Optimizing Spectrum Sensing Parameters for Localand Cooperative Cognitive Radios

Ayman A. EI-SalehWireless Networks and Communications Group,Universiti Kebangsaan Malaysia, 43600 Bangi,

Malaysiaand Centre for Broadband Communications (CBC),Multimedia University, 63100 Cyberjaya, Malaysia

+603-8312-5164aVlnan.elsaleh@nll11u.edu Iny

Abstract - In spectrum-sensing cognitive radio, a local radiomight fail to reliably and efficiently monitor the availablespectrum holes. To combat the channel fading suffered by sucha radio, a cognitive network of cooperative spectrum sensingusers is employed. The spectrum sensing time duration shouldbe minimized to maximize the capacity of cognitive radionetwork. In this paper, the twin effects of varying the entireframe duration and sensing time duration is studied. Thesimulation results show that the cognitive radio networkcapacity can be maximized by an appropriate selection of totalframe duration and an optimized corresponding sensing time.

Keywords - Capacity, cognitive radio, optimization,spectrum sensing.

1. Introduction

Although the frequency allocation charts are very crowdedin most countries around the world, wide ranges of alreadylicensed spectrum are underutilized according to actualmeasurements [1]. To satisfy the dramatic increasing demandfor radio spectrum, cognitive radio [2] is proposed as a keytechnology to realize the dynamic spectrum allocation. Thecognitive radio was first introduced by J. Mitola as anupgraded version of the normal software defined radio (SDR)armed with spectrum sensing capability over three degrees offreedom: time, frequency and space [2],[3]. The spectrumsensing is normally considered as a pure detection problemwhere the CR-assisted users have to scan a vast range offrequencies to observe available 'white spaces' or 'holes' thatare temporarily and spatially available for transmission. TheCR-assisted users are classified as secondary users (SUs)competing with primary users (PUs) who are obviously,Licensees, or alternatively, users of existing technologies onunlicensed bands (e.g. IEEE802.11a) [4]. The SUs areallowed to utilize the frequency bands of the PUs when theyare not currently being used but they should willingly andquickly vacate the band once a PU has been detected. Thisfast vacation is necessary to avoid causing harmfulinterference to the PUs who should maintain ubiquitous anduninterrupted accessibility. Therefore, the SUs are required toperiodically monitor the PUs activities using fast and reliabledetection/sensing algorithms. In such algorithms, the PU(s)

Mahamod Ismail and Mohd Alauddin Mohd AliDepartment ofElectrical, Electronics, and System

Engineering,Faculty ofEngineering and Built Envirnoment,Universiti Kebangsaan Malaysia, 43600 Bangi,

Malaysia+603-8921-7004

mahanl0d mama@eng.ulan. nlV

detection probability and false alarm probability measured bythe SU will be of interest.

In this paper, a cognitive radio network based on WRANsystem deployment has been employed. Each frame inWRAN consists of one sensing time slot plus one datatransmission slot. The effect of varying the total frameduration and the corresponding sensing time on the SU(s)capacity for local and cooperative spectrum sensing wasstudied in detail. In local sensing, a SU makes an individualdecision on the presence of PUs, whereas in cooperativesensing, several SUs collaborate together to come out with afinal decision on the presence of PUs by combining allindividual decisions of local SUs at a central base station(BS) using OR or AND fusion schemes [5],[6]. Thecollaborative sensing aims to improve the detectionsensitivity at low SNR environments as well as to tackle thehidden terminal problem where the PUs activities might beshadowed from the local SU receiver by any existingintermediate objects such as in fading environments [7]. Inthis work, the capacity of SU(s) of local and cooperativesensing is analyzed under two operational modes, namely, theconstant primary user protection (CPUP) and constantsecondary user spectrum usability (CSUSU) scenarios. InCPUP scenario, the interference from SUs to PUs will be setto a specific level that is low enough to ensure ubiquitous anduninterrupted service for the active PUs. This is done byfixing the probability of detecting PUs to a high value, e.g. Pd

