energy-efficient cooperative spectrum sensing schemes for cognitive radio networks

Zhao et al. EURASIP Journal onWireless Communications and Networking 2013, 2013:120http://jwcn.eurasipjournals.com/content/2013/1/120

RESEARCH Open Access

Energy-efficient cooperative spectrum sensingschemes for cognitive radio networksNan Zhao1,5, Fei Richard Yu2*, Hongjian Sun3 and Arumugam Nallanathan4

Abstract

Rapidly rising energy costs and increasingly rigid environmental standards have led to an emerging trend ofaddressing “energy efficiency” aspect of wireless communication technologies. Cognitive radio can play an importantrole in improving energy efficiency in wireless networks, because from the green perspective, spectrum is a naturalresource which should not be wasted but be shared. In this article, we propose two energy-efficient and time-savingone-bit cooperative spectrum sensing schemes, which have two stages in the spectrum sensing process. If thesignal-to-noise ratio is high or no primary user exists, only one stage of coarse spectrum sensing is needed, by whichthe sensing time and energy are saved. Otherwise, the second stage of fine spectrum sensing will be performed toincrease the spectrum sensing accuracy. Furthermore, only one-bit decision is sent by each secondary user tominimize the overhead. The second proposed algorithm fully utilizes the local decisions of the coarse detection, andits energy consumption is further reduced with its sensing performance close to the first one. Plenty of simulation isperformed, and the results show that the sensing time and energy consumption are both reduced significantly in theproposed schemes.

Keywords: Cognitive radio; Cooperative spectrum sensing; Energy efficiency

1 IntroductionCognitive radio (CR) has attracted significant attention asa promising technology to overcome the spectrum short-age problem caused by the current inflexible spectrumallocation policy [1,2]. In CR networks, secondary (unli-censed) users (SUs) should sense the radio environment,and adaptively choose transmission parameters accordingto sensing outcomes to avoid the interference to primary(licensed) users (PUs) [3]. Hence, it is a fundamental issuein CR networks that SUs should be able to efficiently andeffectively detect the presence of PUs [4,5].The existing spectrum sensing techniques can broadly

be divided into four categories: matched filter detection[6], cyclostationary detection [7], entropy-based detec-tion [8-10], and energy detection [11,12]. In matchedfilter detection and cyclostationary detection, SUs shouldhave some knowledge about the primary signal features.Entropy-based detection can solve the noise uncertaintyof the spectrum sensing through information entropy. In

*Correspondence: [email protected] Engineering, Carleton University, Ottawa, ON K1S 5B6, CanadaFull list of author information is available at the end of the article

[8], an entropy-based spectrum sensing scheme is pro-posed by combining the entropy detection in the timedomain and the matched filter. However, the matchedfilter in the scheme needs some necessary knowledgeabout the primary signal features, which requires addi-tional overhead and even hardly holds in CR, and thus it isnot a blind detector. An entropy-based spectrum sensingscheme in the frequency domain based on the spectrumamplitude is proposed in [9] and proved to be robust tothe noise uncertainty; however, its performance can stillbe improved. In [10], a novel entropy-based spectrumsensing scheme in the frequency domain based on thespectrum power density is proposed, and it is proved thatit is also robust to the noise uncertainty with better prob-ability of detection and lower computational complexity.Energy detection has widely been applied, since it doesnot require a priori knowledge of the primary signals,and has much lower computational complexity than theother three detection schemes. Hence, we focus on energydetection for spectrum sensing throughout this article.Spectrum sensing is a tough task especially when signal-

to-noise ratio (SNR) is low. To improve the performanceof spectrum sensing, cooperative spectrum sensing (CSS),

© 2013 Zhao et al.; licensee Springer. This is an Open Access article distributed under the terms of the Creative CommonsAttribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproductionin any medium, provided the original work is properly cited.

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where individual SUs sense the spectrum and send theinformation to a fusion center to obtain the final deci-sion, has been studied extensively [10,13-15]. In conven-tional hard combination CSS [13], only one-bit decisionis sent to the fusion center by each SU, and its over-head is minimum; however, its performance can still beimproved. Soft combination CSS scheme [14] has theoptimal performance through using the accurate sensingresults from different SUs; however, its overhead is large,which makes it difficult to be implemented in practicalnetworks.Two-bit overhead combination CSS scheme is proposed

in [10,15], which is a trade-off between hard combina-tion and soft combination CSS. In [16], a three-thresholddecision-based CSS (TTD-CSS) scheme is proposed, inwhich the second local decision bit is sent to the fusioncenter after the failure of the first cooperation to elim-inate the sensing failure. However, the performance ofTTD-CSS is not improved much compared with the con-ventional hard combination CSS. In [17], a two-stagespectrum sensing scheme is proposed for multi-channelsensing. It can decrease the average channel sensing timeby allowing the spectrum detector to focus on the chan-nels that are more likely to be vacant. A two-stage sensingscheme using energy detection in the first stage and cyclo-stationary detection in the second stage is designed in[18], and the second stage detection is performed whenthe decision metric is greater than the threshold. In [19],a two-stage sensing scheme is proposed to minimize thesensing time. Although the above two-stage spectrumsensing schemes can achieve better performance than thesingle-stage schemes [17-19], none of them considers CSS.A two-stage two-bit CSS (TSTB-CSS) is given in [10],and its performance is improved over the conventionalhard combination CSS; however, it uses two-bit overhead,and its sensing time and energy consumption can still bereduced.Although some excellent works have been done in

