A Cooperative MAC with Efficient Spectrum Sensing Algorithm for Distributed Opportunistic

A Cooperative MAC with Efficient SpectrumSensing Algorithm for Distributed Opportunistic

Spectrum NetworksAmmar Alshamrani, Xuemin (Sherman) Shen, and Liang-Liang Xie

Department of Electrical and Computer EngineeringUniversity of Waterloo, Waterloo, ON, Canada, N2L 3G1

Email: {asaalsha, xshen}@bbcr.uwaterloo.ca, [email protected]

Abstract—The spectrum scarcity has led to rethink in thecurrent frequency spectrum usage and develop a new con-cept of wireless networking. Opportunistic Spectrum Net-works (OSNs) have been considered as a promising solutionto the problem of spectrum shortage. In this paper, in orderto compensate for the need of complex hardware, a noveland efficient Medium Access Control (MAC) framework thatintegrates a kind of cooperative spectrum sensing methodat the physical layer into a cooperative MAC protocol isdeveloped for distributed OSNs considering the require-ments of both the primary and secondary users. For theMAC framework, an innovative deterministic sensing policycalled Allocated-group Sensing Policy (ASP) is proposed toidentify the spectrum opportunities based on a dynamic IDnumbering approach, and its effectiveness is demonstratedby comparison with two random sensing policies. Moreover,a computationally simple but efficient sensing algorithmis developed to assist each sensing user to identify theoptimal number of channels to sense and the optimal sensingduration. It is demonstrated that the proposed cooperativeMAC framework can efficiently achieve the ultimate goal ofthe OSNs even with only a small number of sensing userseach equipped with a single cognitive radio transceiver.

Index Terms—Opportunistic Spectrum Networks (OSNs),Medium Access Control (MAC), Dynamic ID Numbering,Sensing Policy.

I. INTRODUCTION

In the last two decades, the world has witnessed rapidincreasing applications of wireless networks; however,with the past and current fixed spectrum allocating reg-ulations, the natural resource of the frequency spectrumis running out. This has led to what so-called spectrumscarcity [1]. Since the spectrum is the lifeblood of wirelesscommunications, it should be used efficiently, or theemerging wireless applications would not be supportedany more. In this paper, we are interested in a kindof wireless networks that is considered as a promisingsolution to the spectrum scarcity. These networks areknown as Opportunistic Spectrum Networks (OSNs). It isworth mentioning that Cognitive Radio Networks (CRNs),Dynamic Spectrum access Networks (DSNs), and Oppor-tunistic Spectrum access Networks (OSNs) terminologiesare often used interchangeably in the literature to describe

Manuscript received April 14, 2009; revised August 8, 2009; acceptedAugust 20, 2009.

the new emerging wireless networks that use the CR tech-nology to exploit the spectrum opportunities. However, weprefer to use the term OSNs that exactly describes whatthis network means.

There are two types of users in OSNs: Primary Users(PUs) owning licenses to use exclusively assigned spec-trum bands, and Secondary Users (SUs) having no spec-trum licenses but seeking for any spectrum opportunities.The SUs can make use of the unused spectrum portionsof the PUs if they do not make any harmful interferenceto the PUs. Therefore, the SUs should be able to carryout two key functions: spectrum opportunity identificationand spectrum access [2].

Although the basic idea of OSNs seems simple, theimplementation of it is challenging. The co-existence withthe primary networks, which may comprise of differenttypes of PUs, and the spread of the spectrum opportunitiesover wide spectrum bands and their consequent challengesmake the research in this area very challenging [3].Fortunately, with the advanced technologies in the newevolution of Cognitive Radios (CRs), implementation ofOSNs can be envisioned. Cognitive Radios are promisingtechnologies that can be used by SUs to perform twokey functions: detecting the spectrum opportunities byspectrum sensing and exploiting these opportunities byspectrum accessing [4], [5]. These two functions arerelated and one cannot work properly without the other.This leads to the concept of cross-layer design in wirelessnetworks. Since distributed networks may be the practicalarchitecture that attracts the future applications of spec-trum secondary usage, we will focus on distributed OSNsthroughout this paper.

Researchers in OSNs try to tackle some new challengesnot found in the traditional wireless networks. Sincemost of these challenges are related to the design of theMedium Access Control (MAC), several protocols havebeen proposed. Most of these protocols are discussed ina recent survey [6]. Indeed, the MAC protocols for OSNscan be considered in general as multichannel MAC pro-tocols with special requirements. Reference [7] providescomprehensive comparison between these multichannelMAC protocols, while [8] compares between the oppor-tunistic multichannel MAC protocols.

Some researchers have proposed using Markov decision

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© 2009 ACADEMY PUBLISHERdoi:10.4304/jcm.4.10.728-740

process to develop an analytical framework for OSNs. In[9], each SU pair decides, without cooperation with otherSUs, when to sense and when to access the spectrumbased on the Partial Observable Markov Decision Process(POMDP) framework where optimal and suboptimal de-cision rules are discussed. In [10], a separation principlethat optimizes the spectrum sensing at the physical layerand then optimizes the spectrum access at the MAClayer is used to reduce the computational complexityof the POMDP approach. However, the computationalcomplexity of this approach may still not be suitable forthis kind of networks.

Other researchers have proposed frameworks based onstatistical information about the existence of PUs. Theauthors of [11] have proposed statistical MAC (SCA-MAC) protocols, while the proposed MAC in [12] iscalled opportunistic cognitive MAC (OC-MAC), whichis almost the same concept of that in [11] except it uses adifferent handshake mechanism. Both of these two worksuse statistical sensing about the PUs from the last timeslot, so if the status of the PUs in the current time slotsignificantly changes for any reason, the decision takenby the secondary receiver may not be the best any more,and possibly the secondary transmission may increase theinterference to the PUs to an unacceptable level.