= 0.9, while minimizing the probability of false alarm. Onthe other hand, in CSUSU scenario, the usability ofunoccupied bands by SUs can be kept constant by setting theprobability of false alarm at a certain level, e.g. PI = 0.1,while maximizing the probability of detection. In this paper,the capacity of the SU network is analyzed under these twooperational modes. This paper is structured as follows:Section 2 reviews the channel sensing hypotheses, energydetector, and the CPUP and CSUSU transmission modes forlocal spectrum sensing. In section 3, the cooperativespectrum sensing is presented using OR and AND fusionschemes. The performance of local and cooperative spectrumsensing is characterized and effect of varying the frameduration and number of cooperative users in section 4, andfinally, the conclusions are drawn in section 5.

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2. Local Spectrum Sensing

In local sensing, each SU senses the spectrum within itsgeographical location and makes a decision on the presenceof primary user(s) based on its own local sensingmeasurements.

A. Channel Sensing Hypotheses

Consider a SU in a cognitive radio system sensing afrequency band Wand a the received demodulated signal issampled at sampling rate,ls, then Is ~ W. Hence, the sampledreceived signal, X[ n] at the SU receiver will have twohypotheses as follows:

distribution can be approximated as Gaussian based on the

central limit theorem. Then

(4)

(5)

C. Cognitive Radio Transmission Scenarios

1) Constant Primary User Protection (CPUP) Scenario:

where n = 1, ..., K; K is the number of samples. The noiseW[n] is assumed to be additive white Gaussian (AWGN) withzero mean and variance a ~. S[n] is the primary user's signal

and is assumed to be a random Gaussian process with zeromean and variancea ~. The goal of the local spectrum sensing

is to reliably decide on the two hypotheses with highprobability of detection, Pd, and low probability of falsealarm, Pf P d and Pf can now be defined as the probabilitiesthat the sensing SU algorithm detects a PU under 1fo and 14,respectively.

1fo: X[n] =W[n]14: X[n] =W[n] + S[n]

if PU is absentif PU is present (1)

This transmission mode is viewed from the PUs'perspective. It guarantees a minimum level of interference to

PUs who by right, should not be affected by the SUstransmission. This scenario can be realized by fixing Pd at asatisfactory level, e.g. 90%, and trying to minimize Pf asmuch as possible. Thus, PI is derived to be

where the number of samples, K, is the product of sensingtime times sampling frequency.

B. Statistical Model ofEnergy Detector

The energy detector is known as a suboptimal detectorwhich can be applied to detect unknown signals as it does notrequire a prior knowledge on the transmitted waveform as theoptimal detector (matched filter) does. The decision statistic,T, for energy detector is given by

2) Constant Secondary User Spectrum Utilization(CSUSU) Scenario:

This mode is taken from the SUs' perspective; it aims tostandardize the spectrum utilization by SUs. As such, the Pfvalues should be fixed at lower values (e.g. ~ 10%) whilekeep maximizing Pd which can be written in terms of adesired Pfas follows

where P is a particular threshold that tests the decisionstatistic. Since we are interested in low signal-to-noise ratioof primary user (SNR =a 2/a 2) regime, large number of

p x w

samples should be used. Thus, the test statistic chi- square

It is well known that under the common Neyman-Pearson

detection performance criteria, the likelihood ratio yields the

optimal decision. Hence, the energy detector performance can

be characterized by a resulting pair of (PI' P d) that is

estimated asThe collaborative sensing aims to improve the detection

sensitivity at low SNR environments as well as to tackle the

hidden terminal problem where the PUs activities might beshadowed from the local SU receiver by any existingintermediate obstacles. This section presents the SU cognitiveradio network model using some well-known fusion schemes.In addition, the overall network PU detection and false alarmprobabilities will be derived for the CPUP and CSUSUtransmission scenarios, respectively.