CSS, most of them focus on achieving high data ratefor SU networks while avoiding the interference to PUnetworks. Consequently, many of these techniques signifi-cantly increase system overhead and energy consumption.However, rapidly rising energy costs and increasingly rigidenvironmental standards have led to an emerging trendof addressing “energy efficiency” aspect of wireless com-munication technologies [20]. CR can play an importantrole in improving energy efficiency in wireless networks,because from the green perspective, spectrum is a naturalresource which should not be wasted but be shared. CRsenable this paradigm with smart operation and agile spec-trum access. Therefore, the “green” requirements of CRshould be well satisfied [21-23].In this article, we propose two time-saving and energy-

efficient one-bit CSS (TSEEOB-CSS) schemes, which have

two stages in the spectrum sensing process. If the SNRis high or no PU exists, only one stage of coarse spec-trum sensing is needed, by which the sensing time andenergy are saved. Otherwise, the second stage of finespectrum sensing will be performed to increase the spec-trum sensing accuracy. In addition, only one bit decisionis sent to the fusion center to minimize the overhead.Simulation results are presented to show that the sensingtime and energy consumption are both reduced signifi-cantly in the proposed schemes. The distinct features andcontributions of this article are as follows.

• We propose a TSEEOB-CSS algorithm, which canimprove the energy efficiency of the system withalmost the same detection performance as theconventional CSS algorithms.

• Based on the first TSEEOB-CSS algorithm, anotheralgorithm is proposed. The algorithm makes full useof the local decisions of the first stage coarsedetection, and it can further reduce the energyconsumption of the first TSEEOB-CSS algorithmwith reliable detection performance.

• The parameters setting is important to the twoproposed algorithms, and it can affect theperformance greatly. Therefore, some rules are givenin this article to help to set the parameters suitably.

• The sensing time and energy consumption of theproposed schemes are reduced significantlycompared to the conventional CSS scheme, and thetime-saving and energy-efficiency performance of theproposed two schemes are analyzed theoretically.

• Extensive simulation results are presented toillustrate the effectiveness of the algorithms inreducing energy consumption and the rules ofparameters setting in the proposed algorithms.

The remainder of this article is organized as follows.In Section 2, the system model of the CR network isdescribed, and the energy detection and CSS schemesare introduced. In Section 3, two proposed TSEEOB-CSSalgorithms are introduced. In Section 4, parameter set-ting of the algorithms is discussed, and the performanceis analyzed. Simulation results are discussed in Section 5.Finally, we conclude this study and present the future workin Section 6.

2 SystemmodelWe consider CSS in a centralized CR network consistingof a cognitive base station (fusion center) and a numberof SUs. In the network, each SU sends its sensing data tothe base station, and the base station combines the sens-ing data from different SUs and makes the final decisionon the presence or absence of the PUs. We assume thesensing data are sent from the SUs to the base station


free of error throughout this article. In this section, energydetection and CSS are introduced.

2.1 Energy detectionThe target of spectrum sensing in CR network is to deter-mine whether a licensed band is currently occupied byany PUs or not. This can be formulated into a binaryhypotheses testing problem as [24]

x(n) ={

ω(n), H0h(n)s(n) + ω(n), H1,

(1)

where n = 0, 1, . . . ,N ; and N is the number of sam-ples. The PU signal, background noise, and received signalare denoted by s(n), ω(n), and x(n), respectively. h(n) isthe impulse response of the channel between the SU andPU. H0 represents the absence of primary signal, while H1represents the presence of primary signal. The noise ω(n)

is assumed to be additive white Gaussian noise with zeromean and unit variance (i.e., ω(n) ∼ N(0, 1)). For easeof analysis, we assume that the channel impulse responseh(n) is unchanged during the sensing process, i.e., h(n) =h [25]. Mathematically, the problem can be formulated asa binary hypothesis testing as follows:

T = 1N

N∑n=1

|x (n)|2{

> λ H1< λ H0,

(2)

where T is the test statistic, and λ is the predeterminedthreshold.Let σ 2

s and σ 2ω denote the transmitted signal power and

noise power, respectively, and assume that σ 2ω = 1. Define

γ = σ 2s /σ 2

ω as the SNR value. The local false alarm anddetection probability of the SU can be represented as [26]

Pf = Pr (T > λ|H0)

= Q(

(λ − 1)√N2

), (3)

Pd = Pr (T > λ|H1)

= Q(

(λ − γ − 1)

√N

2(γ + 1)2

), (4)

where Q(.) is the Q-function.

2.2 CSSWe consider a CR network composed of K SUs and a basestation (fusion center), as shown in Figure 1. We assumethat each SU performs energy detection independentlyand then sends the local decision to the base station,which will fuse all available local decision information toinfer the absence or presence of the PU.

Figure 1 Cooperative spectrum sensing structure in a CRnetwork.

In the conventional hard combination CSS scheme, eachcooperative partner imakes a binary decision based on itslocal observation and then forwards its one-bit decisionDi (Di = 1 stands for the presence of the PU, and Di = 0stands for the absence of the PU) to the base station. Atthe base station, all one-bit decisions are fused togetheraccording to the logic decision fusion rule [27,28], and thefinal decision can be obtained as

Y =K∑i=1

Di

{ ≥ k H1< k H0,

(5)

where H0 and H1 denote the decision made by the basestation that the PU is present or absent, respectively. Thethreshold k is an integer, representing the “n-out-of-K”rule. It can be seen that the OR rule corresponds to thecase of k = 1, the AND rule corresponds to the case ofk = K , and in the VOTING rule k is equal to the minimalinteger larger than K/2 [27].Only one-bit decision information is used in the hard

combination CSS, and thus its detection performance islimited. Soft combination CSS scheme uses the accu-rate sensing results from the SUs, and it can achievethe better performance; however, its overhead is large.Two-bit overhead combination CSS scheme can obtainrelatively higher performance than hard combination CSSwith lower overhead than soft combination CSS, and itmakes a trade-off between hard and soft combination CSSschemes.

