Immune System-inspired Evolutionary Opportunistic SpectrumAccess in Cognitive Radio Ad Hoc Networks

Baris Atakan Burhan Gulbahar Ozgur B. Akan

Next-generation Wireless Communications LaboratoryDepartment of Electrical and Electronics Engineering

Middle East Technical University, 06531, Ankara, TurkeyEmail:{atakan, gulbahar, akan}@eee.metu.edu.tr

Abstract— Underutilization of licensed spectrum stimulates theopportunistic spectrum access (OSA) paradigm that aims toenable unlicensed users to detect and access the temporarilyunused spectrum bands so as to enhance overall spectrumutilization. However, the realization of these paradigms entailsseveral difficulties such as self-organization of unlicensed users,self-regulation of communication parameters, and self-adaptationto time-varying radio environment. In nature, biological systemsintrinsically have these great abilities that can be modeled andadopted to overcome the difficulties posed by opportunisticspectrum access. In this paper, a new Immune system-inspiredEvolutionary Opportunistic Spectrum Access (ESA) protocol isintroduced. Based on the self-nonself detection and clonal selectionprinciples in immune system, ESA allows unlicensed users toseparately detect, share, and access the available spectrum bandswithout interfering the licensed users. The overall ESA operationsdo not need for any priori information about the access statisticsof licensed users and also do not need for any coordinationand message exchanges among the network nodes. In addition,unlike the existing works, ESA does not require any dedicatedcontrol channel in the entire network. Furthermore, ESA alsoexploits the contention among the nodes and their mobility, ifexists, towards accelerating the evolution in the system, andhence, yielding higher overall spectrum utilization. Performanceevaluation results show that ESA achieves high throughput undervarious network conditions.

Index Terms— Cognitive radio, spectrum sharing, spectrummanagement, immune system, clonal selection.

I. INTRODUCTION

UNDERUTILIZATION of wireless spectrum stimulatescognitive radio ad hoc networks (CRAHNs) aiming to

enable unlicensed ad hoc users to opportunistically access un-used licensed spectrum [1]. The term Opportunistic SpectrumAccess (OSA) [3] is broadly used to identify a such kindof overlay spectrum access, in which unlicensed users, i.e.,secondary users (SU) detect and access the temporarily unusedspectrum bands legitimately licensed to primary users (PU).OSA mainly includes three operations:

• Spectrum opportunity identification (SI) aims to identifyand intelligently track temporally unused spectrum bandsto allow each SU to decide whether a spectrum band istemporally accessible or not.

This work was supported in part by the Turkish Scientific and TechnicalResearch Council Career Award under grant #104E043 and by the TurkishNational Academy of Sciences Distinguished Young Scientist Award Program(TUBA-GEBIP).

• Spectrum sharing (SS) enables SUs to intelligently al-locate the spectrum opportunities to minimize perceivedinterference level so as to maximize utilization.

• Spectrum access allows SUs to access the identified spec-trum opportunities to maximize the throughput achieved.

Beside its promising spectrum utilization capabilities, OSAincurs many additional challenges on the conventional accesstechnologies. For example, SI includes scanning and sensingthe available channels to determine whether a channel isaccessible. However, this process brings significant overheadinto the secondary communication in terms of time andenergy consumption. Moreover, SI is prone to some inaccurateobservations of SUs about channel availability. These wrongobservations may impose excessive interference on the li-censed communication. Unshared spectrum opportunities mayalso yield high interference among the SUs and the overallspectrum utilization inevitably decreases. Thus, the efficientSI, SS, and spectrum access mechanisms are imperative toallow SUs to effectively communicate over the temporarilyunused spectrum bands [2].

In nature, biological systems maintain their normal opera-tions in absolutely random and continuously changing envi-ronments by means of their self-organization, self-adaptation,and self-regulation capabilities. For example, immune systemis a natural defense mechanism to protect organism againstpathogens. The aim of immune system is to detect and elim-inate the pathogens. Immune system first reliably detects anddiscriminates the pathogens from self-molecules of the body,e.g., other blood cells or beneficial vitamins and minerals.These operations are intrinsically exposed to many detectionand discrimination faults as well. However, immune systemcan amazingly tolerate the possible detection and discrimina-tion faults via self-nonself identification mechanism [4].

Immune system has also an evolutionary instrument calledclonal selection mechanism (CSM) allowing its cells called B-cells and T-cells to dynamically change their genetic shapes togenetically recognize and eliminate the pathogens. CSM canalso improve its genetic recognition talent such that as diver-sity of infections increases, the organism and immune systembecome more robust toward a large set of new infections.