K

T = L (X[n])2n=l

Pf = P(T> P 1110)Pd = P(T> P11lj)

(2)

(3)

Q-l{Pf )-lSNRp

(l+SNRp )

3. Cooperative Spectrum Sensing

(7)

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C. Derivations ofNetwork Probabilities under CPUP andCSUSU Scenarios

In this section, the SUs network false alarm and detection

probability formulas will be stated under CPUP and CSUSUscenarios, respectively. Let's here, for instance, assume thatwe are interested to derive the overall false alarm probabilityof the network, PI, under the CPUP transmission mode usingOR fusion scheme. Firstly, we find the individual desired

detection probability, Pd j, in terms of the desired network

detection probability, P d , using (8). Secondly, the

probability of false alarm of each SU, Pf,i, can be found by

substituting the P d,i equation into (6). Finally, PI is

estimated by substituting the Pf,i equation into (9). Thus, P.l

for CPUP-OR combination is

A. Cognitive Radio Network Deployment

The network deployment in this paper is based on theIEEE 802.22 WRAN [8]. The WRAN base BS collectsinformation on the PU activities from the SUs within itscoverage area as shown in Figure 1. Local SUs keepmonitoring the presence of a PU, which is a TV broadcaststation, and send their detection and false alarm probabilitiesto the base station for combining them into one overall final

decision. In this scenario, it is assumed that the TV BS is faraway from the WRAN BS and therefore, low SNRp values areused.

yeQ- ....- -- - - - - - - - -...UC;O~·I"~~ --14ft~~~ • J .... ,

:---.. -~--------- " " J " ~ • J ... "--- \ ~ .----~-_~~ __~B~ ... ,," I

TV BS =PU WRAN users = SUs

N

Pf = TIPf ,;;=1

(11 )

Figure 1. A Simplified Representation of an IEEE 802.22 WRAN SystemDeployment.

B. Fusion Schemes for Local Spectrum Sensor' Decisions

At the SUs base station, all local sensing information arecombined and merged into one final decision using Chair­Varshney fusion schemes [5],[ 6]. Two fusion schemes areused in this paper, OR- and AND-rule. In OR-rule fusionscheme, the final decision on the presence of a PU will bepositive if only one SU of all collaborating users detects thisPU. Assuming that all decisions are independent, thedetection and false alarm probability of the SUs network

under OR-rule, Pd and P.l, respectively, can then bemathematically written as

Please refer to [9] for more explanations on the overallnetwork probabilities of the other three combinations,CPUP_AND, CSUSU-OR, and CSUSU-AND. Thus, weintroduce PI for CPUP-AND combination can be derived as

where P d,i and Pf,i are the individual detection probability andfalse alarm probability, respectively. N is the number ofcooperating SUs. In AND-rule fusion scheme, allcollaborating SUs should declare the presence of a PU inorder for the final decision to be positive. Again, assumingthat all decisions are independent, the SUs networkprobabilities under AND-rule can be presented as

In CSUSU scenario, the false alarm probability of the SUsnetwork is set constant at Pf' and the detection probability of

the SUs network, Plh is derived. For CSUSU-OR

Similarly, for CSUSU-AND

(14)Q-l(l-~-Pf)~)-~SNRpj

(I+SN~,J

N

~==I-TI 1-i~

(9)

(8)

(10)

N

Pd =Ilpd,ii=l

N

Pd = 1- IT (1 - Pd,i )i=1

N

Pf = 1-n(l- PfJi=1

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4. Optimizing Sensing Parameters for Local andCooperative Spectrum Sensing

In this section, we analyze the relationship between SUscapacity and sensing capability for both local and cooperativesensing under the CPUP and CSUSU transmission modes. InWRAN system, each frame consists of one sensing slot (ts)plus one data transmission slot (1j - ts), where 1j is the totalframe duration. Indeed, short sensing slots should be alwaystargeted as it results in longer data transmission slot andtherefore, higher throughput capacity.

A. Problem Formulation

There are two cases for which the SUs network might

operate at the PU's licensed band; the first one is when the

PU is inactive and the SUs successfully declare that there is

no PU. In this case, the normalized capacity of the WRAN

system is represented as

Co =[1- ~ }-Pf)P(JlO) (16)

where P(Jfo) is the probability that the PU is inactive in the

frequency band being sensed. The other case is when the PU

is active but the SUs fail to detect it. The normalized capacity

is then given by

C1 =[1- iJ(1-Pd )P(Jij) (17)

where P(:Hi) is the probability of the PU being active in the

frequency band of interest. Obviously, P(Jfo) + P(%.) = 1.