3 TSEEOB-CSSThe TSTB-CSS algorithm proposed in [10] can improvethe performance of the conventional hard combinationCSS algorithm; however, its sensing time and energy con-sumption are the same as those in hard combination CSS.In this article, two TSEEOB-CSS algorithms are presentedwith almost the same sensing accuracy, and their sens-ing time and energy consumption are reduced greatlyespecially when the SNR is high or no PU exists.


In the proposed algorithms, we try to reduce theenergy consumption of conventional CSS scheme withNs samples by designing two TSEEOB-CSS schemes withαNs-sample first stage detection and (1 − α)Ns-samplesecond stage detection. We assume that, in the Ns-sampledetection of these three algorithms, the presence/absencestatus of the PU does not change. In other words, thereceived signal is stationary with the observation time T(i.e.,Ns samples); this assumption is commonly used in theliterature [29-32].

3.1 The first proposed algorithmThe first proposed two-stage one-bit CSS scheme isshown in Figure 2, and the proposed scheme is repre-sented by the following steps:

Step 1: Perform the first stage coarse energy detectionwith αNs samples at each SU, where 0 < α < 0.5.The sensing result of the SUi can be calculated as

T1i = 1αNs

αNs∑n=1

|xi (n)|2, (6)

where xi(n) is the nth sample of the signal to besensed at the SUi.If the detection result of SUi(i = 1, 2, . . . ,K)

T1i > λ1 + �, sends the local decision D1i = 1 tothe fusion center indicating that PUs exist; ifT1i < λ1 − �, sends the local decision D1i = 0 tothe fusion center indicating that no PU exists; ifλ1 − � ≤ T1i ≤ λ1 + �, nothing will be sent. λ1 and� are two positive parameters that define the upperthreshold λ1 + � and the lower threshold λ1 − � inthe first stage detection.Step 2: The first stage local decisions D1i are fused atthe fusion center, and the final decision DF can beobtained as

DF =⎧⎨⎩1, More than K/2SUs indicate presence of PU0, More than K/2SUs indicate absence of PUFinal decision cannot be obtained, Otherwise.

(7)

Figure 2 The proposed two-stage one-bit cooperative spectrumsensing scheme.

If the final decision DF can be obtained, DF is sent toeach SU. If the final decision DF cannot be obtained,nothing will be done.Step 3: If the final decision DF is received by the SUs,goes to step 6. If the final decision DF is not receivedby the SUs after τ period, perform the second stagefine energy detection with (1 − α)Ns samples, andthe sensing result of the SUi can be calculated as

T2i = 1(1 − α)Ns

(1−α)Ns∑n=1

|xi (n)|2. (8)

Assume τ � T1 < T2 in Figure 2, and thus τ can beignored compared with T1 and T2.Step 4: Local decision D2i (i = 1, 2, . . . ,K) isobtained through the second stage fine energydetection as

D2i ={1 T2i ≥ λ20 T2i < λ2,

(9)

where T2i is the second stage local sensing result ofSUi using energy detection. Then the local decisionsD2i are sent to the fusion center.Step 5: The second stage local decisions D2i arefused at the fusion center, and the final decision DFcan be obtained according to

DF =⎧⎨⎩ 1

K∑i=1

D2i ≥ K/2

0 Otherwise.(10)

DF is sent to each SU, and goes to step 6.Step 6: Current detection ends.

The first TSEEOB-CSS algorithm described above canachieve almost the same performance as the conventionalhard combination CSS algorithm. Its sensing time andenergy consumption are reduced obviously when no PUexists or the SNR of PU is high, and therefore the sens-ing time can be saved and the energy efficiency can beimproved effectively.

3.2 The second proposed algorithmThe first proposed TSEEOB-CSS algorithm above canachieve better performance with lower energy; however,the sensing results in the first stage coarse detection arenot fully utilized, and its energy efficiency can still beimproved. Thus, a second TSEEOB-CSS algorithm is pro-posed based on the first one, and its structure can also bedescribed in Figure 2. The second TSEEOB-CSS schemeis represented by the following steps:

Step 1: Perform the first stage coarse energydetection with αNs samples at each SU as in Equation(6). If the detection result of SUi (i = 1, 2, . . . ,K)

T1i > λ1 + �, sends the local decision D1i = 1 to


the fusion center indicating that PUs exist; ifT1i < λ1 − �, sends the local decision D1i = 0 tothe fusion center indicating that no PU exists; ifλ1 − � ≤ T1i ≤ λ1 + �, nothing will be sent.Step 2: The first stage local decisions D1i are fused atthe fusion center, and the final decision DF can beobtained as in Equation (7).If the final decision DF can be obtained, DF is sent toeach SU. If the final decision DF cannot be obtained,nothing will be done.Step 3: If the final decision DF is received by the SUs,goes to step 6.If the final decision DF is not received by the SUsafter τ period, for the SUs that did not obtain thelocal decision D1 at the first stage, perform thesecond stage fine energy detection with (1 − α)Nssamples as in Equation (8), and for the SUs thatobtained the local decision at the first stage, no moreprocessing is needed.Assume τ � T1 < T2 in Figure 2, and thus τ can beignored compared with T1 and T2.Step 4: Local decisionD2i(i = 1, 2, . . . ,K) is obtainedthrough the second stage fine energy detection as

D2i =⎧⎨⎩1 T2i ≥ λ2, when D1i was not obtained0 T2i < λ2, when D1i was not obtainedD1i, whenD1i was obtained,

(11)

where T2i is the second stage local sensing result ofSUi using energy detection, D1i is the local decisionof the SUi in the first stage. Then the local decisionsD2i are sent to the fusion center.Step 5: The second stage local decisions D2i arefused at the fusion center, and the final decision DFcan be obtained according to

DF =⎧⎨⎩ 1

K∑i=1

D2i ≥ K/2

0 Otherwise.(12)

DF is sent to each SU, and goes to step 6.Step 6: Current detection ends.

