1.

[52] R. W. Brodersen, A. Wolisz, D. Cabric, S. M. Mishra, and D. Willkomm, “Corvus: a cognitive radioapproach for usage of virtual unlicensed spectrum,” Berkeley Wireless Research Center (BWRC) Whitepaper, 2004.

[53] A. Ghasemi and E. S. Sousa, “Collaborative spectrum sensing for opportunistic access in fading envi-ronments,” in New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005. 2005 FirstIEEE International Symposium on, 2005, pp. 131–136.

[54] K. Letaief and W. Zhang, “Cooperative communications for cognitive radio networks,” Proceedings ofthe IEEE, vol. 97, no. 5, pp. 878–893, 2009.

[55] H. Urkowitz, “Energy detection of unknown deterministic signals,” Proceedings of the IEEE, vol. 55,no. 4, pp. 523–531, Apr. 1967.

[56] A. H. Nuttall, “Some integrals involving the q-function,” Tech. Rep., Apr. 1972.

[57] I. F. Akyildiz, B. F. Lo, and R. Balakrishnan, “Cooperative spectrum sensing in cognitive radionetworks: A survey,” Physical Communication, vol. 4, no. 1, pp. 40–62, Mar. 2011.

[58] A. Ghasemi and E. S. Sousa, “Opportunistic spectrum access in fading channels through collaborativesensing,” Journal of Communications, vol. 2, no. 2, Mar. 2007.

[59] P. K. Varshney, Distributed Detection and Data Fusion, 1st ed. Secaucus, NJ, USA: Springer-VerlagNew York, Inc., 1996.

[60] W. Zhang, R. Mallik, and K. Letaief, “Optimization of cooperative spectrum sensing with energydetection in cognitive radio networks,” IEEE Transactions on Wireless Communications, vol. 8, no. 12,pp. 5761–5766, Dec. 2009.

[61] E. Peh, Y.-C. Liang, Y. L. Guan, and Y. Zeng, “Optimization of cooperative sensing in cognitive radionetworks: A sensing-throughput tradeoff view,” IEEE Transactions on Vehicular Technology, vol. 58,no. 9, pp. 5294–5299, Nov. 2009.

[62] J. Ma, G. Zhao, and Y. Li, “Soft combination and detection for cooperative spectrum sensing incognitive radio networks,” IEEE Transactions on Wireless Communications, vol. 7, no. 11, pp. 4502–4507, Nov. 2008.

[63] E. Visotsky, S. Kuffner, and R. Peterson, “On collaborative detection of TV transmissions in supportof dynamic spectrum sharing,” in 2005 First IEEE International Symposium on New Frontiers inDynamic Spectrum Access Networks, 2005. DySPAN 2005, Nov. 2005, pp. 338–345.

[64] S. Chaudhari, J. Lunden, V. Koivunen, and H. V. Poor, “Cooperative sensing with imperfect reportingchannels: Hard decisions or soft decisions?” IEEE Transactions on Signal Processing, vol. 60, no. 1,pp. 18–28, Jan. 2012.

[65] Z. Chair and P. Varshney, “Optimal data fusion in multiple sensor detection systems,” IEEE Trans-actions on Aerospace and Electronic Systems, vol. AES-22, no. 1, pp. 98–101, Jan. 1986.

40

[66] S. M. Mishra, A. Sahai, and R. W. Brodersen, “Cooperative sensing among cognitive radios,” inCommunications, 2006. ICC’06. IEEE International Conference on, vol. 4. IEEE, 2006, pp. 1658–1663.

[67] Y. Yuan, P. Bahl, R. Chandra, P. Chou, J. Ferrell, T. Moscibroda, S. Narlanka, and Y. Wu, “KNOWS:cognitive radio networks over white spaces,” in 2nd IEEE International Symposium on New Frontiersin Dynamic Spectrum Access Networks, 2007. DySPAN 2007, Apr. 2007, pp. 416–427.