Clearly, the capabilities of immune system give great in-spiration for the development of many efficient evolutionaryalgorithms such as network intrusion detection [5], multi-objective function optimization [6], [7] and fault diagnosis

978-1-4244-8435-5/10/$26.00 ©2010 IEEE

[8]. Moreover, based on natural immune system principles,in [9], distributed node and frequency rate selection (DNRS)algorithm is proposed for energy-efficient and reliable commu-nication in wireless sensor networks. In this paper, to harnessthe great natural capabilities of immune system we develop anevolutionary opportunistic spectrum access (ESA) algorithm.

A. Related Work

Various OSA protocols in current literature are generallycategorized according to their control information exchangestrategies divided mainly to two different types. In [10],[11], [12], and [13], a dedicated control channel is used toexchange control information. However, a single dedicatedcontrol channel may be a bottleneck when primary usersfrequently use this channel. In [14], [15], time slots are dividedinto control and data phases. In the control phase, controlinformation are exchanged using a single dedicated channel.However, in the data phase, data transmission is carried outusing the channels negotiated in the control phase. In thisapproach, the dependency to a single dedicated channel isalleviated because of exchange of only the control informationon the single control channel.

In addition to control information exchange strategies, theexisting OSA protocols also differ in terms of SI strategies. Infact, some can identify spectrum opportunities [10], [11], [12],[14], while others acquire this information from an externalentity. However, dependency on an external entity is a majorbarrier for the realization of OSA in CRAHNs.

Beside the existing OSA algorithms, some bio-inspiredmechanisms are also developed for some spectrum sharingand management problems in cognitive radio based OSAscenarios. In our previous works, some biological systemsare investigated to discover their promising properties thatcan be harnessed to develop efficient algorithms in cognitiveradio networks (CRN) [17]. Based on the task allocation phe-nomenon in an insect colony, a spectrum sharing mechanismis proposed to enhance spectrum utilization [16]. In [18],similarly, some resource allocation mechanisms adopted frominsect colonies are used for cognitive wireless networks.

None of the existing OSA protocols simultaneously providesSI, SS, and spectrum access. Lack of a spectrum sharingmechanism severely reduces the spectrum utilization. Theseprotocols are also subject to possible congestion in the singlecontrol channel. Moreover, they consider highly static networkmodels in which a fixed number of immobile nodes oppor-tunistically access the available spectrum bands which is notpractical for CRAHNs.

B. Contributions

In this paper, immune system based Evolutionary Oppor-tunistic Spectrum Access (ESA) protocol is introduced. Basedon the principles of self-nonself identification and CSM, ESAconcurrently provides efficient SI, SS and spectrum accessoperations for SUs in CRAHNs. The salient features of ESAare outlined as follows:

• Unlike all the existing OSA mechanisms using sin-gle dedicated control channel that may hamper all SU

communications, ESA enables SUs to use all availablechannels to exchange control information.

• ESA is error-tolerant to provide the resilient identifi-cation of all spectrum opportunities despite potentiallyerroneous spectrum sensing measurements.

• ESA allows SUs to employ optimal spectrum sharing andaccess strategies to maximize overall spectrum utilization.

• ESA quickly incorporates the changes in the network likenode mobility or new node arrivals adaptively.

• ESA is an evolutionary algorithm that can surprisinglyimprove its performance as contention and also interfer-ence among SUs increase. This is a fascinating featurefor OSA domain such that undesirable situations such asinterference or node mobility can be harnessed to improvenetwork robustness via the evolution of network nodes.

• ESA is an autonomous algorithm that does not need anycoordination among network nodes for SI and SS andit also does not need any priori information about thechannel access statistics of primary users.

The remainder of this paper is organized as follows. InSection II, we discuss the analogies and similarities betweenimmune system and OSA. Based on these similarities, inSection III, we introduce an evolutionary OSA model and ESAprotocol operations. In Section IV, we present the performanceevaluation results, which are followed by the concludingremarks given in Section V.

II. IMMUNE SYSTEM AND OPPORTUNISTIC SPECTRUM

ACCESS

Here, we first introduce the immune system, then, discussits relation with OSA.

A. Biological Immune System

The natural immune system is a complex defense mech-anism that protects the organism from hazardous moleculesor micro-organisms. This complex task can be accomplishedby specific blood cells called B-cells and T-cells in immunesystem. When an organism is exposed to antigen molecules,B-cells and T-cells cooperatively detect and eliminate theantigens through self-nonself detection and CSM [6].

Self-nonself detection mechanism can be considered as adistributed detection mechanism in which B-cells are en-visaged as small independent detectors that diffuse throughthe organism in the blood [4]. Detection of antigens can beachieved when the genetic shape of an B-cell chemicallymatches with an antigen genetic shape. However, due to thehuge diversity in B-cell genetic shapes, it is highly likely forB-cell to match with the self-cells of the organism. Althoughthis is a fatal dilemma for the organism, immune system hasthe great error-tolerance capability to avoid this fatal dilemma.This is accomplished through thymus that is an organ andincludes most self-molecules of the organism and in whichB-cells genetically mature. During this maturation process, ifan B-cell matches with some self-molecules in the thymus, itis eliminated to avoid eliminating self-cells of the organism.