The first proposed TSEEOB-CSS algorithm can achieveexcellent performance with less energy consumption;however, the local decisions of the coarse detection arenot fully used. The local decisions of the coarse detec-tion are obtained through two-threshold sensing scheme,and they are more reliable than those of the conven-tional sensing with the same number of samples. If thelocal decisions of the coarse detection are utilized in thealgorithm, its energy efficiency can be improved. Thus,the second TSEEOB-CSS algorithm is proposed based on

the first one, which uses the local decision of the coarsedetection to improve the energy efficiency of the algo-rithm with the same length of sensing time. However,the detection performance of the second TSEEOB-CSSalgorithm is worse than the first TSEEOB-CSS algorithm.Thus, we should make a trade-off between the energy effi-ciency and detection performance to choose the properalgorithm in practical applications.

4 Threshold setting and performance analysisThe parameters are important to the proposed algo-rithms, so in this section, the threshold setting is dis-cussed. In addition, the energy-efficiency and time-savingperformance of the algorithms are further analyzed.

4.1 Analysis of threshold settingThere are three important parameters in the two pro-posed TSEEOB-CSS algorithms, λ1, λ2, and �. Theseparameters affect the detection performance greatly.Thus, we discuss the parameters setting, and give somerules as follows.

(1) The rules in setting �

Remark 1.For a fixed value of λ1, the larger the value of� is, the better the detection performance can be achievedwith longer sensing time.Assume the square of the sampled signal x(n), |x(n)|2,

follows a distribution with mean μ and variance σ 2. Thesensing result of the first stage detection can be describedas

T = 1αNs

αNs∑n=1

|x (n)|2, (13)

which follows Gaussian distribution with mean μ andvariance σ 2/(αNs) according to the central limit theorem.Therefore, the probability the final decision obtained ateach SU after the first stage coarse detection can beexpressed as

P = 1 −∫ λ1+�

λ1−�

1√2παNsσ

e− (x−μ)22σ2/(αNs) dx. (14)

Hence, when � is larger, and the probability (1 − P)

indicating the need of second stage fine detection cor-respondingly becomes larger, which means the sensingtime becomes longer. Also, the detection performancebecomes better for the probability of second stage detec-tion is larger.Remark 2: � should be smaller as the sample number

αNs of the first stage coarse detection becomes larger.Assume the background noise ω(n) in (1) follows

Gaussian distribution with zero mean and unit variance


(i.e.,ω(n) ∼ N(0, 1)), and thus the square ofω(n), |ω(n)|2,follows chi-square distribution with 1 degree of freedom.The mean of |ω(n)|2 is 1, and its variance is 2. The firststage coarse energy detection result can be expressed as

T = 1αNs

αNs∑n=1

|ω (n)|2, (15)

when no PU exists, and it follows Gaussian distributionwith mean 1 and variance σ 2

H0 = 2/(αNs) according to thecentral limit theorem.We set

� = aσH0 = a√2/(αNs), (16)

where a is a positive constant. When the number of sam-ples becomes larger, e.g., Ns2 = 2αNs, the variance ofthe first stage coarse energy detection result changes to2/(2αNs) = 1/(αNs), and hence � should be set asa√1/(αNs) accordingly. Therefore, the larger the number

of samples is, the smaller the value of � should be.

(2) The rules in setting λ1

Remark 3: λ1 should be set according to the requirementof the false alarm probability (Pf ).In Case I as shown in Figure 3, λ1 is set relatively low

(e.g., below 1). If no PU exists, the distribution of the firststage energy detection result is shown in Figure 3. Theprobability that the detection result is larger than λ1 + �

is so large that Pf cannot be set small (e.g., Pf = 0.01).In Case II as shown in Figure 3, λ1 is set relatively high(e.g., above 1). The probability that the detection resultis smaller than λ1 − � is so large that Pf cannot be setlarge (e.g., Pf = 0.5). For Pf above 0.5 is not meaningful inpractical networks, λ1 is usually set above 1.Furthermore, if certain Pf can be achieved, the detec-

tion performance is better when λ1 is smaller. However,when λ1 becomes smaller, the probability indicating theneed of second stage fine detection becomes larger when

Figure 3 The distribution of the first stage energy detectionresult.

SNR is low or no PU exists, which means the sensing timebecomes longer and the energy consumption becomeslarger.

(3) The rules in setting λ2

As analyzed above, we know that the parameters � andλ1 determine the detection performance and the com-putational complexity of the algorithms. Thus, � andλ1 should be set first according the requirements of thedetection performance and energy consumption whenapplied to practical systems.

λ2 can be set according to the values of � and λ1 When� is fixed, λ2 should be set larger with smaller λ1. Whenλ1 is fixed, λ2 should be set larger with larger �. With cer-tain values of � and λ1, the value of λ2 is deterministic.Thus, after λ1 and� are set, λ2 can be set according to therequired value of Pf through measurements in advance.The above rules in setting �, λ1, and λ2 are all suitable

for both of the two proposed TSEEOB-CSS algorithms.