The slotted time MAC structure approach has been pro-posed by some researchers. In [13], an opportunistic MACprotocol based on this approach for the data and controlchannels has been proposed. This protocol requires eachSU to be equipped with two radios: a traditional radiodevoted for the control channel and a Software DefinedRadio (SDR) to sense and transmit/receive data. There-fore, the hardware cost is a main issue in this study.Moreover, when the number of the SUs is small, the aver-age throughput of the secondary network is consequentlylow. In [14], a distributed multichannel MAC protocol forCRNs (MMAC-CR) is proposed based on a slotted timestructure also. It is almost similar to that of IEEE 802.11PSM, and the sensing scheme is similar to that proposedfor IEEE 802.22, i.e., on two phases: fast sensing and finesensing. Although, the used sensing scheme may increasethe accuracy of PUs detection, it is at the cost of reducingthe remaining time for data exchanging; moreover, theevaluation of this protocol is mainly done by simulation,so analytical analysis is necessarily required.

The hardware constraints are the main concern in[15]. Each SU node is assumed to be equipped witha single half-duplex CR. This proposed hardware con-strained MAC (HC-MAC) deals well with the sensing andtransmission constraints using optimal stopping time rule;however, the simultaneous transmission from farawaysecondary nodes may lead to inaccurate detection of thePUs; moreover, the spectrum utilization is not optimizedsince there may be some available channels not exploitedby any secondary users.

The sensing time is a key parameter in the spectrumsensing in OSNs; therefore, it should be optimized to ob-tain a reasonable sensing result that maintains acceptable

interference to the primary network while maximizingthe spectrum utilization. In [16]-[22], the sensing time isoptimized based on some proposed models and scenarios.

Although several MAC protocols have been proposedto tackle some of the new challenges in the distributedOSNs, designing practical and efficient such protocols isstill in its infancy, so more research efforts are needed torealize the concept of this emerging wireless networks.Cooperation between the SUs can compensate for theneed of complex hardware. Cooperation here implies thatthe SUs cooperate with each other to identify the spectrumopportunities and also to exploit these opportunities. Tothe best of our knowledge, there is no such study thatprovides this twofold cooperation in distributed OSNsconsidering the hardware limitations and costs in additionto the requirements of both the primary and secondaryusers. In this paper, we develop a MAC frameworkthat can handle this cooperation to achieve the ultimategoals of the OSNs without need for complex hardware.In order to control the spectrum sensing and accessingefficiently, we propose a new dynamic ID numberingapproach that helps out to order the SUs in a distributedmanner. Furthermore, we investigate a computationallysimple but efficient sensing algorithm that relies on aninnovative sensing policy called Allocated-group SensingPolicy (ASP) in order to assist the SUs to optimallyidentify the spectrum opportunities online.

The remainder of this paper is organized as follows.Section II provides the system model. The proposed MACframework is presented in Section III. In Section IV,the Allocated-group Sensing Policy is investigated andevaluated. Using this sensing policy, the sensing timeduration and hence the sensing algorithm are analyzedand evaluated in Section V. Finally, Section VI concludesthis paper.

II. SYSTEM MODEL

In OSNs, as mentioned before, there are two types ofnetworks: primary and secondary. The primary networksconsist of PUs that have licenses to exclusively use oneor more spectrum bands; however, they do not use thespectrum all the time and all the locations, so somechannels in these bands are spatiotemporal underutilized.In order to efficiently utilize the spectrum, the spectrumregulator coordinates with the primary network holders toopen up these bands to be utilized by a secondary networkthat consists of SUs seeking for spectrum opportunities.Moreover, the PUs have the priority to use the spectrum,and can reclaim the channel(s) at any time withoutnotifying the SUs.

A. Primary Network Model

Consider primary networks consisting of N non-overlapped channels numbered from 1 to N based on itssequence in the spectrum. Each channel of the N licensedchannels can be modeled at any time slot as ON/OFFsource, i.e., either occupied by a PU or idle. Therefore,

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© 2009 ACADEMY PUBLISHER

the states of each channel can be modeled as two statesMarkov chain, and the i-th channel utilization at any timeslot can be written as:

δi =βi

αi + βi; i = 1, 2, ..., N, (1)

where αi is the probability that the channel i transits fromstate ON to state OFF, and βi is the probability that ittransits from state OFF to state ON.

B. Secondary Network Model

The secondary network consists of M total numberof SUs seeking for spectrum opportunities over the Nlicensed channels. Any secondary node is equipped witha single CR transceiver that has the ability to sense at mostL channels in sequence and access at most K channelssimultaneously based on its hardware and technologyconstraints, where 1 ≤ L,K ≤ N . The errors that mayoccur during the spectrum sensing at the physical layerwill be considered.

In order to exchange their control messages, the SUsuse a local Common Control Channel (CCC) dedicatedto them. The CCC can be either a channel in unlicensedbands or a channel licensed to the SUs especially for thepurpose of spectrum sharing in OSNs. The SUs that are inthe range of each other form a single-hop ad hoc networkcovering a relatively small area; however, the SUs maycover larger area in a multi-hop network scenario. In thispaper, we consider the single-hop case. Assuming thecommunication range of the legacy PUs is larger than thatof the SUs, each single-hop SUs group can be consideredto be under the same coverage of the PUs set.

III. THE MAC FRAMEWORK

We propose a cooperative MAC framework based onthe slotted time MAC structure shown in Fig.1. All thelicensed channels and the control channel are slottedinto time slots each with T time duration. Moreover,the control channel is further divided into four phases.The first phase is called First Registration and SensingPhase (1RSP), the second phase is the Reporting Phase(RP) that is divided into N mini-slots corresponding tothe N licensed channels, the third phase is the SecondRegistration and Competing Phase (2RCP), and the fourthphase is the Leaving Phase (LP). The data will beexchanged on the identified available channels duringthe 2RCP and LP phases together, so this duration iscalled Data Period (DP). The purposes of these phasesare discussed in Section III-A. The primary signals areassumed to be constant during each time slot, i.e., eachchannel is assumed to be either occupied by a PU or idle.Moreover, the duration of the time slot must be chosen tobe large enough for the SUs to exchange their control anddata packets; however, it should not exceed a thresholdthat maintains the potential interference to be tolerable tothe PUs (see Section V).