[68] Y.-C. Liang, Y. Zeng, E. Peh, and A. T. Hoang, “Sensing-throughput tradeoff for cognitive radionetworks,” IEEE Transactions on Wireless Communications, vol. 7, no. 4, pp. 1326–1337, Apr. 2008.

[69] E. Peh, Y.-C. Liang, Y. L. Guan, and Y. Zeng, “Power control in cognitive radios under cooperativeand non-cooperative spectrum sensing,” IEEE Transactions on Wireless Communications, vol. 10,no. 12, pp. 4238–4248, Dec. 2011.

[70] K. Bian and J.-M. Park, “Asynchronous channel hopping for establishing rendezvous in cognitive radionetworks,” in 2011 Proceedings IEEE INFOCOM, Apr. 2011, pp. 236–240.

[71] K. Bian, J.-M. Park, and R. Chen, “Control channel establishment in cognitive radio networks usingchannel hopping,” IEEE Journal on Selected Areas in Communications, vol. 29, no. 4, pp. 689–703,Apr. 2011.

[72] C. Rago, P. Willett, and Y. Bar-Shalom, “Censoring sensors: a low-communication-rate scheme fordistributed detection,” IEEE Transactions on Aerospace and Electronic Systems, vol. 32, no. 2, pp.554–568, Apr. 1996.

[73] C. Sun, W. Zhang, and K. Letaief, “Cooperative spectrum sensing for cognitive radios under bandwidthconstraints,” in Wireless Communications and Networking Conference, 2007. WCNC 2007. IEEE.IEEE, 2007, pp. 1–5.

[74] J. Lunden, V. Koivunen, A. Huttunen, and H. V. Poor, “Collaborative cyclostationary spectrumsensing for cognitive radio systems,” Signal Processing, IEEE Transactions on, vol. 57, no. 11, pp.4182–4195, 2009.

[75] C. Sun, W. Zhang, and K. Ben, “Cluster-based cooperative spectrum sensing in cognitive radio sys-tems,” in IEEE International Conference on Communications, 2007. ICC ’07, Jun. 2007, pp. 2511–2515.

[76] T. Chen, H. Zhang, G. Maggio, and I. Chlamtac, “CogMesh: A cluster-based cognitive radio network,”in 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2007.DySPAN 2007, 2007, pp. 168–178.

[77] X.-L. Huang, G. Wang, and F. Hu, “Multitask spectrum sensing in cognitive radio networks viaspatiotemporal data mining,” IEEE Transactions on Vehicular Technology, vol. 62, no. 2, pp. 809–823,Feb. 2013.

[78] W. Wang, H. Li, Y. Sun, and Z. Han, “Attack-proof collaborative spectrum sensing in cognitive radionetworks,” in 43rd Annual Conference on Information Sciences and Systems, 2009. CISS 2009, 2009,pp. 130–134.

[79] L. Lazos, S. Liu, and M. Krunz, “Mitigating control-channel jamming attacks in multi-channel ad hocnetworks,” in Proceedings of the Second ACM Conference on Wireless Network Security, ser. WiSec’09. New York, NY, USA: ACM, 2009, pp. 169–180.

[80] Y. Wu, B. Wang, K. Liu, and T. Clancy, “Anti-jamming games in multi-channel cognitive radionetworks,” IEEE Journal on Selected Areas in Communications, vol. 30, no. 1, pp. 4–15, 2012.

[81] P. Tague, M. Li, and R. Poovendran, “Mitigation of control channel jamming under node captureattacks,” IEEE Transactions on Mobile Computing, vol. 8, no. 9, pp. 1221–1234, Sep. 2009.

41

[82] A. W. Min and K. G. Shin, “Impact of mobility on spectrum sensing in cognitive radio networks,” inProceedings of the 2009 ACM workshop on Cognitive radio networks. ACM, 2009, pp. 13–18.

[83] A. Cacciapuoti, I. Akyildiz, and L. Paura, “Primary-user mobility impact on spectrum sensing incognitive radio networks,” in 2011 IEEE 22nd International Symposium on Personal Indoor and MobileRadio Communications (PIMRC), Sep. 2011, pp. 451–456.

