Discontinuous spectrum sensing scheme for energy-constrained cognitive radio networks
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Discontinuous spectrum sensing scheme for energy-constrained cognitive radio networks Bo Wang, Zhiyong Feng, Dongyan Huang and Ping Zhang Compared with traditional communication systems, cognitive radio (CR) systems consume additional sensing and reporting energy. Minimising additional energy consumption while maintaining maximum throughput is an emerging issue in CR systems. Proposed is a discontinuous spectrum sensing scheme for CR systems that adopts cooperative spectrum sensing. The simulation results show that the proposed scheme can achieve remarkable energy savings with negligible throughput loss. Introduction: Cognitive radio (CR) is a paradigm that facilitates the effi- cient use of the radio spectrum through spectrum sharing between primary user (PU) and secondary user (SU) transmissions [1]. To prevent interference to the PU, spectrum sensing has to be performed by the SUs. To improve sensing accuracy, cooperative spectrum sensing is proposed. However, the participation of more SUs in coopera- tive sensing will result in more consumption of additional sensing and reporting energy. Considering that SU devices are typically battery- powered, minimising additional consumption while maintaining maximum throughput is an emerging issue in CR networks. Reference [2] reduced sensing and reporting energy consumption by dividing the SUs into several subsets and activating one subset at a period of time. Different from [2], this Letter proposes a discontinuous spectrum sensing (DisSS) scheme to minimise sensing and reporting energy consumption while maintaining maximum throughput by using the PU call duration characteristic. We prove the existence of optimal non-sensing duration τ ns * and find the closed-form solution to the optimisation problem. System model: In cooperative spectrum sensing, all SUs are synchro- nised to enable them to sense the channel and report their respective local sensing results to a cognitive base station (CBS) in unified sensing slots through a public channel. After collecting the local sensing results, the CBS obtains the occupancy decision for the channel according to the fusion criterion and then determines the follow-up action of the group either to enable all SUs to remain silent or to enable one SU to access the channel. We assume the CBS adopts the OR rule for fusion; the probability of false alarm and misdetection Q fa and Q md of cooperative spectrum sensing can be solved by [3]. The channel usage model is considered as an alternating renewal process with busy and idle states. The duration of busy and idle states follows the exponential distribution with averages of α 1 and α 0 , respect- ively [4]. The probability density functions of the durations of busy and idle states can be expressed as f B (t )= α 1 - 1 exp( - t/α 1 ) and f I (t )= α 0 - 1 exp ( - t/α 0 ), respectively. The probability of the busy and idle states of the channel can be expressed as P B = α 1 /(α 0 + α 1 ) and P I = α 0 /(α 0 + α 1 ), respectively. Proposed DisSS scheme: A fact as follows: when a channel is occupied by a PU, the probability that the PU’s call would end is extremely low in the early busy state. However, this probability increases over time. Thus, sensing early in the busy state is almost insignificant, because the CR system always gets the same sensing result, that is, the channel is occupied. This Letter proposes a DisSS scheme for cooperative spectrum sensing. The detailed steps of the proposed scheme are as follows: Step1: Each SU senses the channel and reports local sensing result to the CBS. The CBS executes fusion algorithm. Step2: When fusion result shows the channel state switching from idle to busy, the CBS broadcasts the non-sensing instruction to all SUs. Step3: SUs stop sensing and reporting after receiving the non-sensing instruction. Step4: After a certain duration t ns , the CBS broadcasts the sensing instruction to all SUs. All SUs resume periodic spectrum sensing and report the local sensing result to the CBS. By utilising the proposed scheme, the CR system will save sensing and reporting energy while losing a certain amount of throughput. Both energy saving and throughput loss increase with τ ns . Thus, the proposed scheme primarily aims to determine the optimal non-sensing duration τ* ns to achieve a good balance between energy saving and throughput loss. Problem formulation: Considering the increasing popularity of the CR system, we consider a heavy traffic scenario in which the CR system always receives access requests from SUs. In this scenario, the utility function U(τ ns ) comprises two parts: the average revenue in sensing and reporting energy saving R(τ ns ) and the average cost related to the throughput loss C(τ ns ) for the non-sensing duration τ ns . The proposed scheme was executed in two cases as follows: Case I : The channel is busy and the cooperative sensing result is correct. The probability of the case is P B (1 - Q md ). This case has two subcases, as shown in Fig. 1. subcase 1: t ns < t busy t ns t ns subcase 2: t ns > t busy the channel states SUs sensing stages periodic sensing duration all SUs resume sensing