4.2 Analysis of energy-efficiency and time-savingperformance

Remark 4: The energy consumption and sensing time ofthe proposed schemes are reduced significantly comparedto the conventional CSS scheme with the same number ofsamples, especially when the SNR of the PU signal is highor no PU exists.The probability of the local decisionD1i in the first stage

coarse detection at the ith SU equal to 1 and 0 can beexpressed, respectively, as

P1 = Q(

(λ1 + � − γ − 1)

√αNs

2(γ + 1)2

)Pr(H1)

+ Q(

(λ1 + � − 1)√

αNs2

)Pr(H0),

(17)

P0=(1−Q

((λ1−�−γ −1)

√αNs

2(γ + 1)2

))Pr(H1)

+(1−Q

((λ1−�−1)

√αNs2

))Pr(H0),

(18)

where Pr(H1) and Pr(H0) = 1−Pr(H1) denote the proba-bilities of the presence and absence of the primary signal,respectively.The energy consumption of the spectrum sensing is

mainly caused by the energy detection using the samplesof the received signal at all the SUs, and we can define the


energy consumption of the conventional CSS algorithmthrough a function of the number of the samples as

ECSS = KNse0, (19)

where K is the number of SUs in the CSS, Ns is the num-ber of samples at each SU in the detection, and e0 is theenergy consumption corresponding to one sample of thedetection.The energy consumption of the first and second pro-

posed TSEEOB-CSS algorithms can be expressed as

E1 = KαNse0 + K((1 − α)Ns)e0⎛⎜⎜⎝1 −

K∑i=

⌊K2

⌋+1

(Ki

)Pi1 (1 − P1)K−i (20)

−K∑

i=⌊K2

⌋+1

(Ki

)Pi0 (1 − P0)K−i

⎞⎟⎟⎠ ,

E2 = KαNse0 + K((1 − α)Ns)e0⎛⎜⎜⎝1 −

K∑i=

⌊K2

⌋+1

(Ki

)Pi1 (1 − P1)K−i (21)

−K∑

i=⌊K2

⌋+1

(Ki

)Pi0(1−P0)K−i

⎞⎟⎟⎠(1−P1−P0).

From (19)–(21), we can easily obtain

E2 < E1 < ECSS. (22)

Thus, we can conclude that the energy consumption isreduced significantly by the two proposed CSS schemes,and the energy consumption of the second TSEEOB-CSSalgorithm is much lower than that of the first algorithm.Similarly, assuming the sensing time of the CSS algo-

rithms is mainly determined by the energy detection usingthe samples of the received signal at all the SUs, the sens-ing time of conventional CSS algorithm can be definedthrough a function of the number of the samples as

TCSS = Nst0, (23)

where t0 is the duration corresponding to one sample ofthe detection.

The sensing time of the first and second proposedTSEEOB-CSS algorithms is the same, and can be repre-sented as

T1= T2 = αNst0 + ((1 − α)Ns)t0⎛⎜⎜⎝1 −

K∑i=

⌊K2

⌋+1

(Ki

)Pi1 (1 − P1)K−i (24)

−K∑

i=⌊K2

⌋+1

(Ki

)Pi0 (1 − P0)K−i

⎞⎟⎟⎠ .

From (23) and (24), we can obtain that

T1 = T2 < TCSS. (25)

Thus, we can conclude that the time consumption isreduced significantly by the two proposed CSS schemes,and the time consumption of the two algorithms is thesame.The time and energy consumption of the proposed algo-

rithms can be reduced effectively, and we will clarify itbriefly. When there are PUs in the network, the first stagecoarse energy detection result T follows a distributionwith the mean of 1 + μs. If the SNR of PU signal is larger,1 + μs will be larger, and thus the probability that T >

λ1 + � will also become larger. This means the proba-bility indicating the need of second stage fine detectionbecomes smaller, and thus the sensing time and energyconsumption will be reduced due to the low probabilityof the need of second stage fine detection. When no PUexists in the network, the probability that T < λ1 − � isrelatively large because λ1 is usually set above 1. There-fore, the probability indicating the need of second stagefine detection is relatively small, and the sensing time andenergy consumption will also be reduced greatly when noPU exists.From the above analysis, we can conclude that the sens-

ing time and energy consumption is reduced greatly whenthe SNR of PU signal is high or no PU exists in theproposed two algorithms, and it enables a “green” CRnetwork.

5 Simulation results and discussionsIn this section, we illustrate the performance of the pro-posed TSEEOB-CSS algorithms using computer simula-tions.We assume that the signal of PU is BPSKmodulated,and the baseband symbol rate fb is equal to 1Mbps. Thesampling frequency fs at SUs is 64MHz. In the TSEEOB-CSS algorithms, the number of samples of the two stages,Ns, is set to 768, α=1/3, and � is set to 0.1, which is equalto 1.13σH0.


−16 −14 −12 −10 −8 −6 −40.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

SNR (dB)

P d

Energy detection, 768 samplesCSS, 256 samplesCSS, 768 samplesTTD−CSS,VOTINGTSTB−CSS, VOTINGFirst TSEEOB−CSS, λ

1=1.125

First TSEEOB−CSS, λ1=1.100


Figure 4 Detection performance against SNR comparison when Pf = 0.1.

First, we compare the detection performance of the firstTSEEOB-CSS algorithm with that of TTD-CSS algorithmin [16] and TSTB-CSS algorithm in [10]. In TTD-CSSalgorithm, λ is set to 1.007 and � is set to 0.1, the numberof samples, N, is set to 768, and VOTING rule is used. InTSTB-CSS algorithm, the number of samples in the first

and second stages is both set to 768/2 = 384, λ is setto 1.034, and � is set to 0.1. The detection performanceof the energy detection and the hard combination CSSscheme is also compared. The detection probability (Pd) ofthese algorithms is compared in Figure 4, with unit powerof the background noise and Pf = 0.1.