The objective of this research is to determine the optimal

sensing time for each frame such that the SUs network

capacity is maximized. Consequently, this objective can beformed as an optimization problem described as follows:

selected to be 3MHz. The SN~ is set to -18 dB. Let's first

define the optimal sensing time by the sensing time at whichthe SU capacity is maximized. For local spectrum sensingunder CPUP transmission scenario, (6) states that Pfdecreases with increasing the sensing time and consequently,Pf suppose to increase the SU capacity. However, Figure 2shows that decreasing Pfdoes not lead to an absolute increasein the SU capacity as thought but instead, there is an optimalsensing time at which the capacity is maximized. Table 1summarizes the maximum capacities obtained in Figure 2 andtheir corresponding optimum sensing times at differentdetection probabilities and total frame durations. It is seenthat for a given total frame time duration, let say 100 ms,increasing the detection probability from 0.7 to 0.9 leads to

degrade the capacity in general and the maximum normalizedcapacity decreases from 0.8288 to 0.7760. The optimumsensing time is then increased from 2.85 ms to 4.5 ms. On theother hand, if the detection probability has been set at acertain value, let say 0.9 for high PU protection, then,increasing the total frame time duration from 100 ms to 500ms leads to increase the optimum capacity from 0.7760 to0.8088 and also increase the optimum sensing time from 4.5ms to a range of 5.8 to 6.1 ms. Obviously, increasing the totalframe duration leads to a very interesting finding that is theSU capacity becomes more uniform.

0.9.----r-----r------.......---~

0.85 -----;;.;>--,.,+-.---..-.,.-.,~:.+.:;.:;.;;.:;.;.-.:.-~.-.:,-,f.-.:.-.:.-.:.::.::.::.::.:.- ..

Jo~;: ::::~~:vi~~~:t:~:~:::::L;~:::_ -~~T~:~~~~~:~~~~-"'0 i I • ........ I

i 0:: ::::r:::::::::t:::::::::::::::::t:::::::::::::~J~:~~~:~:~:~->o , i : :Z 0 6 .--i-------------~-----.--- - Pd = 0.7 &Tf = 100 ms'; i _._._.. Pd = 0.9 & Tf= 100 ms

0.55 --+----------.--~--.------ Pd = 0.7 &Tf = 500 msi ----- Pd =0.9 &Tf =500 ms

Figure. 2: The Effect of Varying the Detection Probability and TotalFrame Duration on the Normalized Capacity versus Sensing TimeRelation under CPUP Transmission Mode.

Subject to: 0 < Is S TI and

5 10 15Sensing time (ms)

20

Pd ~ Pd under CPUP or PI S Plunder CSUSU

(18)

B. Optimizing Sensing Parameters for Local SpectrumSensing

In this section, MATLAB simulations have beenperformed to investigate the capacity-sensing capability

optimization problem. The WRAN frame duration was set to100 ms and the one- side bandwidth of PU bandpass signal is

Under CSUSU scenario, (7) reveals that Pd increases withincreasing the sensing time, this means that the PU will bemore protected, but unfortunately, the SU capacity will thendecrease as shown in Figure 3. It is shows that, at certainframe duration, let say 100 ms, decreasing the false alarmprobability from 0.3 to 0.1 leads to increase the SU capacityin general. This matches the fact that there will be more

chances for SUs to use the spectrum if the probability todetect a PU while it is absent is low. On the other hand, if thefalse alarm probability has been set at a specific value, say0.1 for high spectrum usability by SUs, then, increasing the

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total frame duration increases the capacity and makes it moreuniform. This finding matches the one above for the CPUPtransmission scenario.