Fig. 1. The MAC framework.

A. MAC Protocol Overview

Based on the phases illustrated in Fig.1, the proposedMAC protocol is described in the following.

1) First Registration and Sensing Phase(1RSP):• At the beginning of each time slot, the first winner

from the last 2RCP phase sends beacon B1 fortwo reasons. The first reason is to synchronize thenetwork. Second, this beacon contains importantinformation broadcasted to the other SUs. This in-formation includes the new total number of the SUsin the network, M , the number of the winning usersin the last 2RCP phase, Mw, and the number of theleaving users in the LP phase, Ml, in addition to theold dynamic ID numbers of the winning and leavingusers.

• All other users use this information to calculate thenew number of the users involved in the sensingprocess, i.e., Ms = M −Mw, and to update theirdynamic ID number as will be discussed in thedynamic ID updating algorithms in Section III-B.

• Spectrum Sensing: the sensing users sense the Nchannels based on a sensing algorithm, which willbe discussed in Sections V.

• First Registration: any new user having packets tosend must exchange Request-to-Register (RTR) andRTR-Acknowledgment (RTR-ACK) with the firstwinner and get its dynamic ID number based ona distributed algorithm that will be discussed inSection III-B.

2) Reporting Phase (RP):• Each user involved in the sensing process sends tones

at the mini-slots only if it detects primary signalson the corresponding sensed channels to inform thewinning users about the existence of PUs on thesechannels.

• All the winning users monitor the CCC at this phaseand get full picture about the spectrum opportunities.

• At the end of this phase, the first winner sendsanother updating beacon, i.e., B2. This beacon has



twofold. The first is to tighten the network synchro-nization, and the second is to update the value ofM .

• The other users use beacon B2 to update theirinformation.

3) Data Period (DP):• Based on the number of the identified available chan-

nels, Na, each winning user calculates how manychannels can be accessed as follows. If Mw ≥ Na,then only a number of assigned winning users, Maw,which are the first Na of the winning users, are eligi-ble to access the spectrum. Each of them will accessone channel based on its order and the correspondingavailable channel order. The remaining winning usersshould join the other competing users in the next2RCP phase. If Mw < Na, then each winninguser can access at least na = bNa/Mwc channels,where bxc means the real number x is roundeddown to the nearest integer number; however, whenNa/Mw is not an integer number, the remainingNa−na channels should be divided equally betweenthe first winning users based on their order and thecorresponding order of the available channels.

• The eligible winning users access the assigned avail-able channels using the Orthogonal Frequency Divi-sion Multiplexing Access (OFDMA) technique andexchange their data and acknowledgments on thosechannels.

4) Second Registration and Competing Phase (2RCP):

• All the remaining users, i.e., M − Maw, competeusing a fair access mechanism, such as RTS/CTS,and record their winning orders, i.e., the first, second,and so on.

• Any intended receiving user that is not registered yetin the network will get its dynamic ID number fromthe access mechanism based on its winning sequenceand includes it in the replying message. The otherwinning and competing users update the M value.

5) Leaving Phase (LP):• Users that want to leave the network send short

messages called Request-to-Leave (RTL) containingtheir dynamic ID numbers in this phase. They mayleave either to save their power when they do nothave packets to send or to join another network.

• The new first winning user records the Ml and theirdynamic ID numbers to broadcast them in the nextbeacon B1.

B. Dynamic ID Numbering

The SUs can cooperate efficiently to sense and accessthe spectrum if each has a unique ID number that maychange dynamically each time slot. As mentioned before,the first winning user broadcasts B1 that includes thenew numbers M , Mw, and Ml in addition to the old IDnumbers of the winning pairs and the leaving users wherethese ID numbers are ordered from low to high; moreover,

it sends B2 to update the value of M . In the following,all distributed algorithms used in obtaining or updatingthe dynamic ID numbers of the SUs in the network areprovided.

1) The new winning users update their dynamicID numbers based on their winning order usingAlgorithm 1 as follows:Algorithm 1i-th winning user← ID # (M − i+ 1),where 1 ≤ i ≤Mw.

2) The sensing users having old dynamic ID numbersless than or equal to the number of the new sensingusers, i.e., Ms , do nothing, while the sensingusers having old ID numbers greater than the newnumber of Ms must update their ID numbers usingAlgorithm 2 as follows:Algorithm 2For each sensing user, if its old ID > new Ms,then,Last old ID ← 1st ID on B1,i-th last old ID ←i-th ID on B1,where i=1, 2,... .

Using steps 1 and 2, we have Ms sensing userswith new ID numbers ordered from 1 to Ms andMw winning users with new ID numbers orderedfrom Ms + 1 to M .

3) During the 1RSP phase, each new user exchangesregistering packets with the first winner and getsits ID number based on its appearance usingAlgorithm 3 as follows:Algorithm 3i-th new user← ID # (M + i),where i= 1, 2, ... .

4) During the 2RCP phase, the receiving user thatis not already registered in the network gets itsdynamic ID number from the access mechanism’spackets based on their sequence.

C. General Notes

1) Since the first winning user has the key functionof broadcasting B1 and B2 in this protocol, thereshould be a backup user to do this function in casethe first winner fails to do it for any reason. Theother winning users can do the same job, so theymonitor the first winner, and if it fails, the secondwinner will replace it and if not the third and so on.The failure user will realize this and should becomethe last winner.