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.20

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Pf

P d

Energy detection, 768 samples

CSS, 768 samples

First TSEEOB−CSS, different λ1 when P

f varies

Figure 5 ROC curves comparison of the detectors when SNR is equal to−10 dB. In the first TSEEOB-CSS algorithm, λ1 is 1.10 whenPf ∈ (0, 0.05], λ1 is 1.05 when Pf ∈ (0.05, 0.1], and λ1 is 1.00 when Pf ∈ (0.1, 0.2].


Table 1 Parameter λ1 setting for the first TSEEOB-CSSalgorithmwith different PfPf ∈ (0, 0.05] (0.05, 0.1] (0.1, 0.2]

λ1 1.10 1.05 1.00

From Figure 4 we can see that the performance of thefirst TSEEOB-CSS algorithmwith λ1 equal to 1.125, 1.100,and 1.050, and corresponding λ2 equal to 1.024, 1.0295,and 1.033, respectively, is all better than that of 768-sample energy detection algorithm and 256-sample hardcombination CSS algorithm. The larger the value of λ1is, the better performance the first TSEEOB-CSS algo-rithm can achieve. The detection performance of the firstTSEEOB-CSS algorithm with λ1 = 1.1000 is close tothat of TTD-CSS algorithm, TSTB-CSS algorithm, and768-sample hard combination CSS algorithm, and its per-formance is even better than that of these algorithmswhen λ1 is equal to 1.050.In Section 4, we have analyzed the rules in setting λ1,

and we can conclude that when the required Pf is rela-tively small, λ1 should be set larger accordingly, and whenthe required Pf is relatively large, λ1 should be set smallercorrespondingly. For example, from the simulation resultsin Figure 4 we can see that when Pf = 0.1, the detec-tion performance of the first proposed algorithm withλ1 = 1.05 is better than that with λ1 = 1.1. However,the requirement Pf < 0.05 cannot be achieved whenλ1 = 1.1, and thus λ1 = 1.05 should be selected inthis case. Therefore, λ1 should be set properly according

to the required Pf . The receiver operation characteristic(ROC) curves of the energy detection with 768 samples,the hard combination CSS algorithm with 768 samplesand the first TSEEOB-CSS algorithm with 256 samples inthe first stage and 512 samples in the second stage aredepicted in Figure 5. Pf is usually set below 0.2, and thusonly part of the ROC curves with Pf ∈ (0, 0.2] are givenin Figure 5. λ1 should be set according to the required Pf ,and the λ1 setting in the simulation of Figure 5 is listedin Table 1.From the simulations results in Figure 5, we can observe

that the ROC performance of the first TSEEOB-CSS algo-rithm is almost equal to that of the CSS algorithmwith dif-ferent λ1 set according to the required Pf in Table 1. Thus,the proposed first TSEEOB-CSS algorithm can achievethe same performance as CSS algorithm with much lesssensing time and energy consumption.In Section 3, the second TSEEOB-CSS algorithm is also

proposed. It can improve the energy efficiency of the firstTSEEOB-CSS algorithm, with its sensing performance alittle decrease. The detection performance of the first andsecond TSEEOB-CSS algorithms is compared in Figure 6with unit power of the background noise and Pf = 0.1.The detection performance of the CSS algorithm with 768samples is also adopted in Figure 6.From the simulation results in Figure 6, we can see

that the detection performance of the second TSEEOB-CSS algorithm is close to that of the first TSEEOB-CSSalgorithm and the CSS algorithm with 768 samples.A remarkable advantage of the proposed TSEEOB-CSS

algorithms is that it can save sensing time and energy

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Figure 6 Detection performance comparison between the first and second TSEEOB-CSS algorithms when Pf = 0.1.


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Figure 7 Probability of the second stage detection of the first and second TSEEOB-CCS algorithms with different λ1 values.

consumption greatly, which will be discussed in the fol-lowing. The probability of second stage fine detectionunder different SNRs of the two proposed algorithms isanalyzed in Figure 7.We can see that although the detection performance

with λ1 = 1.125 is worse than that with λ1 = 1.100and 1.050 (from Figure 4), the probability of second stagedetection is the lowest with the shortest sensing time

and smallest energy consumption when the SNR is lowor no PU exists. Hence, we can conclude that the sens-ing time is shorter, the energy consumption is smaller,and the detection performance is worse with larger λ1. Inpractical CR networks, if the sensing time or energy con-sumption is the most important factor to be considered,we can use large λ1; otherwise, λ1 should be smaller. Inaddition, the sensing time and energy consumption of the

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2nd, λ1=1.100

2nd, λ1=1.050

Figure 8 Energy-efficiency performance comparison of the proposed algorithms to the CCS algorithmwith different λ1 values.