While the self-nonself detection mechanism discriminatesantigens from self-molecules of the organism, CSM concur-rently allows B-cells to recognize genetic shape of antigens by

means of affinity maturation and hypermutation phenomenon.Hypermutation separately enables each antibody to change itsgenetic shape. As the genetic change can improve the matchingbetween the antibody and antigen, affinity maturation processallows the B-cells having the best matching with the antigento proliferate so as to genetically recognize and eliminate theantigen. The genetic shape of B-cells that having the bestmatching with the pathogen can be considered as an elitistgenetic shape that is no longer mutated and is memorized bycontinuously proliferating in the course of life time. This elitistselection mechanism provides great robustness for organism.Moreover, this mechanism gives great inspiration for thedevelopment of immune system based multi-objective functionoptimization algorithms [6], [7]. Next, we introduce someadvantageous analogies between immune system and OSA.

B. Immune System and Opportunistic Spectrum Access

Although OSA seems different from the natural immunesystem, they have great deal of intrinsic analogies in termsof operation principles. When immune system encounters amolecule, it determines whether this molecule is a nonselfharmful pathogen by means of self-nonself identification mech-anism. If the molecule is determined as a pathogen, immunesystem recognizes and eliminates the pathogen using clonalselection mechanism. Similar to self-nonself identification inimmune system, OSA needs a detection mechanism to identifyspectrum opportunities. Moreover, similar to clonal selectionin immune system, OSA also needs a selection mechanismto enable each SU to choose and access a set of spectrumopportunities so as to maximize spectrum utilization.

In addition to the operational analogies, some attributiveanalogies between immune system and OSA are as follows:

• Immune system is a highly robust system since it isdiverse and error-tolerant. For OSA, it is also essential toprovide robust opportunistic spectrum access via diver-sity and error tolerance. Diversity in channel availabilityimproves the OSA performance in terms of achievedthroughput and interference level. Error tolerance inchannel sensing provides great robustness in terms ofinterference level incurred on primary users.

• Immune system is adaptive to recognize and learn newinfections. Immune system achieves this adaptivity byimproving useful genetic shapes and discarding uselessor dangerous ones. Similarly, OSA should be adaptiveto recognize and learn channel access pattern of primaryusers. This can be accomplished by discarding uselesschannels, on which primary user activities are high,and selecting other fruitful spectrum channels on whichprimary user activities are minor.

• Immune system is a fully autonomous system which doesnot need any central control to perform its fascinatingsteps. Similarly, OSA needs autonomous operations toalleviate the coordination latency overhead and to makespectrum access more robust.

In order to harness the operational and attributive advan-tages of immune system, we establish the following analogiesbetween immune system and OSA outlined in Table I.

TABLE I

RELATION BETWEEN IMMUNE SYSTEM AND CRN.

Immune System Opportunistic Spectrum AccessB-cell Secondary userGenetic shape of antibody Frequency hopping sequence of SUAntigen Spectrum opportunitySelf-nonself identification Spectrum opportunity identificationClonal selection via somatic hypermu-tation and affinity maturation

Spectrum sharing and access

CHANNEL SENSING FRAME TRANSMISSION ACK

SLOT TIME

TIME

Fig. 1. The time slot structure of a secondary user.

III. IMMUNE SYSTEM BASED EVOLUTIONARY

OPPORTUNISTIC SPECTRUM ACCESS

In this section, we present Immune System Based Evolu-tionary Opportunistic Spectrum Access (ESA) protocol basedon the analogies between immune system and OSA. Beforegiving the details of ESA, we introduce the network modeland assumptions and also give a brief overview for ESA.

A. Network Model and Assumptions

We consider a network model in which entire spectrumis divided into number of N non-overlapping orthogonalchannels having the same bandwidth. Number of M mobileSUs may randomly arrive or depart without any need fora negotiation among SUs. Slot timing is broadcast to SUsby the primary system. Each slot lasts for τ seconds. ESAsplits a time slot into three phases as shown in Fig. 1. In thechannel sensing phase, each SU randomly selects a numberof channels less than N and senses them using the energybased channel sensing [20]. In the data transmission phase,each SU intending to transmit makes data transmission. Afterthe data transmission phase, each SU sent data packet waitsfor an ACK frame from its receiver SU. Each of these threephases consecutively occur in every τ seconds.

All SUs use fixed length frequency hopping sequences(FHS). FHSi denotes the FHS of SUi composed of K distinctfrequency channels where K ≤ N . In each time slot, SUi

consecutively hops on FHSi to transmit or receive data frame.As will be detailed in Section III-D, each transmitter SU canhandshake with a receiver SU to tune its FHS.