2) In case there are only two users in the networkwanting to communicate with each other, the onethat wants to transmit sends B1, and the receiversenses the channels while the transmitter registersany new user, then the receiver reports its obser-vation on the RP phase and the transmitter figures



out which channels are available to be used in theDP duration. If there are new users registered at the1RSP phase, the network will become with manyusers and everything works as discussed previously,and if not, the procedure of two users will berepeated.

3) In order to establish a secondary network, any SUwant to communicate with other SUs scans theintended CCC. If it does not find control packetson this CCC, then it will realize that it may bethe first user that should establish the network andsend beacon B1. If there is a collision with othersecondary user trying to establish the network too,both of them will realize that the network is stillempty of users, so they have to try to send B1 againusing random back off time.

IV. SENSING POLICY

A spectrum sensing policy is required to managethe detecting of the spectrum opportunities. This policyshould assist to identify which and how many channelsare available to be used by the SUs. Therefore, when thepercentage of the sensed channels out of the N channelsincreases, the throughput of the secondary network isexpected to increase based on the activity of the PUs.

The channel is said to be available at a specific timewhen it is not used by any PU at that time; however, thechannel utilization given by (1) is unknown to the SUs.Let Na be the random number of the available channelsat time slot t. Knowing that the channels that can beexploited are just that sensed by the SUs, the averagenumber of the available and sensed channels at time slott can be calculated as:

Na =NPsens∑n=0

nPr{Na = n, sensed}, (2)

where NPsens is the number of the sensed channels out ofthe N channels. The activities of the PUs on each channel,δi, can be estimated by collecting statistical informationduring the previous time slots and then using a techniquesuch as Bayesian learning [21]. However, in order tosimplify the analysis without loss of generality, let δi = δfor all i, then Pr{Na,sensed} is binomial and (2) can berewritten as:

Na =NPsens∑n=0

n

(NPsens

n

)(1− δ)nδ(NPsens−n)

= (1− δ)NPsens.(3)

In other words, more spectrum opportunities can beexploited if a proper sensing policy is investigated to senseas many as possible channels.

The sensing process is considered to be ideal if the allN licensed channels can be sensed. Therefore, Psens = 1in ideal sensing case. Based on the system model, eachSU can sense only L out of the N channels, so thePsens highly depends on Ms and L, during the 1RSPphase. In the following, three sensing policies namely

Fig. 2. Channel group selection in the RSP policy.

Random Sensing Policy (RSP), Distinct-group SensingPolicy (DSP), and Allocated-group Sensing Policy (ASP)are proposed and discussed. Eventually, the best of themcompared to the ideal sensing case will be chosen as thesensing policy that can be integrated in the proposed MACframework.

A. Random Sensing Policy

In the RSP policy, each sensing user chooses indepen-dently and uniformly L consecutive channels out of theN channels as shown in Fig. 2. Therefore, there are anumber of possible channel groups given by:

Nr = N − L+ 1. (4)

Similar to [13], the probability mass function (pmf) ofthe sensed channels can be modeled as (Nr + 1)-stateMarkov chain. However, each SU is assumed to be ableto sense only one channel in [13], while each user cansense L channels in sequence in this paper. The Markovchain can be written mathematically as:

qij =

i/Nr; j = i,

1− i/Nr; j = i+ 1,0; o.w.

(5)

and the probability transition matrix of this chain can begiven by:

Q = {qij} ; 0 ≤ i, j ≤ Nr. (6)

The probability of C channels are sensed by Ms sensingusers can be evaluated by calculating the Ms-step transi-tion probability from state 0 to state c as follows [13]:

Pr {C = c} = QMs

(0,c), (7)



where the right hand side of (7) means the element in row0 column c of the Ms-step transition matrix. Therefore,the probability of the sensed channels can be given as:

Psens =1Nr

Nr∑c=0

cQMs

(0,c). (8)

B. Distinct-group Sensing Policy

For the DSP policy, the N channels are divided intodistinct non-overlapped channel groups given by:

Nd =⌈N

L

⌉, (9)

wheredxe means the real number x is rounded up tothe nearest integer number. Each sensing user choosesindependently and uniformly one of the groups and startsto sense L channels beginning by the first channel of thechosen group as shown in Fig. 3. When N/L is not aninteger number, the last group will contain some channelsoverlapped with the previous group. Similar to what havebeen done in the RSP policy, (Nd+1)-state Markov chainis used to find the pmf of the sensed channels. Finally,the probability of the sensed channels in this policy canbe given as:

Psens =1Nd

Nd∑c=0

cQMs

(0,c). (10)

C. Allocated-group Sensing Policy

The randomness in both RSP and DSP policies isexpected to decrease the average number of the sensedchannels and consequently to decrease the averageaggregate throughput of the secondary network. Themain purpose of ASP policy is to ensure that eachsensing user will sense different channel group from anyother group sensed by any other sensing user when thenumber of the sensing users is less than or equal to thenumber of the channel groups. Using the same channelgrouping of DSP policy shown in Fig. 3, each sensinguser chooses a group deterministically instead of randomselection as follows. After updating the dynamic IDnumbers of all the SUs and before starting the sensingprocess at the 1RSP phase, each sensing user calculateshow many channel groups there are based on (9) andchooses a specific group using Algorithm 4 as follows:Algorithm 4Calculate num = mod(user-dynamic-ID, Nd)If num = 0, choose channel group # Nd.Otherwise, choose channel group # num.

Therefore, the percentage of the sensed channels in thispolicy can be given as:

Psens =

{LMs

N ; Ms < Nd,

1; Ms ≥ Nd.(11)

Fig. 3. Channel group selection in the DSP policy.

D. Performance Evaluation

The performance of the proposed sensing policies dis-cussed above will be evaluated in this subsection. Theperformance here means the percentage of the sensedchannels out of the N licensed channels, i.e., Psens.Since the channel group in RSP and DSP policies arechosen randomly, we use simulation in order to validatethe analytical results.