and reporting result all SUs stop sensing and reporting result PU leaving PU arriving busy state idle state non-sensing duration t t t busy t busy Fig. 1 Case I of DisSS scheme Subcase 1: τ ns < t busy , the probability of Subcase 1 is P 1 = 1 − tns 0 f B (t)dt = e − tns a1 . The average revenue of Subcase 1 is R 1 (t ns )= N (t ns /T )(E S + E R ) (1) where N is the number of SUs; E S and E R refer to the sensing and report- ing energy consumption for one frame, respectively. In Subcase 1, the CR system does not exhibit any throughput loss, such that the average cost is C 1 (τ ns ) = 0. The utility function of Subcase 1 can be expressed as U 1 (t ns )= R 1 (t ns )− kC 1 (t ns )= R 1 (t ns ) (2) where k is an adjustable ratio factor that reflects the comparison of the degrees of importance of energy saving and throughput loss. Subcase 2: τ ns > t busy , the probability of Subcase 2 is P 2 = tns 0 f B (t)dt = 1 − e − tns a1 . The average revenue of Subcase 2 is R 2 (τ ns )= R 1 (τ ns ). The average cost of Subcase 2 is C 2 (t ns )= E[c(t ns − t busy )] = c(t ns − a 1 ) (3) where E(·) is expectation operation and c is the data rate of the SU. The utility function of Subcase 2 can be expressed as U 2 (t ns )= R 2 (t ns )− kC 2 (t ns ) (4) So the utility function of Case I can be expressed as U I (t ns )= P 1 U 1 (t ns )+ P 2 U 2 (t ns ) (5) Case II: The channel is idle but the cooperative sensing result is incor- rect. The probability of the case is P I Q fa . We consider the worst case scenario wherein the PU does not arrive during non-sensing duration. In this scenario, the throughput during non-sensing duration is comple- tely wasted. Thus, the utility function of Case II is U II (t ns )= R II (t ns )− kC II (t ns ) = N (t ns /T )(E S + E R )− kct ns (6) According to the analysis of the two cases, the utility function of the ELECTRONICS LETTERS 14th March 2013 Vol. 49 No. 6
Transcript of Discontinuous spectrum sensing scheme for energy-constrained cognitive radio networks
Discontinuous spectrum sensing scheme forenergy-constrained cognitive radionetworks
Bo Wang, Zhiyong Feng, Dongyan Huang and Ping Zhang
Compared with traditional communication systems, cognitive radio(CR) systems consume additional sensing and reporting energy.Minimising additional energy consumption while maintainingmaximum throughput is an emerging issue in CR systems. Proposedis a discontinuous spectrum sensing scheme for CR systems thatadopts cooperative spectrum sensing. The simulation results showthat the proposed scheme can achieve remarkable energy savingswith negligible throughput loss.
Introduction: Cognitive radio (CR) is a paradigm that facilitates the effi-cient use of the radio spectrum through spectrum sharing betweenprimary user (PU) and secondary user (SU) transmissions [1]. Toprevent interference to the PU, spectrum sensing has to be performedby the SUs. To improve sensing accuracy, cooperative spectrumsensing is proposed. However, the participation of more SUs in coopera-tive sensing will result in more consumption of additional sensing andreporting energy. Considering that SU devices are typically battery-powered, minimising additional consumption while maintainingmaximum throughput is an emerging issue in CR networks.
Reference [2] reduced sensing and reporting energy consumption bydividing the SUs into several subsets and activating one subset at aperiod of time. Different from [2], this Letter proposes a discontinuousspectrum sensing (DisSS) scheme to minimise sensing and reportingenergy consumption while maintaining maximum throughput by usingthe PU call duration characteristic. We prove the existence of optimalnon-sensing duration τns* and find the closed-form solution to theoptimisation problem.
System model: In cooperative spectrum sensing, all SUs are synchro-nised to enable them to sense the channel and report their respectivelocal sensing results to a cognitive base station (CBS) in unifiedsensing slots through a public channel. After collecting the localsensing results, the CBS obtains the occupancy decision for thechannel according to the fusion criterion and then determines thefollow-up action of the group either to enable all SUs to remain silentor to enable one SU to access the channel. We assume the CBSadopts the OR rule for fusion; the probability of false alarm andmisdetection Qfa and Qmd of cooperative spectrum sensing can besolved by [3].
The channel usage model is considered as an alternating renewalprocess with busy and idle states. The duration of busy and idle statesfollows the exponential distribution with averages of α1 and α0, respect-ively [4]. The probability density functions of the durations of busy andidle states can be expressed as fB(t) = α1
− 1exp(− t/α1) and fI(t) = α0− 1exp
(− t/α0), respectively. The probability of the busy and idle states of thechannel can be expressed as PB = α1/(α0 + α1) and PI = α0/(α0 + α1),respectively.
Proposed DisSS scheme: A fact as follows: when a channel is occupiedby a PU, the probability that the PU’s call would end is extremely low inthe early busy state. However, this probability increases over time. Thus,sensing early in the busy state is almost insignificant, because the CRsystem always gets the same sensing result, that is, the channel isoccupied.