B. ESA Overview

ESA concurrently perform SI, SS and spectrum access op-erations. It enables each SU to resiliently identify all spectrumopportunities based on the entropy based channel availabilityinformation acquired by channel sensing. This information canbe reinforced to tolerate possible channel sensing errors. Basedon the channel availability information, each SU generates its

FHS including the channels on which PU activities are minormaximizing overall spectrum utilization.

For spectrum sharing and access, ESA allows each SU tomutate its FHS separately to generate an optimal spectrumsharing and access performance based on a clonal selectionbased distributed immune algorithm. For this optimization,each receiver SU first interacts with the environment by meansof channel sensing and listening and receiving. This interactionlasts for a mutation interval that is a specific number of slotsand preselected by ESA. During the mutation interval, eachreceiver SU gathers information from the radio environment toinfer which of its frequency hops are exposed to interference.Based on this information, it mutates its FHS at the end ofthe mutation interval. If a receiver SU does not receive anyinterference, it stops the mutation operation on its FHS andits FHS becomes an elitist FHS. When all receiver SUs havean elitist FHS, the spectrum sharing process converges to aglobal optimum point maximizing the spectrum utilization.

Next, we detail the ESA operations for SI, SS and spectrumaccess, which can also be seen in Fig. 3.

C. Spectrum Opportunity Identification

SI in ESA is based on the principles of self-nonself iden-tification in immune system. Immune system enables each B-cell to separately discriminate a harmful pathogen from self-cells of the organism. This discrimination is based on naturalmaturation process in which each B-cell is matured to inferwhether it is useful to eliminate pathogens. This provides greaterror-tolerance for the identification errors by reinforcing theinformation about self and non-self cells. Similar to immunesystem, ESA allows each SU to separately infer whethereach spectrum channel can be useful for transmission or not.This is accomplished by the spectrum opportunity maturationprocess. This process empowers each SUi to reinforce channelavailability information for each channel j as follows.

• Whenever SUi senses channel j, it keeps a channelavailability information about channel j using its idle andbusy counters, i.e., Ij

i and Bji , respectively.

• If SUi senses channel j as idle, it updates the idle counterIji as Ij

i = Iji + 1.

• If SUi senses channel j as busy, it updates the busycounter Bj

i as Bji = Bj

i + 1.• Based on the updated Ij

i and Bji , SUi computes the

probability, i.e., pji , that channel j is available for SUi,

as follows

pji =

Iji

Iji + Bj

i

(1)

• To characterize the uncertainty in the availability of chan-nel j, SUi also computes channel identification entropy,i.e., Hj

i , for channel j as follows

Hji = −

(pj

i log2(pji ) + (1 − pj

i )log2(1 − pji )

)(2)

• SUi computes Hji for all available channels and sorts

Hji , ∀j in ascending order and accordingly, generates the

set Ti that includes the channel IDs arranged accordingto the sorted channel identification entropies.

• Ti includes all channel IDs for SUi from the mostaccessible to the least accessible such that Ti(1) is theID of a channel that is the most useful and accessible forSUi to maximize spectrum utilization.

Next, ESA spectrum sharing, access method is presented.

D. Spectrum Sharing and Access

Based on the principles of CSM in immune system, ESAallows each SU to separately share and access the spectrumopportunities that are identified by SI given above. In immunesystem, to recognize and eliminate a pathogen, each B-cellmutates its genetic shape in a way that it improves somegenetic patterns having best matches with the genetic shape ofthe pathogen. Genetic mutations are based on the some interac-tions between B-cells and the pathogen. As these interactionsincrease, the rate of the genetic mutations also increases torecognize and eliminate the pathogen. Similarly, ESA allowseach SU to generate a frequency hopping sequence (FHS)like the genetic shape of B-cells. Let FHSi is the frequencyhopping sequence of SUi. Each FHS is a fixed length stringcomposed of some channel IDs. Similar to a B-cell in theimmune system, each SUi can mutate FHSi based on thesome interactions with the radio environment such as listeningit or transmitting to an intended receiver.

ESA provides the receiver centric spectrum sharing suchthat each receiver SU mutates its FHS to avoid possible in-terferences and to share the spectrum opportunities efficiently.The interference avoidance can be only possible for each SUif it can gain a FHS not overlapping with other SU’s FHSsvia mutations. In Fig. 2, mutations for M receiver SUs areobserved. The mutations can be modeled as an optimizationprocess subject to

minimizeM∑i=1

M∑j �=i

K∑l=1

[1 − sgn

(|FHSi(l) − FHSj(l)|

)](3)

where sgn(.) is the sign function M is the number of receiverSU in the environment, K is the number of channels ineach FHS and FHSi(l) denotes the lth channel of the FHSsequence of SUi. ESA performs this optimization processsimilar to the clonal selection based optimization procedures[6], [7] and each SUi separately performs the followingoperations,

• During a mutation interval, SUi senses the radio environ-ment and receives the transmissions from the transmitterSUs to infer the interferences on its frequency hops.