[84] ——, “Optimal primary-user mobility aware spectrum sensing design for cognitive radio networks,”IEEE Journal on Selected Areas in Communications, vol. 31, no. 11, pp. 2161–2172, Nov. 2013.

[85] G. Ganesan and Y. Li, “Cooperative spectrum sensing in cognitive radio networks,” in New Frontiers inDynamic Spectrum Access Networks, 2005. DySPAN 2005. 2005 First IEEE International Symposiumon. IEEE, 2005, pp. 137–143.

[86] ——, “Agility improvement through cooperative diversity in cognitive radio,” in IEEE Global Telecom-munications Conference, 2005. GLOBECOM ’05, vol. 5, 2005, pp. 5 pp.–2509.

[87] C.-h. Lee and W. Wolf, “Energy efficient techniques for cooperative spectrum sensing in cognitiveradios,” in 5th IEEE Consumer Communications and Networking Conference, 2008. CCNC 2008, Jan.2008, pp. 968–972.

[88] Q. Zou, S. Zheng, and A. Sayed, “Cooperative sensing via sequential detection,” IEEE Transactionson Signal Processing, vol. 58, no. 12, pp. 6266–6283, Dec. 2010.

[89] A. Wald, Sequential Analysis. Courier Corporation, 1973.

[90] S. Maleki, A. Pandharipande, and G. Leus, “Energy-efficient distributed spectrum sensing for cognitivesensor networks,” IEEE Sensors Journal, vol. 11, no. 3, pp. 565–573, Mar. 2011.

[91] J. Zhao, H. Zheng, and G.-H. Yang, “Distributed coordination in dynamic spectrum allocation net-works,” in New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005. 2005 FirstIEEE International Symposium on, 2005, pp. 259–268.

[92] W. Saad, Z. Han, M. Debbah, A. Hjorungnes, and T. Basar, “Coalitional games for distributed col-laborative spectrum sensing in cognitive radio networks,” in IEEE INFOCOM 2009, Apr. 2009, pp.2114–2122.

[93] N. Ahmed, D. Hadaller, and S. Keshav, “GUESS: gossiping updates for efficient spectrum sensing,” inProceedings of the 1st international workshop on Decentralized resource sharing in mobile computingand networking, 2006, pp. 12–17.

[94] F. Hu, R. Patibandla, and Y. Xiao, “Spectrum sensing in cognitive radio sensor networks: towardsultra low overhead, distributed channel findings,” International Journal of Sensor Networks, vol. 3,no. 2, pp. 115–122, Jan. 2008.

[95] R. Chen, J.-M. Park, and K. Bian, “Robust distributed spectrum sensing in cognitive radio networks,”in IEEE INFOCOM 2008. The 27th Conference on Computer Communications, Apr. 2008, pp. –.

[96] Y. Chen, Q. Zhao, and A. Swami, “Distributed spectrum sensing and access in cognitive radio networkswith energy constraint,” IEEE Transactions on Signal Processing, vol. 57, no. 2, pp. 783–797, Feb.2009.

[97] Z. Li, F. Yu, and M. Huang, “A distributed consensus-based cooperative spectrum-sensing scheme incognitive radios,” IEEE Transactions on Vehicular Technology, vol. 59, no. 1, pp. 383–393, Jan. 2010.

[98] L. Xiao, S. Boyd, and S. Lall, “A scheme for robust distributed sensor fusion based on average con-sensus,” in Information Processing in Sensor Networks, 2005. IPSN 2005. Fourth International Sym-posium on. IEEE, 2005, pp. 63–70.

42

[99] A. Anandkumar, N. Michael, and A. Tang, “Opportunistic spectrum access with multiple users: Learn-ing under competition,” in 2010 Proceedings IEEE INFOCOM, 2010, pp. 1–9.

[100] P. Auer, N. Cesa-Bianchi, and P. Fischer, “Finite-time analysis of the multiarmed bandit problem,”Machine Learning, vol. 47, no. 2-3, pp. 235–256, May 2002.