This Letter proposes a DisSS scheme for cooperative spectrumsensing. The detailed steps of the proposed scheme are as follows:
Step1: Each SU senses the channel and reports local sensing result to theCBS. The CBS executes fusion algorithm.Step2: When fusion result shows the channel state switching from idle tobusy, the CBS broadcasts the non-sensing instruction to all SUs.Step3: SUs stop sensing and reporting after receiving the non-sensinginstruction.Step4: After a certain duration tns, the CBS broadcasts the sensinginstruction to all SUs. All SUs resume periodic spectrum sensing andreport the local sensing result to the CBS.
By utilising the proposed scheme, the CR system will save sensing andreporting energy while losing a certain amount of throughput. Both
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energy saving and throughput loss increase with τns. Thus, the proposedscheme primarily aims to determine the optimal non-sensing durationτ*ns to achieve a good balance between energy saving and throughputloss.
Problem formulation: Considering the increasing popularity of the CRsystem, we consider a heavy traffic scenario in which the CR systemalways receives access requests from SUs. In this scenario, the utilityfunction U(τns) comprises two parts: the average revenue in sensingand reporting energy saving R(τns) and the average cost related to thethroughput loss C(τns) for the non-sensing duration τns.
The proposed scheme was executed in two cases as follows:
Case I : The channel is busy and the cooperative sensing result iscorrect. The probability of the case is PB(1−Qmd). This case has twosubcases, as shown in Fig. 1.
subcase 1: tns < tbusy
tns tns
subcase 2: tns > tbusy
the channel states
SUs sensing stages
periodic sensingduration
all SUs resume sensingand reporting result
all SUs stop sensingand reporting resultPU leavingPU arriving
busy state idle state non-sensing duration
t
t
tbusytbusy
Fig. 1 Case I of DisSS scheme
Subcase 1: τns < tbusy, the probability of Subcase 1 is
P1 = 1− �tns0 fB(t)dt = e
−tnsa1 .
The average revenue of Subcase 1 is
R1(tns) = N (tns/T )(ES + ER) (1)where N is the number of SUs; ES and ER refer to the sensing and report-ing energy consumption for one frame, respectively.
In Subcase 1, the CR system does not exhibit any throughput loss,such that the average cost is C1(τns) = 0.
The utility function of Subcase 1 can be expressed as
U1(tns) = R1(tns) − kC1(tns) = R1(tns) (2)where k is an adjustable ratio factor that reflects the comparison of thedegrees of importance of energy saving and throughput loss.
Subcase 2: τns > tbusy, the probability of Subcase 2 is
P2 =�tns0 fB(t)dt = 1− e
−tnsa1 .
The average revenue of Subcase 2 is R2(τns) = R1(τns).The average cost of Subcase 2 is
C2(tns) = E[c(tns − tbusy)] = c(tns − a1) (3)where E(·) is expectation operation and c is the data rate of the SU.
The utility function of Subcase 2 can be expressed as
U2(tns) = R2(tns) − kC2(tns) (4)So the utility function of Case I can be expressed as
UI (tns) = P1U1(tns) + P2U2(tns) (5)Case II: The channel is idle but the cooperative sensing result is incor-rect. The probability of the case is PIQfa. We consider the worst casescenario wherein the PU does not arrive during non-sensing duration.In this scenario, the throughput during non-sensing duration is comple-tely wasted. Thus, the utility function of Case II is
UII (tns) = RII (tns) − kCII (tns)= N (tns/T )(ES + ER) − kctns
(6)
According to the analysis of the two cases, the utility function of the
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proposed DisSS scheme can be expressed as follows:
U (tns) = PB(1− Qmd)UI (tns) + PIQ faUII (tns)= [PIQ fa + PB(1− Qmd)]N (tns/T )(ES + ER)− kc[PIQ fatns + PB(1− Qmd)(tns − a1)
(1− e−tnsa1 )]
(7)
The optimisation problem can be expressed mathematically as follows:
maxU (tns) (8)s.t. tns . 0 (9)
The constraint condition, i.e. (9), implies that non-sensing duration τnsshould be a positive number.
Problem solution: Differentiating U(τns) with respect to τns, we derivestationary point
t∗ns = a1[2−W (x)] (10)
where x = e2{1− [PIQ fa+PB(1−Qmd )]NT (ES+ER)−kcPIQ fa
kcPB(1−Qmd ) }, W(x) is Lambert W
function [5] that can be solved by Matlab or Maple. It provides the sol-ution to W(x)eW(x) = x.
Differentiating U ′(tns) with respect to τns, we obtain
U ′′(tns) = PB(1− Qmd)kca−11 e−tns/a1 (tns/a1 − 3) (11)
According to (10) and (11), τ*ns is the global maximum point of theoptimisation problem when t∗ns [ (0, 3a1). By adjusting the value ofk, we derive t∗ns [ (0, 3a1).