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Figure 9 Detection performance comparison of the two proposed algorithmwith different values of�. λ1 is set to 1.1 in the simulation.

second TSEEOB-CCS algorithm is much lower than thoseof the first TSEEOB-CCS algorithm, and about 20% ofthe energy consumption can be saved in the second stagedetection.Using the probability of second stage fine detection

obtained from Figure 7, we can calculate the percentof the reduced processing energy by the TSEEOB-CSS

algorithms under different SNRs, and the results areshown in Figure 8.From Figure 8, we can see that when SNR is larger

than −5 dB, the percentage of the reduced energy of thesetwo proposed algorithm is more than 60, and the maximalpercentage of the reduced energy is 66.67. When no PUexists in the network, the energy consumption can also be

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Figure 10 Probability of the second stage detection of the first and second TSEEOB-CCS algorithms with different� values. λ1 is set to 1.1in the simulation.


reduced by 50, 35, and 15% when λ1 is set to 1.125, 1.100,and 1.1050, respectively, in the first TSEEOB-CCS algo-rithm; while in the second TSEEOB-CCS algorithm, theenergy consumption can be reduced by 55, 45, and 30%with these values of λ1, respectively. Thus, the proposedalgorithms can improve energy efficiency of the conven-tional CSS algorithm significantly with almost the samesensing performance.In Theorems 1 and 2 of the Section 4, we know that the

value of the parameter� affects the detection and energy-efficiency performance greatly, so the performance of thealgorithms according to � is analyzed. In the followingsimulation, we set λ = 1.1. The detection performance ofthe two proposed algorithms with different values of � isshown in Figure 9, and the probability of second stage finedetection with different �s of the algorithms is comparedin Figure 10.The simulation results in Figure 9 have shown that the

larger � is, and the better detection performance canbe obtained for both the two algorithms. However, it isshown in Figure 10 that when � becomes larger, the prob-ability of second stage fine detection, which also meansthe energy efficiency becomes lower. Thus, in the practi-cal systems, we should choose appropriate value of � tobalance between the detection performance and energyefficiency.Plenty of simulation results are given in this section, and

they have shown the effectiveness of the proposed algo-rithms. The algorithms can achieve reliable detection per-formance withmuch lower energy consumed. The rules insetting the parameters of the two algorithms described inSection 4 are also proved.

6 Conclusions and future workIn this article, we have presented two two-stage time-saving and energy-efficient CSS algorithms for CR net-works. In the algorithms, only one stage of sensing isneeded when the SNR is high or there is no PU. One-bit decision is sent by each SU to minimize the overhead.The second TSEEOB-CSS algorithm fully uses the localdecision of the coarse detection, and it can achieve evenbetter energy-efficiency performance with its sensing per-formance close to the first one and the CSS algorithm.We have also further analyzed the design parameters inthe proposed algorithms, and given the rules in settingthe parameters. Simulation results were presented to showthe effectiveness of the proposed algorithm.In this article, energy detection is used due to its

low computational complexity; however, it cannot avoidthe noise uncertainty effectively [11,33]. When the back-ground noise fluctuates severely, the performance ofthe proposed algorithm using energy detection becomesunacceptable. In our previous research work, a spec-trum sensing detector based on the entropy of spectrum

amplitude with relatively low computational complexitywas proposed, and it is robust to the noise uncertainty[10]. The proposed entropy detection algorithm [10] caneasily be applied to the proposed TSEEOB-CSS algo-rithms. In our future work, we will further apply entropy-based spectrum detection to the TSOBEE-CSS algorithmsproposed in this article, and conduct more research toconquer the problem of noise uncertainty. Moreover,it is interesting to derive closed-form expressions fordetection performance and energy efficiency, and ana-lyze the effects of different parameters on the systemperformance.

Competing interestsThe authors declare that they have no competing interests.

AcknowledgementsWe thank the reviewers for their detailed reviews and constructive comments,which have helped to improve the quality of this article. This study wassupported in part by the National Natural Science Foundation of China (NSFC)under Grant no. 61201224, the China Postdoctoral Science Foundation under2012M510806, and the Natural Science Foundation of Changzhou City underCJ20120015.

Author details1School of Information and Communication Engineering, Dalian University ofTechnology, Dalian, China. 2Computer Engineering, Carleton University,Ottawa, ON K1S 5B6, Canada. 3School of Engineering and ComputingSciences, Durham University, Durham, DH1 3LE, UK. 4Institute ofTelecommunications, King’s College London, London, WC2R 2LS, UK.5Changzhou Institute of Dalian University of Technology, Changzhou, China.

Received: 26 September 2012 Accepted: 19 March 2013Published: 3 May 2013

References1. J Mitola, GQ Maguire, Cognitive radio: making software radios more

personal. IEEE Pers. Commun. 6(4), 13–18 (1999)2. Haykin S, Cognitive radio: brain-empowered wireless communications.

IEEE J. Sel. Areas Commun. 23(2), 201–220 (2005)3. Zhao N, Sun H, Robust power control for cognitive radio in spectrum

underlay networks. KSII Trans. Internet Inf. Syst. 5(7), 1214–1229 (2011)4. S Haykin, Thomson D J, JH Reed, Spectrum sensing for cognitive radio.

Proc. IEEE. 97(5), 849–877 (2009)5. T Cui, FF Gao, A Nallanathan, Optimization of cooperative spectrum

sensing in cognitive radio. IEEE Trans. Veh. Technol. 60(4), 1578–1589(2011)

6. HV Poor, An Introduction to Signal Detection and Estimation. (Springer,New York, 1994)

7. N Ghozzi, F Marx, M Dohler, J Palicot, in Cyclostationarilty-based test fordetection of vacant frequency bands, vol. 1. Proceedings of theInternational Conference on Cognitive Radio Oriented Wireless Networksand Communications (Mykonos Island Greece, 2006, 2006), pp. 43–47

8. SV Nagaraj, Entropy-based spectrum sensing in cognitive radio. SignalProcess. 89(2), 174–180 (2009)

9. YL Zhang, QY Zhang, T Melodia, A frequency-domain entropy-baseddetector for robust spectrum sensing in cognitive radio networks. IEEECommun. Lett. 14(6), 533–535 (2010)

10. N Zhao, A novel two-stage entropy-based robust cooperative spectrumsensing scheme with two-bit decision in cognitive radio. Wirel. Pers.Commun. 69(4), 1551–1565 (2013)