Fig. 4 shows the performance comparison of the threeproposed sensing policies with respect to the number ofthe sensing users. It can be seen that the ASP policy,which is a deterministic sensing method, outperformsthe other two random policies, where it needs onlyMs = N/L sensing users to achieve the ideal sensingcase. However, due to the randomness of the other sensingpolicies, more sensing users are required to achieve theideal sensing case, e.g., around 40 sensing users arerequired for the DSP policy while more and more sensingusers are required for the RSP policy. Moreover, theDSP policy is better than that of the RSP policy sincethe number of the channel groups in the DSP policyis less than that of the RSP policy. It is obvious thatthe simulation and analytical results are almost identicalfor both DSP and RSP policies, which verifies the usedanalytical expressions.

It is desirable also to examine the performance ofthese sensing policies with respect to the number ofthe sensed channels per user. As shown in Fig. 5, theperformance of the ASP policy increases sharply withincreasing L until saturates at the ideal sensing case oncethe number of the sensed channels per user becomesL = ceil(N/Ms). However, using this number of sensingusers, the performance of the RSP policy reaches the idealsensing case only when each sensing user can sense all



Fig. 4. Performance comparison between the proposed sensing policies,N=20 and L=2.

Fig. 5. Performance comparison between the proposed sensing policies,N=20 and Ms = 5.

the licensed channels. Moreover, the performance of theDSP policy increases in steps based on the number of thechannel groups and the overlapped channels between thelast two channel groups if N/L is not an integer number.The simulation results in this figure are also consistentwith the analytical results.

From these two figures, the ASP is able to sensehigher number of the intended licensed channels, i.e.,more spectrum opportunities can be identified even withlower number of sensing users. Therefore, the ASP policycan be integrated into the proposed MAC framework inorder to manage the SUs involving in the sensing processto identify as many as possible spectrum opportunities.

Fig. 6. The MAC frame time.

V. SPECTRUM SENSING ALGORITHM

The required duration for the spectrum sensing isrelated to two key issues in OSNs: the spectrum utilizationand the interference to the PUs. One of the principles ofOSNs is to maximize the spectrum usage by utilizing effi-ciently the available unused channels, so SUs are requiredto detect the spectrum opportunities as fast as possible inorder to exploit these opportunities as long as possiblefor transmitting the secondary traffic. However, the SUsmust maintain its potential interference to the PUs undera predetermined level acceptable by the PUs; therefore,the SUs must sense the spectrum for enough time tomeet this interference constraint. These two requirementsnecessitate designing the MAC frame time shown in Fig. 6in an optimal way.

A. Spectrum Utilization

The spectrum utilization can be defined as the percent-age of time that the sensed channels are utilized; therefore,the required spectrum utilization is related to the sensingpolicy and the frame time of the proposed MAC protocolas follows:

η =T − Tc − τ

TPsens, (12)

where T is the overall time slot duration, and τ is thesensing duration during the sensing phase. In addition,Tc is the time duration for the control messages, whichcan be given by:

Tc = TB1 + TB2 +NTms + 5SIFS, (13)

where TB1 and TB2 are the time duration for beacon pack-ets B1 and B2 respectively and Tms is the time duration ofeach mini-slot corresponding to the N licensed channels.Moreover, a Short Inter Frame Space (SIFS) time is usedin order to give time for the propagation delay and fortuning the transceiver to the next phase.

B. Interference to the PUs

The potential harmful interference to the PUs mayhappen when the SUs transmit for longer time thanthe tolerant interference duration acceptable by the PUs.Moreover, the potential interference may happen due tosensing errors made by the SUs. When the SUs identifysome licensed channels as idle and send packets on themwhile they are occupied, there will be collisions with theprimary traffic. The acceptable sensing error level of theSUs can be interpreted in terms of the required detectionlevel of the PUs.



In the sensing at the physical layer, there are two relatedhypothetic parameters: the probability of detection, Pd,and the probability of false alarm, Pf . The probabilityof detection is a measure of the ability of the SUs todetect the presence of the primary signals, and it isdesirable to be maximized in order to protect the PUsfrom the interference of secondary signals. However, theprobability of false alarm is the probability of announcinga primary signal is present while it is not, and it isrequired to be minimized in order to increase the spectrumopportunities. Using a simple energy detector, these twoprobabilities can be related for each licensed channel asfollows [17]:

P(i)f = Q

(√2γ + 1Q−1

(P

(i)d

)+√t(i)s Bγ

), (14)

where γ is the SNR detection sensitivity of the detector, Bis the bandwidth of the sensed channel, ts is the requiredsensing time for channel i, and Q(.) is the Q-function,which is given by Q(x) = 1√

2π

∫∞x

exp(−t2/2)dt.

C. Average Aggregate Throughput

In order to obtain higher throughput for the secondarynetwork, it is important to design the parameters of thesensing duration in an optimal way. Since the data packetsare sent during the Data Period, DP, the average numberof the available sensed channels given by (3) can beexploited during this time only. Suppose that the SUsdivide the intended spectrum bands into N channels withequal bandwidth, B, so without considering the sensingerror, the average aggregate throughput of the secondarynetwork can be given as:

Φ =T − Tc − τ

T(1− δ)NBPsens. (15)

Let us define the normalized average aggregate throughputof the secondary network as:

Θ =Φ

(1− δ)NB=T − Tc − τ

TPsens. (16)

Now, considering the sensing error and using the ASPsensing policy, (16) becomes as:

Θ =

1N

MS∑j=1

Lj∑i=1

T−Tc−τT

(1− P (i)

fj

); MsLj ≤ N,

1N

N∑i=1

T−Tc−τT

(1−Q(i)

f

); MsLj > N.