[101] Y. Xu, A. Anpalagan, Q. Wu, L. Shen, Z. Gao, and J. Wang, “Decision-theoreticdistributed channel selection for opportunistic spectrum access: Strategies, challenges andsolutions,” IEEE Communications Surveys & Tutorials, pp. 1–25, 2013. [Online]. Available:http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6492306

[102] Y. Gai and B. Krishnamachari, “Decentralized online learning algorithms for opportunistic spectrumaccess,” in 2011 IEEE Global Telecommunications Conference (GLOBECOM 2011), 2011, pp. 1–6.

[103] L. Chen, S. Iellamo, and M. Coupechoux, “Opportunistic spectrum access with channel switchingcost for cognitive radio networks,” in 2011 IEEE International Conference on Communications (ICC),2011, pp. 1–5.

[104] K. Liu and Q. Zhao, “Distributed learning in multi-armed bandit with multiple players,” IEEE Trans-actions on Signal Processing, vol. 58, no. 11, pp. 5667–5681, 2010.

[105] A. Anandkumar, N. Michael, A. K. Tang, and A. Swami, “Distributed algorithms for learning andcognitive medium access with logarithmic regret,” Selected Areas in Communications, IEEE Journalon, vol. 29, no. 4, pp. 731–745, 2011.

[106] B. Wang, Y. Wu, and K. R. Liu, “Game theory for cognitive radio networks: Anoverview,” Computer Networks, vol. 54, no. 14, pp. 2537–2561, Oct. 2010. [Online]. Available:http://linkinghub.elsevier.com/retrieve/pii/S1389128610001064

[107] D. E. Charilas and A. D. Panagopoulos, “A survey on game theory applications in wireless networks,”Computer Networks, vol. 54, no. 18, pp. 3421–3430, Dec. 2010.

[108] S. Sengupta, R. Chandramouli, S. Brahma, and M. Chatterjee, “A game theoretic framework fordistributed self-coexistence among IEEE 802.22 networks,” in IEEE Global Telecommunications Con-ference, 2008. IEEE GLOBECOM 2008, Nov. 2008, pp. 1–6.

[109] W. B. Arthur, “Complexity and the economy,” Science, vol. 284, no. 5411, pp. 107–109, Apr. 1999.

[110] Y. Tan, S. Sengupta, and K. P. Subbalakshmi, “Human society inspired dynamic spectrum accessnetworks: The effect of parochialism,” in 2011 IEEE Global Telecommunications Conference (GLOBE-COM 2011), Dec. 2011, pp. 1–5.

[111] R. Etkin, A. Parekh, and D. Tse, “Spectrum sharing for unlicensed bands,” IEEE Journal on SelectedAreas in Communications, vol. 25, no. 3, pp. 517–528, 2007.

[112] Y. Xiao and M. van der Schaar, “Dynamic spectrum sharing among repeatedly interacting selfish userswith imperfect monitoring,” IEEE Journal on Selected Areas in Communications, vol. 30, no. 10, pp.1890–1899, Nov. 2012.

[113] Y. Liu, M. Liu, and J. Deng, “Revisiting optimal power control: its dual effect on SNR and contention,”arXiv preprint arXiv:1309.3716, 2013.

[114] O. Simeone, I. Stanojev, S. Savazzi, Y. Bar-Ness, U. Spagnolini, and R. Pickholtz, “Spectrum leasing tocooperating secondary ad hoc networks,” IEEE Journal on Selected Areas in Communications, vol. 26,no. 1, pp. 203–213, Jan. 2008.

[115] A. Host-Madsen and J. Zhang, “Capacity bounds and power allocation for wireless relay channels,”IEEE Transactions on Information Theory, vol. 51, no. 6, pp. 2020–2040, Jun. 2005.

43

[116] J. Laneman and G. W. Wornell, “Distributed space-time-coded protocols for exploiting cooperativediversity in wireless networks,” IEEE Transactions on Information Theory, vol. 49, no. 10, pp. 2415–2425, Oct. 2003.