Simulation results: Long-term evolution (LTE) is one of the best candi-dates for CR systems. Thus, we set frame duration T as 10 ms, which isalready adopted in LTE. c is set as 1 Mbit/s because most of the traffic inCR systems will be date traffic. ES and ER are set as 0.1 and 1 mJ,respectively. N ranges from 2 to 8. Pfa and Pmd of each SU are set as0.01 and 0.4, such that Qfa and Qmd can be solved according to [3]. kis set as 0.005. α1 is set as 300 s considering that data traffic will bemore popular in PU systems. Given that a channel with low utilisationrate is suitable for CR systems, we assume that α0 = 700 s. Thenumber of busy and idle states n is 10000 over the observation duration.
h1 =(1−Qmd )n
t∗nsT (ES+ER)N+Qfan
t∗nsT (ES+ER)N
∑n
i=1
tibusyT (ES+ER)N
is the ratio of the energy saving
achieved by the proposed scheme to the total energy consumption of thetraditional scheme over n busy durations of the channel, where tBusy
i isthe channel’s ith busy duration.
h2 =(1−Qmd )
∑n1
i=1
c(t∗ns−tibusy)+Qfa(n−n1)t∗nsc
(1−Qfa)∑n
i=1
ctiidle
is the ratio of the throughput loss
of the proposed scheme to total throughput of the traditional schemeover n idle durations of the channel, where tidle
i is the channel’s ithidle duration and n1 is number of τ*ns > tbusy.
0 50 100 150 200 250 3000
0.2
0.4
0.6
0.8
1.0
τns, s
norm
alis
ed u
tility
func
tion
U(τ
ns)
N=2N=3N=4N=5N=6N=7N=8
Fig. 2 U(τns) against τns under different N
Normalised utility function U(τns) against non-sensing duration τnsunder different N is shown in Fig. 2. With N increasing from 2 to 8,the maximum value of U(τns) and the optimal no-sensing time τ*ns
ELECTRO
increases. This condition indicates that the CR system derives morebenefits from the proposed scheme when a greater number of SUsparticipate in cooperative sensing.
Table 1 shows that, with the increase in N from 2 to 8, the optimalnon-sensing duration τ*ns increases from 132 to 139 s. η1 increasesfrom 38.0 to 50.1%, whereas η2 only increases from 3.0 to 4.8%. TheCR system can achieve significant energy saving with negligiblethroughput loss when utilising the proposed scheme.
Table 1: Comparison of η1 with η2 under different N
N 2 3 4 5 6 7 8
τ*ns(s) 132 133 134 135 136 137 139
η1(%) 38.0 43.1 45.4 46.9 48.1 48.9 50.1
η2(%) 3.0 3.5 3.8 4.1 4.3 4.6 4.8
Conclusion: Given that SU devices are typically battery-powered,determining a process by which to save sensing and reporting energyis an emerging issue in cooperative spectrum sensing. This Letter pro-poses a DisSS scheme to achieve a good balance between energysaving and throughput loss. The simulation results show that the pro-posed scheme can achieve remarkable energy saving with negligiblethroughput loss. Furthermore, the proposed scheme can significantlyreduce the transmission throughput of the public channel while lessen-ing the fusion workload of the CBS.
© The Institution of Engineering and Technology 20135 January 2013doi: 10.1049/el.2012.4534One or more of the Figures in this Letter are available in colour online.
Bo Wang, Zhiyong Feng, Dongyan Huang and Ping Zhang (WirelessTechnology Innovation Institute, School of Information andCommunication Engineering, Beijing University of Posts andTelecommunications, No. 10, Xitucheng Road, Haidian District,Beijing 100876, People’s Republic of China)E-mail: [email protected]
References
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2 Deng, Ruilong, et al.: ‘Energy-efficient cooperative spectrum sensing byoptimal scheduling in sensor-aided cognitive radio networks’, IEEETrans. Veh. Tech., 2012, 61, pp. 716–725
3 Peh, E., and Liang, Y.: ‘Optimization for cooperative sensing in cognitiveradio networks’. Proc. IEEE WCNC2007, Kowloon, 2007, pp. 27–32
4 Sriram, K., and Whitt, W.: ‘Characterizing superposition arrival pro-cesses in packet multiplexers for voice and data’, IEEE J. Sel. AreasCommun., 1986, 4, pp. 833–846
5 Fritsch, F.N., Shafer, R.E., and Crowley, W.P.: ‘Solution of the transcen-dental equation wew = x’, Commun. ACM, 1973, 16, pp. 123–124
NICS LETTERS 14th March 2013 Vol. 49 No. 6