(17)where τ is the sensing duration, P (i)

fjis the probability

of false-alarm by user j for channel i, and Q(i)f is the

probability of false-alarm in the cooperative sensing ofchannel i, which is a kind of OR-rule decision cooperativesensing. These values can be given respectively as:

τ = maxj

Lj∑i=1

t(i)sj; 1 ≤ j ≤Ms, (18)

P(i)fj

= Q

(√2γj + 1Q−1

(P

(i)dj

)+√t(i)sj Bγj

), (19)

Q(i)f = 1−

ui∏j=1

(1− P (i)

fj

), (20)

where ui in (20) means the number of the sensing userscooperate to sense channel i.

It is clear from (17) that maximizing the averagethroughput leads to maximum spectrum utilization, so oneobjective of the OSNs is met. The design of the spectrumsensing duration now becomes an optimization problemthat can be defined as:

maxτ>0

Θ

s.t. P(i)d ≥ P

thd ,

T ≤ Tmax,

(21)

where P thd is the probability of detection threshold andTmax is the maximum time slot duration. These twoparameters should be chosen to maintain the interferenceto the primary network under a specific level. It is obviousthat these two parameters depend on the traffic typeof the primary network, and for each primary network,there may be different requirements of these parameters.However, determining the values of these two parametersis beyond this research work at this time, so we assumethat they are known, e.g., given by the spectrum regulatoror the primary networks’ owners.

The solution of (21) can be simplified based on theproposed system model. All the SUs are assumed to beequipped with identical CRs that have the same SNRdetection sensitivity, and assuming that they detect at theminimum SNR, so they have the same ability of spectrumsensing, i.e., γj = γ. Moreover, according to the proposedprotocols each sensing user is required to sense the samenumber of channels, so (17) can be rewritten as:

Θ =

{MsLN

(T−Tc−Lts)T (1− Pf ) ; MsL ≤ N,

T−Tc−LtsT (1−Qf ) ; MsL > N.

(22)

In the OR-rule cooperative sensing, the probability offalse-alarm, Qf , can be found as:

Qf = 1− (1− Pf )uc , (23)

where Pf is the individual probability of false-alarm madeby each cooperative sensing user and uc is the number ofthe cooperative sensing users. However, according to theASP sensing policy, some channels may be sensed bydifferent numbers of sensing users, so we have to findthese numbers first.

In the ASP sensing policy, the number of the cooper-ative sensing users, uc, and the number of the channelssensed cooperatively, Nc, when the number of the sensingusers is greater than the number of the channel group,i.e., Ms > Nd where Nd = dN/Le , can be found as(24). Therefore, the average value of the probability ofcooperative false-alarm given in (23) can be obtained as(25).

However, it is known that the cooperative sensingincreases the probability of detection as well as theprobability of false-alarm, but increasing the false-alarm



uc =

uc1 = bMs/Ndc ,uc2 = uc1 + 1, ;uc3 = 2uc1,

Nc =

Nc1 = N −Nc2 −Nc3,Nc2 = mod(Ms, Nd)L,Nc3 = L−mod(N,L).

(24)

Qf = 1−(Nc1N

(1− Pf1)uc1 +Nc2N

(1− Pf2)uc2 +Nc3N

(1− Pf3)uc3

). (25)

is not desirable in OSNs. In order to balance betweenthese two probabilities, each cooperative sensing user isrequired to recalculate its requisite individual probabilityof detection based on the number of cooperative sensingusers. The probability of detection in OR-rule cooperativesensing is given by:

Qd = 1− (1− Pd)uc . (26)

Now, for a given value of Qd, which maintains thepotential interference of the SUs to the PUs under aspecific level, the individual detection probability thateach sensing user should meet can be found as:

Pd = 1− (1−Qd)1/uc . (27)

Therefore, the individual false alarm probabilities Pf1,Pf2, and Pf3 in (25) can be obtained as (28). Finally,the normalized average throughput given by (22) can berewritten as (29).

Thus, the optimization problem in (21) can be solvedusing (29) subject to Qd ≥ P thd and T ≤ Tmax. Bythis optimization problem we want to find the optimalsensing duration, i.e., τ = Lts, which depends on twovalues: the required sensing time for each channel andthe number of the sensed channels that each user cansense. Our ultimate goal is to develop a sensing algorithmthat can be executed online to find the optimal sensingduration. In the following we will discuss how to developthis algorithm.

D. Sensing Algorithm

The time required to sense each channel, ts, is expectedto be small for practical threshold values of the requiredprobability of detection and the probability of false-alarm,say 0.95 and 0.01 respectively. That means, even if wewant to find the optimal value of the sensing time,its acceptable range will be small and does not affectthe sensing duration significantly. Therefore, the sensingduration mainly depends on the number of the sensedchannels rather than the sensing time for each channel.The minimum required sensing time for any channel giventhe threshold values of the detection probability, P thd , andthe probability of false-alarm, P thf , can be found from(14) as:

tsmin=

(√2γ + 1Q−1(P thd )−Q−1(P thf )

γ√B

)2

. (30)

Using this value, the optimal number of the sensedchannels can be found by maximizing the throughputgiven in (29). The first constraint mentioned in (21),

i.e., Qd ≥ P thd , is implied in calculating the minimumrequired sensing time in (30). Therefore, the optimizationproblem becomes as:

L∗ = arg maxL

Θ

s.t. T ≤ Tmax,1 ≤ L ≤ N,L ∈ I,

(31)

where I means a set of positive integer numbers.This optimization problem is Nonlinear Integer Pro-

gramming (NIP). Since the value of the variable L isnot binary, one approach to solve this problem is torelax the value of L to be a real number, then solve theproblem as a standard Non-Linear Programming (NLP)problem, and finally round the output value of L up to thenearest integer number. However, solving an optimizationproblem online may not be possible in OSNs due to thetime limitation.