Table 1. Performance Evaluation for Local Spectrum Sensing underCPUP Transmission Mode

Detection Frame Optimum MaximumProbability, Pd Duration, Tf Sensing Time, t s Capacity, C

(ms) (ms) (bits/sec/Hz)

0.7 100 2.85 0.82880.9 100 4.5 0.77600.7 500 3.9 - 4.3 0.85180.9 500 5.8 - 6.1 0.8088

sensing time, respectively. Figure 4 shows that the maximumSUs network capacity increases by cooperating more users inthe network using OR and AND fusion schemes. In addition,it is also obvious that the maximum normalized improves bylengthening the total frame duration from 100 ms to 500 ms.Interestingly, at long frame duration, the network capacity ismaintained at a constant level and it is going to besignificantly changed by incorporating more users. Figure 5reveals that cooperating more users will reduce the optimalsensing time required to achieve the maximum throughput. Itis also seen that lengthening the frame duration will logicallyincrease the optimal sensing time.

2010 15Number of users. N

5

0.7751-----..l5--===1:cO======::I15======::::J20

Number of users. N

7 r---;----;::::=:::!:=========:::::!::=========:;-,i - OR-rule & Tf= 100 ms

.-. 6 --------------t----------- -.-.-.• AND-rule &Tf = 100 msE \ : OR-rule & Tf= 500 ms- \ : ----- AND-rule &Tf =500 ms

Figure 4: Maximum Normalized Capacity versus Number of Usersunder CPUP Mode Using Logical OR and OR Fusion Schemes.

, I Ii::~~ ~~~-:~·~J·~:::·-··:~-;:I:~:~~~~~~t::'::::;~::E .... : :<5 0.795 -------/.~-- ;------------------~----.------------+------.---------

~ 0.79 ---,l-------i---------.. -- .....L.---... -- ...---.L..--- .... -- ...E f : -- OR-rule &Tf= 100 ms.~ 0.785 - -----------\---------- -.-.-.. AND-rule &Tf =100 ms:E 0.78 ! OR-rule & Tf= 500 ms

: ----- AND-rule &Tf = 500 ms

Figure 5: Optimal Sensing Time versus Number of Users under CPUPMode Using Logical OR And OR Fusion Schemes.

Figures 6 and 7 present the CSUSU transmission modeusing either OR or AND fusion, respectively. For both cases,It was found that at short sensing times, e.g. ts is less than 5ms of total frame duration, cooperating more users willsignificantly reduce the network capacity whereas at longersensing times, there was no effect on the network capacity byincorporating more users in the cognitive network. On the

0.3 L::::::======::::I:========::r=~ __i---.~_--J

o 5 10 15 20Sensing time (ms)

0.8 .------...----~---...,....---------,

.•.........~ 0.7 -------- --:.....~ ~:'.:.::.::.::.::.".::.".".L,·,·"· ..---.--·r··"·"·,,·,,.o.-.o'-.0..

~ 0.6 --------.--------~----------------+----------------!----u ,~ , , ,

I0.5 ----"~~~:.~~·~~f:~·~=:~~::~~;::I=~:~:~~;J~~.~-~~~-~~~-~-~ -Pf=0.1&Tf=100ms j .-...... - ...

0.4 -.-.-.. Pf = 0.3 & Tf = 100 ms --_·---·_·f---·--···---··--.......... Pf= 0.1 & Tf= 500 ms ~

----- Pf = 0.3 & Tf = 500 ms i

C. Optimizing Sensing Parameters for Cooperative SpectrumSensing

Figure 3: The Effect of Varying the False Alarm Probability and TotalFrame Duration on The Normalized Capacity Versus Sensing TimeRelation under CSUSU Transmission Mode.

The cooperative users are actually local sensors cooperatetogether within the coverage area of a BS for reliable PUdetection. They are to perform individual repetitive sensingcycles and send their individual decisions to the WRAN BSas individual detection and false alarm probabilities. Thesensing time period, which is a fraction of total frame timetransmitted by the SU network, should be always as minimalas possible to maximize the SU network capacity. In order toestimate the capacity of WRAN network under, let say,CPUP scenario, we should first determine the overall PI ofthe network using (12) or (13) for OR or AND fusionschemes, respectively. Then, the estimated P.f together withthe desired fixed Pd are substituted in (18) to calculate theoverall capacity of the network. Similar procedure applies forCSUSU scenario. In this section, the number of cooperatingSUs, N, is varied from 1 user (no cooperation) to 20 users (allavailable users in the network are cooperating). The optimalsensing time here is again defined as the sensing timeduration at which the SUs network capacity is maximized.Let us first consider the CPUP transmission mode. Figures 4and 5 presents the effect of varying the number ofcooperative users as well as the total frame duration on themaximum capacity obtained and its corresponding optimal

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other hand, as the total frame duration increases, the networkcapacity is improved and again, the network capacityvariation becomes lesser with increasing the sensing time.