• SUi determines its frequency hops on which it perceivesinterference from the radio environment. This meanssome overlapping frequency hops among some SUs.

• If SUi receives interference on some of its frequencyhops, it randomly mutates these hops on FHSi byrandomly flipping these frequency hops.

• If SUi does not receive any interference on its allfrequency hops, it sets its frequency hopping sequenceFHSi as an elitist FHS. Similar to clonal selection basedoptimization algorithms [6], [7], elitist set selection al-lows the convergence of the spectrum sharing algorithm.

1 5 ... 7

.....................

.....................

1 2 ... 9

8 2 ... 7

2 5 ... 9

5 7 ... 1

.....................

.....................

2 1 ... 9

8 7 ... 2

2 9 ... 5

Mutation

7 5 ... 1

.....................

.....................

9 1 ... 2

8 2 ... 7

9 2 ... 5

Mutation

FirstElitistNode

.....................

.....................

2 1 ... 9

8 2 ... 7

2 9 ... 5

Mutation

SecondElitistNode

MutationMutation ....................

Node 1

Node 2

Node M

Interfering channels

Step 1 Step 2

Step 3

Step 0

7 5 ... 1

HOPPINGSEQUENCES (1 to K)

.....................

.....................

1 2 ... 9

8 ... ... 7

2 9 ... 5Step N

7 5 ... 1

Fig. 2. Spectrum access and sharing by the mechanisms of FHS generationusing mutation and elitist selection.

• Until all FHS become elitist, the mutations continue.

After each FHS becomes the elitist, the spectrum sharingoperations are naturally terminated. At the optimal point, sinceall frequency hops do not overlap, SUs can no longer collideand interfere with each other providing significant spectrumutilization for the opportunistic communication. All mutationand elitist FHS selection operations are illustrated in Fig. 2.

In addition to the simulation analysis given in Section IVfor the convergence of the spectrum sharing, here, we alsobriefly prove it as follows. Let us assume that we concatenateall FHS sequences of receiver SU to form a single largeFHS sequence, i.e., FHS=(FHS1 FHS2...FHSL), where L isthe number of receiver SUs. Since each FHS has the lengthK, the length of FHS is KL. We also assume that thereexists an optimal sequence FHSopt with length KL that canoptimize the objective function given in (3). Hence, if thereare c mismatches between FHSopt and FHS, the probabilityof convergence, i.e., Pc, can be given as [19]

Pc =c!

K(K2 − K)c(4)

Pc shows that the spectrum sharing algorithm is convergent.However, elitist set selection is imperative for the conver-gence of all immune system based optimization algorithms[19]. Since the mutations are separately performed and elitistFHSs are dynamically generated by each SU, immune systembased algorithm strictly converges to the global optimal point.Convergence of artificial immune system algorithms requiresa strict elitist selection mechanism [19] such that the artificialimmune system algorithm is convergent if an elitist set selec-tion mechanism is used. Hence, due to its elitist FHS selectionmechanism ESA is also a convergent algorithm. Although itsconvergence is not analytically proved in the paper, by meansof the simulation results in the performance evaluation part, itis easily shown that ESA is convergent.

For the spectrum access, each transmitter SU also generatesFHS including some channels on which PUs do not frequentlytransmit. In the first phase, each transmitter SU strugglesto handshake its intended receiver SU. For this handshake,each transmitter user transmits a Request-To-Sent message(RTS) in a way that it hops on its FHS. When the transmitterSU achieves to deliver a RTS packet, its intended receiver

ENTROPY BASEDSPECTRUM

IDENTIFICATION

GENERATEFREQUENCY

HOPPINGSEQUENCE

(FHS)

OVERALL ESA OPERATIONS

CHANNELSENSING

INTERFERENCE?

TRACK RECEIVERFHS

DATATRANSMISSION

TRANSMITTER

No

FHSMATURINGPROCESS

FHSMUTATION?

No

Yes

Yes

No

ELITISTFHS?

Yes

No

YesTRANSMITTER -

RECEIVERHANDSHAKESUCCESS?

SELF- NONSELFIDENTIFICATION

ANALOGY

CLONALSELECTIONANALOGY

Fig. 3. Overall ESA operation is illustrated with SI, SS, and spectrum access.All protocol steps can be easily tracked and it is seen how and which partsof ESA operation gives inspiration from the principles of immune system.

TABLE II

COMPARISON OF DISTRIBUTED OSA MAC PROTOCOLS [21].

Protocol Spec.ID.