Fortunately, much simpler and intuitive value of theoptimal number of the sensed channels can be guessed.When there are Ms of identical users trying to senseN channels, the intuitive value of L is just dN/Mse;however, when the number of the sensing users is smallcompared to the N channels, and each of them is allowedto sense a large number of channels, this may comeat the cost of decreasing the spectrum utilization andconsequently the secondary throughput, so the value ofL should not exceed its maximum value that maximizesthe throughput when MsL ≤ N , which can be foundfrom the first part of (29) as:

∂Θ∂L

= 0,

⇒ L =T − Tc

2ts.

(32)

Therefore, the optimal number of the sensed channels canbe found as:

L∗ = min(⌈

N

Ms

⌉,

⌈Tmax − Tc

2ts

⌉). (33)

Finally, in order to set up the time of the MAC frame,each SU calculates the optimal sensing duration, which isjust the multiplication of the required minimum sensingtime given by (30) and the optimal number of the sensedchannels given by (33).

Thus, each SU should be preloaded by a sensingalgorithm that determines how long to sense eachchannel, how many channels to sense, and whichchannels to sense based on the sensing policy. This



Pfi = Q(√

2γ + 1Q−1(1− (1−Qd)1/uci) +√tsBγ

); i = 1, 2, 3. (28)

Θ =

MsLN

(T−Tc−Lts)T

(1−Q

(√2γ + 1Q−1(Qd) +

√tsBγ

)); MsL ≤ N,

T−Tc−LtsT

3∑i=1

Nci

N

(1−Q

(√2γ + 1Q−1(1− (1−Qd)1/Uci) +

√tsBγ

))uci ; MsL > N.(29)

algorithm, which is computationally simple and can beimplemented online, is summarized in the following.

Sensing AlgorithmFor each sensing user, after receiving the beacon B1 onthe CCC channel, do the following:

1) Extract the content information,2) Update the dynamic ID using Algorithm 2,3) Calculate how many sensing users using:

Ms = M −Mw,4) Calculate how long to sense each channel using

(30),5) Calculate how many channels to sense using (33),6) Calculate how many channel groups using (9),7) Determine which channel group to sense using

Algorithm 4, and8) Start to sense.

E. Numerical Results

In this subsection, some numerical results are presentedto illustrate the findings of this section. Similar to [17],the parameters B, Tmax, and γ are chosen to be 6MHz,100ms, and -15dB respectively. Moreover, we chooseTB1 = TB2 = 100µs, Tms = 10µs, and SIFS=15µs. Theprobability of detection and the probability of false-alarmare chosen to be 0.95 and 0.01, respectively, unless anyof them is stated with different value elsewhere.

Fig. 7 illustrates the minimum sensing time requiredto identify any spectrum opportunity on a sensed channelfor given values of the probability of detection and theprobability of false-alarm. It is clear that the requiredsensing time slightly increases with increasing the proba-bility of detection. This means, when the PUs need higherprotection from the potential interference of the SUs,the SUs are required to sense each channel for longertime. On the other hand, when the secondary networkneeds smaller probability of false-alarm, the SUs arerequired to spend more time to sense each channel. Forexample, in practical situations, the primary network mayneed the probability of detection to be 0.95 while thesecondary network may require the false-alarm to be 0.01,so each secondary user should sense each channel for2.7ms assuming the channels are identical.

We would like to see the behavior of the secondarythroughput with respect to the number of the sensedchannels. The normalized average throughput is plottedversus the number of the sensed channels for differentnumbers of sensing users in Fig. 8. Obviously, for eachnumber of the sensing users, there is an optimal number ofthe channels that should be sensed by each sensing user to

Fig. 7. The required sensing time with respect to the probabilities ofdetection and false-alarm.

Fig. 8. The secondary normalized throughput with respect to thenumber of the sensed channels.

maximize the secondary throughput. This optimal valueis consistent with the closed form in (33). In general,the throughput increases with increasing L until all theN channels are sensed, i.e., when MsL = N , wherethe optimal number of L appears, then the throughputdecrease beyond the optimal L. This behavior can beexplained as follows. Before the optimal value of L, there



Fig. 9. The optimal number of sensed channels using two methods.

are some channels are not sensed, so this will lower theaverage throughput; however, after the optimal L, thereare some channels over-sensed that comes at the cost ofincreasing the sensing duration, so the remaining time thatis supposed to be credit for data will be decreased. Thethroughput improves with increasing the number of thesensing users until this number is equal to the number ofthe licensed channels and then saturates.

In order to verify the closed form of obtaining theoptimal number of the sensed channels, Fig. 9 comparesthe optimal number of the sensed channels obtained fromthe solution to the optimization problem defined in (31)and the direct closed form in (33) for different numbers ofsensing users. There are small differences at some pointsbetween the two methods due to the approximation usedin solving the optimization problem when relaxing the Lto be real number and then round it up to the nearestinteger number; however, we can conclude that the twomethods are equivalent.

Fig. 10 illustrates the maximum normalized throughputand the optimal number of the sensed channels per userfor different numbers of the licensed channels. Someintuitive observations can be drawn from this figure. First,more sensing users are required to improve the averagethroughput when the number of the licensed channelsare high, which is intuitive since more licensed channelsmeans the spectrum opportunities are expected to be high,so more users are required to identify them. Second,when the number of the sensing users is relatively low,each one of them is required to sense more channelsto maximize the throughput; however, with increasingthe number of the sensing users, the optimal number ofthe sensed channels per user decreases until reaches onechannel when Ms ≥ N and the throughput saturates.Therefore, in the situations when the power is concern,the ASP policy can include a rule that allows just N usersto sense the spectrum and the others do nothing or eventurn to sleep mode to save their power.

Fig. 10. Optimal number of the sensed channels and the correspondingthroughput.