11. D Cabric, SM Mishra, RM Brodersen, in Proceedings of the AsilomarConference on Signals, Systems, and Computers, vol. 1. Implementationissues in spectrum sensing for cognitive radios, 2004 (Pacific Grove CA,2004), pp. 772–776

12. YQ Zhao, SY Li, N Zhao, ZL Wu, A novel energy detection algorithm forspectrum sensing in cognitive radio. Inf. Technol. J. 9(8), 1659–1664 (2010)


13. G Ganesan, Y Li, in Cooperative spectrum sensing in cognitive radionetworks, vol. 1. Proceedings of the International Symposium on NewFrontiers in Dynamic Spectrum Acess Networks (Baltimore, MD, 2005,2005), pp. 137–143

14. J Ma, Y Li, in Proceedings of the IEEE Global Telecommunication Conference,vol. 1. Soft combination and detection for cooperative spectrum sensingin cognitive radio networks (Washington, DC, 2007, 2007), pp. 3139–3143

15. M Mustonen, M Matinmikko, A Mammela, in Cooperative spectrum sensingusing quantized soft decision combining, vol. 1. Proceedings of theInternational Conference on Cognitive Radio Oriented Wireless Networskin Communication (Hanover, Germany, 2009, 2009), pp. 1–5

16. HX Xia, GP Zhang, HB Xu, A flexible cooperative spectrum sensing schemefor cognitive radio networks. IEICE Electron. Express. 8(8), 542–548 (2011)

17. S Fazeli-Dehkordy, KN Plataniotis, S Pasupathy, in Two-stage spectrumdetection in cognitive radio networks, vol. 1. Proceedings of the IEEEInternational Conference on Acoustics, Speech, and Signal Processing(Dallas, TX, 2010, 2010), pp. 3118–3121

18. S Maleki, A Pandharipande, G Leus, in Two-stage spectrum sensing forcognitive radios, vol. 1. Proceedings of the IEEE International Conferenceon Acoustics, Speech, Signal and Processing (Dallas, TX, 2010, 2010),pp. 2946–2949

19. PR Nair, AP Vinod, AK Krishna, in A fast two stage detector for spectrumsensing in cognitive radios, vol. 1. Proceedings of the IEEE VehicularTechnology Conference, Fall (San Francisco, CA, 2011, 2011), pp. 1–5

20. GY Li, Z Xu, C Xiong, C Yang, S Zhang, Y Chen, S Xu, Energy-efficientwireless communications: tutorial, survey, and open issues. IEEE Wirel.Commun. 18(6), 28–35 (2011)

21. G Gur, F Alagoz, Green wireless communications via cognitive dimension:an overview. IEEE Netw. 25(2), 50—56 (2011)

22. R Xie, FR Yu, H Ji, Y Li, Energy-efficient resource allocation forheterogeneous cognitive radio networks with femtocells. IEEE Trans.Wirel. Commun. 11(11), 3910–3920 (2012)

23. D Grace, J Chen, T Jiang, PD Mitchell, in Using cognitive radio to deliver’green’ communications, vol. 1. Proceedings of the InternationalConference on Cognitive Radio Oriented Wireless Networks andCommunications (Hanover, Germany, 2009, 2009), pp. 1–6

24. FF Digham, MS Alouini, MK Simon, On the energy detection of unknownsignals over fading channels. IEEE Trans. Commun. 55(1), 21–24 (2007)

25. SG Z Quan, AH Cui, HV Sayed, Poor, Optimal multiband joint detection forspectrum sensing in cognitive radio networks. IEEE Trans. Signal Process.57(3), 1128–1140 (2009)

26. H Urkowitz, Energy detection of unknown deterministic signals. Proc IEEE.55(4), 523–531 (1967)

27. E Visotsky, S Kuffner, R Peterson, in On collaborative detection of TVtransmissions in support of dynamic spectrum sharing, vol. 1. Proceedingsof the IEEE International Symposium on New Frontiers in DynamicSpectrum Access Networks (Baltimore, MD, 2005, 2005), pp. 131—136

28. W Zhang, RK Mallik, KB Lataief, in Cooperative spectrum sensingoptimization in cognitive radio networks, vol. 1. Proceedings of the IEEEInternational Conference on Communication (Beijing, China, 2008, 2008),pp. 3411–3415

29. EYC Peh, Y-C Liang, YL Guan, Y Zeng, Optimization of cooperative sensingin cognitive radio networks: a sensing-throughput tradeoff view. IEEETrans. Veh. Technol. 58(9), 5294–5299 (2009)

30. Z Li, FR Yu, M Huang, A distributed consensus-based cooperativespectrum sensing in cognitive radios. IEEE Trans. Veh. Technol. 59(1),383–393 (2010)

31. FR Yu, M Huang, H Tang, Biologically inspired consensus-based spectrumsensing in mobile ad hoc networks with cognitive radios. IEEE Netw.24(3), 26–30 (2010)

32. H Sun, A Nallanathan, N Zhao, C Wang, in Green data transmission in powerline communications, vol. 1. Proceedings of the IEEE GlobalTelecommunication Conference (Anaheim, CA, 2012, 2012), pp. 1–5

33. R Tandra, A Sahai, SNR walls for signal detection. IEEE J. Sel. Topics SignalProcess. 2(1), 4–17 (2008)

doi:10.1186/1687-1499-2013-120Cite this article as: Zhao et al.: Energy-efficient cooperative spectrumsensing schemes for cognitive radio networks. EURASIP Journal on WirelessCommunications and Networking 2013 2013:120.

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