Spec.Sharing

CC Type Scan # ofRFE

MRCC

DOSS [10]√

- Dedicated√

3 -

SCA-MAC [22] - - Dedicated√

2 -

C-MAC[12]√

- Dedicated√

1 -

AS-MAC [11]√

- Dedicated√

1 -

ESCAPE [23] - - Dedicated√

1 -

DC MAC [24] - - Dedicated√

1 -

HC-MAC [13] - - Dedicated√

1 -

BB-OSA [25] - - Dedicated - 1 -

OS-MAC [26] - - Dedicated - 1 -

HD-MAC [14]√

- Split Phase√

1 -

SRAC [15] - - Split Phase - 1 -

EvolutionarySpectrum Access(ESA)

√ √MRCC

√1

√

immediately sends a CTS frame including its FHS. This allowsthe transmitter SU to handshake with its receiver and to trackits FHS. However, as presented above, the receiver SUs mutatetheir FHSs. Therefore, the previously achieved handshakesbetween the transmitter and receiver SUs are corrupted in thecase of the mutations in the FHS of receiver SUs. In this case,the transmitter SU retries to handshake with its receiver SU.However, once the receiver SUs complete the spectrum sharingoperation such that all FHS become elitist, SU handshakes nolonger suffer from the mutation in receiver SUs.

IV. PERFORMANCE EVALUATION

In this section, the performance of the ESA algorithm isinvestigated. We first present an overview of the existing OSAtechniques in the current literature, and discuss the advantagesof ESA with respect to them. Then, we present simulationexperiment results of ESA protocol in terms of wide rangeof performance metrics. Finally, we perform numerical errortolerance analysis of ESA to the inaccuracy in SI process.

A. Overview of Existing OSA Mechanisms

The performance analysis is made for comparison withother OSA protocols. In [21], a comprehensive performancecomparison of the existing solutions are presented. In TableII, the existing OSA algorithms are compared with ESAaccording to their various properties such as dedicated channeltype and number of radio-front-ends 1.

As observed in Table II, ESA has a number of advantageswith respect to others. First of all, ESA is a unique protocolconcurrently performing SI and SS, in addition to default CRfunctionalities such as channel scanning for spectrum sensing.ESA has also a completely different control information ex-change strategy from other OSA such that ESA enables thecontrol information exchanges using all available channels,i.e., multiple rendezvous control channel (MRCC) [21], whileothers use a single control channel, which would be a majorbarrier under congestion by PU transmissions. Furthermore,ESA requires only a single radio-front-end (RFE) since itsplits the channel sensing and data transmission phases in timedomain. Consequently, ESA unifies several salient features.

B. Simulation Experiments

Here, we show the performance of ESA and compare it withgeneric OSA methods capturing the main features, i.e., SI andSS. The ones making only SI identify the available spectrumchannels ordered with respect to entropy and choose K ofthem. The ones additionally making SS choose the spectrumchannels collaboratively with each other but without changingthe resulting sequences over time. Simulation experiments areperformed in MATLAB. Unless otherwise explicitly stated, intotal N = 10 channels are used among 10 nodes, half of whichare ad hoc opportunistic transmitters and each transmitter isassumed to have a specific target receiver with a single-hopnetworking topology. The nodes use K = 6 hops in FHS andlistens to 6 channels in order to collect the entropy informationas explained in Section III. The channels are assumed to beindependent with bandwidth B = 1. Throughput performanceof ESA is measured in terms of PU channel access probability,number of channels available, number of SUs. In addition, thefairness analysis of ESA, its collision probability as well asits performance under high node mobility are also presented.

1) Throughput vs. PU channel usage: We vary the PUchannel access on-off probability and observe the effect onthroughput. It is expected that the nodes using the PU channelsare affected much more than the ones utilizing the channelsthat PU does not reside in. As shown in Fig. 4(a), throughputachieved with ESA is much higher than both cases that do notintelligently share the available channels. In addition, ESA isnot affected by increasing PU channel access probability dueto its evolutionary mechanism which converges to an optimalspectrum access as explained in Section III. The observedadvantages are because of adaptive use of channels in orderto utilize spectrum holes by using entropy information anddistributing the acquired resources in an efficient manner.

1A more comprehensive performance comparison of these existing mecha-nisms can be found in [21].

0.6 0.7 0.8 0.90

0.2

0.4

0.6

0.8

1

PU channel idle probability

Thr

ough

put (

bits

/slo

t)

ESA

w/o SS

w/o SS and SI

10 20 30 400

0.2

0.4

0.6

0.8

1

Number of channels

Thr

ough

put (

bits

/slo

t)

ESA

w/o SS

w/o SS and SI

(a) (b)

4 6 8 10 120

0.2

0.4

0.6

0.8

1

Number of SU nodes

Thr

ough

put (

bits

/slo

t)

ESAw/o SSw/o SS and SI

4 6 8 100

0.005

0.01

0.015

0.02

0.025

Number of SU nodes

Var

ianc

e of

thro

ughp

ut (

bits

/slo

t)

ESA

w/o SS

w/o SS and SI

(c) (d)

Fig. 4. The throughput and the fairness of ESA algorithm compared withthe algorithms making only SI and making SI and SS. The throughput for(a) varying primary user channel idle probability where N = 40, (b) varyingnumber of channels used, (c) varying number of nodes in the network, and(d) the fairness for varying number of nodes.