In the MAC framework, it is important to determine theoptimal spectrum sensing duration. In Fig. 11, the optimalsensing duration is plotted with respect to the number ofthe sensing users for different values of the probabilityof detection threshold. As expected, the optimal sensingduration decreases with increasing the number of thesensing users when the other parameters are fixed. Infact, decreasing the sensing duration is desirable; however,this decreasing almost saturates when Ms ≥ N asdiscussed before. Another observation can be drawn fromthis figure. The required sensing duration increases whenthe required probability of detection threshold increases,which is intuitive since more primary network protectionrequires more sensing time to achieve this protectionlevel.

A higher value of the maximum slot time, i.e., Tmax, isdesirable by the SUs, but the opposite is true for the PUs.In fact, the exact threshold of the time slot is governed bythe primary network requirements; however, we want tostudy its influence on the proposed MAC frame time. Fig.12 illustrates this influence. The optimal sensing durationis the same for all the values of Tmax except when thereis only one sensing user, which can be explained easilyby referring to (33). On the other hand, the throughput isaffected by the value of Tmax. When Tmax is small andthe number of the sensing users is also relatively smallcompared to the N channels, the throughput becomes low;however, the difference between the throughput curvesbecomes smaller until almost finishes with increasing thenumber of the sensing users. This is because larger partof the slot time is used to sense more channels when thenumber of the sensing users is small, while when eachuser senses smaller number of channels, the throughputimproves.



Fig. 11. Optimal sensing duration for different levels of requiredprobability of detection and the corresponding throughput.

Fig. 12. Optimal sensing duration for different levels of the maximumslot time and the corresponding throughput.

VI. CONCLUSION AND FUTURE WORK

In this paper, in order to compensate for the need ofcomplex hardware, we have proposed a novel cooperativeMAC framework for distributed OSNs considering therequirements of both the primary and secondary users.Moreover, we have investigated a simple computationalbut efficient spectrum sensing algorithm that relies onan innovative spectrum sensing policy. This algorithmassists each SU to identify online which channels, how

many channels, and for how long to sense. We havefound that using the proposed framework with this sensingalgorithm, the spectrum opportunities can be identifiedefficiently even with only a small number of the SUseach equipped with a CR transceiver. Consequently thesecondary throughput and the spectrum utilization can bemaximized while constraining the interference to the PUs.

The proposed MAC framework supports simultaneousmultiple access of SUs in order to decrease the trafficdelay of the secondary network. An optimal access algo-rithm that considers the limitations of the access mecha-nism and balances between the number of the sensing andaccess users is of our interest in the future research workin addition to handling the multi-hop secondary networkscenario.

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Ammar Alshamrani received the B.Sc. andM.Sc. degrees in Electrical Engineering fromKing Saud University, Riyadh, Saudi Arabia,in 1997 and 2003 respectively. Since 1997,he has worked with the Ministry of Defenseand Aviation (MODA) of Saudi Arabia asa telecommunication engineer, where he hasgained theoretical and practical experiencein different kinds of telecommunications andelectronic warfare systems.

Currently, he is a Ph.D. candidate in theDepartment of Electrical and Computer Engineering at University ofWaterloo, Ontario, Canada. His research interests include different areasin wireless communications and networking with special interest inCognitive Radio Networks.

Xuemin (Sherman) Shen received theB.Sc.(1982) degree from Dalian Maritime Uni-versity (China) and the M.Sc. (1987) and Ph.D.degrees (1990) from Rutgers University, NewJersey (USA), all in electrical engineering.

He is a Professor and University ResearchChair, Department of Electrical and ComputerEngineering, University of Waterloo, Canada.Dr. Shens research focuses on mobility andresource management in interconnected wire-less/wired networks, UWB wireless communi-

cations networks, wireless network security, wireless body area networksand vehicular ad hoc and sensor networks. He is a co-author of threebooks, and has published more than 400 papers and book chapters inwireless communications and networks, control and filtering.

Dr. Shen served as the Tutorial Chair for IEEE ICC08, the TechnicalProgram Committee Chair for IEEE Globecom’07, the General Co-Chair for Chinacom07 and QShine06, the Founding Chair for IEEECommunications Society Technical Committee on P2P Communica-tions and Networking. He also serves as a Founding Area Editorfor IEEE Transactions on Wireless Communications; Editor-in-Chieffor Peer-to-Peer Networking and Application; Associate Editor forIEEE Transactions on Vehicular Technology; KICS/IEEE Journal ofCommunications and Networks, Computer Networks; ACM/WirelessNetworks; and Wireless Communications and Mobile Computing (Wi-ley), etc. He has also served as Guest Editor for IEEE JSAC, IEEEWireless Communications, IEEE Communications Magazine, and ACMMobile Networks and Applications, etc. Dr. Shen received the ExcellentGraduate Supervision Award in 2006, and the Outstanding PerformanceAward in 2004 and 2008 from the University of Waterloo, the PremiersResearch Excellence Award (PREA) in 2003 from the Province ofOntario, Canada, and the Distinguished Performance Award in 2002and 2007 from the Faculty of Engineering, University of Waterloo. Dr.Shen is a registered Professional Engineer of Ontario, Canada, an IEEEFellow, and a Distinguished Lecturer of IEEE Communications Society.

Liang-Liang Xie received the B.S. degreein mathematics from Shandong Universityand the Ph.D. degree in control theory fromthe Chinese Academy of Sciences in 1995and 1999, respectively. He did postdoctoralresearch at the Automatic Control Group,Linkoping University, Sweden during 1999-2000 and at the Coordinated Science Lab-oratory, University of Illinois at Urbana-Champaign, USA during 2000-2002.

He is an Associate Professor at the Depart-ment of Electrical and Computer Engineering, University of Waterloo,Canada. His research interests include wireless networks, informationtheory, adaptive control, and system identification.



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A Cooperative MAC with Efficient Spectrum Sensing Algorithm for Distributed Opportunistic