------1f =500 ms--Tf= 100 ms

0.64 '-----'-2---4""----.....l6'-----......8------'10

Sensing time of the ith user (ms). where i =1: N

Figure 6: Normalized Capacity versus Sensing Time for N Users underCSUSU Mode Using Logical AND Fusion Scheme (The curves at N = 3to 19 are not indexed to improve the visibility).

------ff =500 ms--Tf= 100 ms

0.64 L....---L..2---....I.-4---S...l......----S..L...-------I10

Sensing time of the ith user (ms), where i =1: N

Figure 7: Normalized Capacity versus Sensing Time for N Users underCSUSU Mode Using Logical OR Fusion Scheme (The curves at N = 3 to19 are not indexed to improve the visibility).

5. Conclusion

This research work presents a performance evaluation forlocal and cooperative spectrum sensing in cognitive radionetworks under CPUP and CSUSU transmission modes byvarying the sensing time, total frame duration, and number ofcooperative users. In CPUP mode, it was found that there isan optimal sensing time at which the capacity of local or coo-

perative users is maximized. Increasing the number ofcooperative users lead to increase the maximum capacity ofcooperative SUs network and decrease the correspondingoptimal sensing time. In CSUSU mode, there was no such anoptimal sensing time as in CPUP and the capacity of local orcooperative users keeps decreasing as the sensing timeincreases. In addition, it was observed that if the sensing timeis Iess than 5 ms of the total frame duration, incorporatingmore users in the network leads to decrease its capacitywhereas at longer sensing times, incorporating more users hasno effect on the network capacity. For both CPUP andCSUSU modes, employing long frame duration will alwaysimprove and stabilize the network capacity and the demandfor incorporating more users becomes less critical. This isevidently a tradeoff case between the total frame duration andnumber of cooperative users in the network.

Acknowledgment

This research work done in this paper was sponsored byUKM-OUP-NBT-29-153/2008 research fund The authorswould like to thank and appreciate Professor KasmiranJumari for his kindness, valuable assistance, and financialsupport.

References

[1] Federal Communications Commission, "Spectrum policy taskforce report, FCC 02-155." Nov. 2002.

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[3] 1. Mitola, "Cognitive radio: an integrated agent architecture forsoftware defined radio," PhD thesis, KTH Royal Institute ofTechnology, Stockholm, Sweden, 2000.

[4] A. A. EI-Saleh, M. Ismail, o. B. A. Ghafoor, and A. H.Ibrahim, "Comparison between Overlay Cognitive Radio andUnderlay Cognitive Ultra Wideband Radio for WirelessCommunications," Proc. of the Fifth lASTED (AsiaCSN2008), pp. 41-45, April 2-4, 2008, Langkawi, Malaysia.

[5] Z. Chair and P. K. Varshney, "Optimal data fusion in multiplesensor detection systems," IEEE Trans. on Aerospace andElect. Syst., vol.22 pp.98-101, January 1986.

[6] P. K. Varshney, "Distributed Detection and Data Fusion".Springer, 1997.

[7] A. Ghasemi and E.S. Sousa, Collaborative spectrum sensing foropportunistic access in fading environments, Proc. ofDySPAN'05, November 2005.

[8] IEEE 802.11 wireless RAN, "Functional requirements for theWRAN standard, IEEE 802.11 05/0007r46" Oct. 2005.

[9] A. A. EI-Saleh, M. Ismail, M. A. M. Ali, and A. N. H.Alnuaimy, "Capacity Optimization for Local and CooperativeSpectrum Sensing in Cognitive Radio Networks", Proc. ofICCT, February 2009.

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