2) Throughput vs. Number of available channels: Secondly,the effects of changing the number of channels on the through-put are observed. As it can be clearly seen in Fig. 4(b),overall throughput increases with the number of channels asexpected. However, in all cases, the full utilization of thechannel resources is best exploited by ESA algorithm withSI and SS incorporated. The reason is that with a balancedand adaptive use of resources overall throughput is leveraged.

3) Throughput vs. Number of SUs: Throughput perfor-mance of evolutionary and bio-inspired nature of ESA algo-rithm is observed by varying the number of nodes. In any bio-logical process the self-adaptation achieves efficient responseto the dynamically varying size of the system autonomously.

As it is observed in Fig. 4(c), ESA without SI and SS areseverely affected by the increasing number of contending SUs.However, due to CSM, the collisions among the new SUs aremitigated, and the high throughput is maintained regardless ofthe increasing demand.

4) Fairness: Here, we investigate the fairness performanceof ESA protocol in terms of opportunistic distribution ofspectrum resources. The variance of the throughput achievedby individual SUs is used as the fairness metric. As shownin Fig. 4(d), if SS and SI were not used, the throughputvariance significantly increases with the number of SUs.As also outlined in Table II, this is, in fact, the case ofmajority of the existing OSA mechanisms in the literature.However, ESA protocol, with its immune system-inspired SIand SS mechanisms, maintains relatively constant and verylow throughput variance. This reveals that ESA provides fairshare of spectrum as well.

5) Collision Probability: In Fig. 5, overall collision prob-ability is observed during a course of ESA operation. Asexplained in Section III, ESA enables receiver SUs to hy-permutate their FHS to optimally share the available spectrum

0 2000 4000 6000 80000

0.2

0.4

0.6

0.8

1

Time (# of slots)

Col

lisio

n pr

obab

ility

Mobility Index = 0,Throughput = 0.805 bits/slot

Fig. 5. Collision probability, and the convergence of ESA algorithm.

0 2000 4000 6000 80000

0.2

0.4

0.6

0.8

1

Time (# of slots)

Col

lisio

n pr

obab

ility

Mobility Index = 0.2,Throughput = 0.705 (bits/slot)

0 2000 4000 6000 80000

0.2

0.4

0.6

0.8

1

Time (# of slots)

Col

lisio

n pr

obab

ility

Mobility Index = 0.4,Throughput = 0.687 (bits/slot)

(a) (b)

0 2000 4000 6000 80000

0.2

0.4

0.6

0.8

1

Time (# of slots)

Col

lisio

n pr

obab

ility

Mobility Index 0.6,Throughput = 0.575 (bits/slot)

0 2000 4000 6000 80000

0.2

0.4

0.6

0.8

1

Time (# of slots)

Col

lisio

n pr

obab

ility

Mobility Index = 0.8,Throughput = 0.506 (bits/slot)

(c) (d)

Fig. 6. The throughput performance with respect to the effect of mobilityover time. (a) Mobility index = 0.2 (b) Mobility index = 0.4 (c) Mobilityindex = 0.6 (d) Mobility index = 0.8.

bands with the minimum collision probability. This optimalpoint can be easily observed in Fig. 5 such that ESA com-pletely avoids all possible collisions and reaches to the optimalpoint in which SUs no longer observes collisions. Note alsothat in Fig. 6, all x-axes implicitly correspond to time sincethe duration of each time slot is fixed to a τ seconds. Forexample, τ is selected as 1ms, the convergence of ESA canbe achieved within approximately 5 seconds. Hence, based onthe evolutionary mutation mechanisms over FHS of SUs ESAsignificantly improves overall collision probability regardlessof initial state of the network.

6) Node Mobility: Here, we investigate the effects of mo-bility in the CRAHNs on the performance of ESA protocol.To this end, we define an evaluation variable, i.e., mobilityindex representing the ratio of the mobile SUs in the network.Here, a number of SUs randomly change their locations witha random velocity. Hence, as the mobility index increases, thenetwork becomes more mobile. In Fig. 6(a),(b),(c), and (d), thevariation of the collision probability with time for the case ofmobile nodes with a varying mobility index value is shown.