[117] J. Zhang and Q. Zhang, “Stackelberg game for utility-based cooperative cognitiveradio networks,” inProceedings of the tenth ACM international symposium on Mobile ad hoc networking and computing,2009, pp. 23–32. [Online]. Available: http://dl.acm.org/citation.cfm?id=1530753

[118] X.-C. Wang, X.-P. Guan, Q.-N. Han, Z.-X. Liu, and K. Ma, “A stackelberg game for spectrum leasingin cooperative cognitive radio networks,” International Journal of Automation and Computing, vol. 10,no. 2, pp. 125–133, Apr. 2013. [Online]. Available: http://link.springer.com/article/10.1007/s11633-013-0705-1

[119] B. Atakan and O. B. Akan, “Biologically-inspired spectrum sharing in cognitive radio networks,” inWireless Communications and Networking Conference, 2007. WCNC 2007. IEEE. IEEE, 2007, pp.43–48.

[120] G. Li, S. W. Oh, K. C. Teh, and K. H. Li, “Enhanced BIOlogically-inspired spectrum sharing forcognitive radio networks,” in Communication Systems (ICCS), 2010 IEEE International Conferenceon. IEEE, 2010, pp. 767–771.

[121] A. Martınez-Vargas and A. G. Andrade, “Particle swarm optimization applied to a spectrum sharingproblem,” AEU - International Journal of Electronics and Communications, vol. 66, no. 12, pp. 969–978, Dec. 2012.

[122] K. Deep and J. Bansal, “A socio-cognitive particle swarm optimization for multi-dimensional knapsackproblem,” in First International Conference on Emerging Trends in Engineering and Technology, 2008.ICETET ’08, Jul. 2008, pp. 355–360.

[123] A. Martınez-Vargas and A. G. Andrade, “Comparing particle swarm optimization variants for a cog-nitive radio network,” Applied Soft Computing, vol. 13, no. 2, pp. 1222–1234, Feb. 2013.

[124] M. Sherman, A. Mody, R. Martinez, C. Rodriguez, and R. Reddy, “IEEE standards supporting cogni-tive radio and networks, dynamic spectrum access, and coexistence,” IEEE Communications Magazine,vol. 46, no. 7, pp. 72–79, Jul. 2008.

[125] C. Cordeiro, K. Challapali, D. Birru, and S. Shankar N, “IEEE 802.22: An introduction to the firstwireless standard based on cognitive radios,” Journal of Communications, vol. 1, no. 1, Apr. 2006. [On-line]. Available: http://www.ojs.academypublisher.com/index.php/jcm/article/viewArticle/01013847

[126] C. Stevenson, G. Chouinard, Z. Lei, W. Hu, S. Shellhammer, and W. Caldwell, “IEEE 802.22: The firstcognitive radio wireless regional area network standard,” IEEE Communications Magazine, vol. 47,no. 1, pp. 130–138, Jan. 2009.

[127] Institute of Electrical and Electronics Engineers and IEEE-SA Standards Board, IEEE standardfor information technology - telecommunications and information exchange between systems -local and metropolitan area networks - specific requirements Part 22.1: standard to enhanceharmful interference protection for low-power licensed devices operating in TV broadcast bands.New York: Institute of Electrical and Electronics Engineers, 2010. [Online]. Available:http://ieeexplore.ieee.org/servlet/opac?punumber=5623444

[128] ——, IEEE standard for information technology–telecommunications and information exchangebetween systems–wireless regional area networks (WRAN)–specific requirements. Part 22, Part22,. New York: Institute of Electrical and Electronics Engineers, 2011. [Online]. Available:http://ieeexplore.ieee.org/servlet/opac?punumber=5951705

[129] IEEE Computer Society, LAN/MAN Standards Committee, Institute of Electrical and ElectronicsEngineers, and IEEE-SA Standards Board, IEEE recommended practice for information technologytelecommunications and information exchange between systems: Wireless Regional Area Networks

44

(WRAN)– specific requirements. Part 22.2, Part 22.2,. New York: Institute of Electrical and Electron-ics Engineers, 2012. [Online]. Available: http://ieeexplore.ieee.org/servlet/opac?punumber=6317265

45