In Fig. 6(a), the network is initially set as immobile and themobility index is set as 0 for a specific number of slots. In this

interval, ESA completely avoids overall collision probabilityafter approximately 5000 time slots. After the convergence, the20% of the nodes start moving, i.e., the mobility index 0.2. Atthis point (approximately between slot 4000 and 5000), pre-vious FHS are invalidated by the SU mobilities and collisionarises. However, ESA can easily recover the network fromthe collisions by re-initiating evolutionary hypermutations asobserved Fig. 6(a).

In Fig. 6(b), (c), and (d), the same immobile and mobile net-work states are consecutively set using the mobility index 0.4,0.6, and 0.8, respectively. As the mobility index increases, theachieved throughput decreases, due to inevitable but temporarycollision. However, in all cases, the ESA protocol operationconverges to very low collision probability case regardless ofthe mobility level in the networks.

Moreover, an evolutionary feature of ESA can be observedin the convergence behavior in Fig. 6(b), (c), and (d). Ifthe convergence time lasts for relatively a long time in theimmobile phase of the network, the network converges morequickly after the mobile phase of the network. Conversely, ifthe convergence lasts for a small amount of time, it strugglesfor a relatively longer time to converge to a low collision state.This is an evolutionary mechanism such that the robustnessagainst the undesirable situations such as node mobility andinterference can be improved with more exposition to theseundesirable situations for a relatively long time.

C. Error-tolerance to Inaccuracy in Spectrum Identification

Since spectrum sensing process is prone to false alarmand mis-detection probabilities, SI needs to be resilientlyperformed in order not to interfere PUs and to maximizespectrum utilization. Similar to error-tolerant self-nonself iden-tification mechanism in immune system, ESA allows each SUto separately tolerate the identification errors by reinforcingthe information about SI. Here, we analytically investigate thiscase using the Gaussian channel model.

When a SU senses a channel, the received signal can beevaluated as [20]

x(t) ={

n(t), H0

s(t) + n(t), H1(5)

where H0 and H1 are the hypotheses corresponding to idle andbusy channel cases, respectively. s(t) is the communicationsignal and n(t) is a zero-mean Gaussian noise.

The output of the energy detector, i.e., Y , has the Chi-square distribution that can be approximated as a Gaussiandistribution [20]

Y ≈{ N (nσ2

n, 2nσ4n), H0

N(n(σ2

n + σ2s), 2n(σ2

n + σ2s)2

), H1

(6)

where n is the number of samples, σ2s and σ2

n are the varianceof signal s(t) and noise n(t). If we assume that a PU accessesthe sensed channel with probability pa, using (6) false alarmpf and mis-detection pm probabilities can be given as

pf = pa

(1 − Q

( λ − n(σ2n + σ2

s)√2n(σ2

n + σ2s)2

))(7)

0.2 0.3 0.4 0.5 0.6 0.7 0.80.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

pa

Hij

σn2=2

σn2=4

σn2=6

σn2=8

σs2=10

λ=5n=5

Fig. 7. Hji with respect to varying pa for different σ2

s to show the errortolerance capability of ESA.

pm = (1 − pa)Q(λ − nσ2

n√2nσ4

n

)(8)

where Q(.) is the Q-function and λ is the detection thresholdof the energy detector. In order to show the error toleranceof ESA to potential inaccuracies in spectrum identificationprocess, in Fig. 7, we plot the spectrum identification entropyfor a channel, i.e., Hj

i in (2), with respect to channel accessprobability of PUs for this channel, i.e., pa. Despite increasingpa and channel noise variance σ2

s , ESA provides the resilienceSI. For the case in which pa is high, Hj

i becomes also high.However, as pa decreases, Hj

i becomes smaller. This allowseach SU to separately identify the spectrum opportunitieswith smaller Hj

i in which PU activities are minor. Entropy-based SI is not affected by the probabilities of false alarmand mis-detection in channel sensing although noise varianceincreases. This shows the error tolerance capability of ESA tothe inaccuracy in SI.

V. CONCLUSION

In this paper, we introduced a new bio-inspired OSA mech-anism, i.e., immune system-inspired evolutionary spectrumaccess (ESA), for CRAHNs. We have shown that immunesystem-inspired opportunistic access mechanism has manyadvantages coming naturally form the inspired process. Theefficient, fair and high-level usage of resources are succeededby ESA with the mechanisms of inspired self-nonself detectionand clonal selection. These natural methods make the systemself-organized, self-adapted and self-regulated resulting in anevolutionary OSA system. Furthermore, unlike the existingOSA mechanisms, ESA does not require a single dedicatedcontrol channel. The performance analysis results show thatESA provides more fair and effective utilization of commu-nication channels where SUs try to use the channels in acognitive sense without disturbing PU. In addition to this,performance of ESA is not affected by the high traffic loadincurred by the nodes, instead, the system evolves faster withthe presence of high contention and converges to an optimalstate earlier especially in the case of high node mobility.

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