2676 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 9, NO. 8, AUGUST 2010
Outage Probability Analysis ofCognitive Transmissions:
Impact of Spectrum Sensing OverheadYulong Zou, Student Member, IEEE, Yu-Dong Yao, Senior Member, IEEE, and Baoyu Zheng, Member, IEEE
AbstractβIn cognitive radio networks, a cognitive source noderequires two essential phases to complete a cognitive transmissionprocess: the phase of spectrum sensing with a certain timeduration (also referred to as spectrum sensing overhead) to detecta spectrum hole and the phase of data transmission through thedetected spectrum hole. In this paper, we focus on the outageprobability analysis of cognitive transmissions by considering thetwo phases jointly to examine the impact of spectrum sensingoverhead on system performance. A closed-form expression of anoverall outage probability that accounts for both the probabilityof no spectrum hole detected and the probability of a channeloutage is derived for cognitive transmissions over Rayleigh fadingchannels. We further conduct an asymptotic outage analysis inhigh signal-to-noise ratio regions and obtain an optimal spectrumsensing overhead solution to minimize the asymptotic outageprobability. Besides, numerical results show that a minimizedoverall outage probability can be achieved through a tradeoff indetermining the time durations for the spectrum hole detectionand data transmission phases. In this paper, we also investigatethe use of cognitive relay to improve the outage performance ofcognitive transmissions. We show that a significant improvementis achieved by the proposed cognitive relay scheme in terms ofthe overall outage probability.
Index TermsβCognitive radio, spectrum sensing, overhead,cognitive relay transmission, outage probability.
I. INTRODUCTION
THERE are increasing demands for the wireless radiospectrum with the emergency of many new wireless
communication networks (e.g., wireless local area networks,wireless sensor networks, Bluetooth and so on). Meanwhile,according to the Federal Communications Commission (FCC),large portions of the licensed wireless spectrum resources are
Manuscript received January 26, 2010; revised April 7, 2010; acceptedMay 28, 2010. The associate editor coordinating the review of this paper andapproving it for publication was J. Olivier.
Y. Zou is with the Institute of Signal Processing and Transmission,Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu210003, China, and with the Electrical and Computer Engineering Depart-ment, Stevens Institute of Technology, Hoboken, NJ 07030, USA (e-mail:[email protected], [email protected]).
Y.-D. Yao is with the Electrical and Computer Engineering Depart-ment, Stevens Institute of Technology, Hoboken, NJ 07030, USA (e-mail:[email protected]).
B. Zheng is with the Institute of Signal Processing and Transmission, Nan-jing University of Posts and Telecommunications, Nanjing, Jiangsu 210003,China (e-mail: [email protected]).
This work was partially supported by the Postgraduate Innovation Pro-gram of Scientific Research of Jiangsu Province (Grant Nos. CX08B 080Z,CX09B 150Z) and the National Natural Science Foundation of China (GrantNo. 60972039).
Digital Object Identifier 10.1109/TWC.2010.061710.100108
under utilized [1]. In order to address this issue, cognitive radio[2] has been proposed by allowing a cognitive user to access aspectrum hole (that is a frequency band licensed to a primaryuser but not utilized by that user at a particular time and aspecific geographic location [3]), which promotes the efficientutilization of the licensed spectrum. As stated in [3], cognitiveradio is an intelligent wireless communication system, whichlearns from its surrounding environment and adapts its internalstates to statistical variations of the environment.
In cognitive radio networks, a cognitive source node typi-cally requires two essential phases to complete its transmissionto its destination: 1) a spectrum sensing phase (also known as aspectrum hole detection phase), in which the cognitive sourceattempts to detect an available spectrum hole with a certaintime duration (referred to as spectrum sensing overhead), and2) a data transmission phase, in which data is transmitted tothe destination through the detected spectrum hole. The twophases have been studied individually in terms of differentdetection [5] - [13] or different transmission [14] - [22]techniques.
In spectrum sensing, the energy detection [5], [6] and thematched filter detection [7], [8] have been proposed first andinvestigated extensively. It has been shown that the energydetection can not differentiate signal types, which could leadto more false detections triggered by some unintended inter-ference signals [5]. Although the matched filter is an optimaldetector in stationary Gaussian noise scenario, it requires priorinformation of the primary user signal, such as the pulseshape, modulation type and so on [4]. As an alternative, thecyclostationary feature detector has been presented in [9],[10], which can differentiate the modulated signal from theinterference and additive noise. The advantage of cyclosta-tionary detection comes at the expense of high computationalcomplexities since it requires an extra training process toextract significant features. Meanwhile, in order to combatfading effects, a collaborative spectrum sensing approach hasbeen proposed [11], where the detection results from multiplecognitive users are pooled together at a fusion center by usinga logic rule. Recently, in [12], [13], the authors have appliedcooperative diversity [16], [17] to the detection of the primaryuser and shown that the detection time can be reduced greatlythrough the cooperation between the cognitive users.
In the wireless transmission research, a large number ofstudies are motivated to combat the large-scale and small-scale fading. Two wireless transmission technologies, i.e., the
1536-1276/10$25.00 cβ 2010 IEEE
ZOU et al.: OUTAGE PROBABILITY ANALYSIS OF COGNITIVE TRANSMISSIONS: IMPACT OF SPECTRUM SENSING OVERHEAD 2677
relay and the diversity techniques, have been proposed [14]- [23] in order to provide reliable systems with high data-rates. As stated in [14], the relay technique has been generallyconsidered as an effective method to improve the capacityand coverage for next-generation wireless networks. On theother hand, multiple-input and multiple-output (MIMO) [15]has been proposed as an effective diversity scheme that canincrease the channel capacity greatly by employing multipleantennas at both the transmitter and receiver. In [16] - [23],the authors have put forward the cooperative diversity toimplement virtual multiple antennas through the cooperationbetween the source and the relay nodes, which combinesthe advantages of the relay and diversity techniques. Morerecently, in [28], [29], the cooperative relay technology hasalso been explored at the medium access control (MAC) andhigher layers to improve the system throughput of cognitiveradio networks.
Notice that the spectrum hole detection and data transmis-sion phases can not be designed and optimized in isolationsince they could affect each other. For example, an availablespectrum hole would get wasted if the cognitive source has notdetected the hole within a certain time duration. This decreasesthe spectrum hole utilization efficiency. While increasing thetime duration of the hole detection phase improves the detec-tion probability of spectrum holes, it comes at the expenseof a reduction in transmission performance since less time isnow available for the data transmission. In [24], the authorshave studied a tradeoff in optimizing the performance ofthe secondary user under a targeted level of the primaryinterference protection. In [25], [26], a sensing-throughputtradeoff has been investigated for cognitive radio networks,where the research focus is on the maximization of secondarythroughput under the constraint of primary user protection.Besides, another optimal spectrum sensing framework hasbeen explored in [27], where the optimization objective is tominimize the spectrum sensing time without considering thetransmission link condition and, moreover, only an additivewhite Gaussian noise (AWGN) channel is considered for thesensing performance analysis. However, the transmission linkcondition plays an important role in optimizing the cognitivetransmission performance. For example, if a small-scaledfading term is taken into account for the transmission link, anoutage event may occur given a fixed transmit power and datatransmission rate when the transmission link is in relativelydeep fading. In this case, a longer time duration is needed forthe data transmission phase to reduce the outage probabilitysatisfying a predefined target.
The main contributions of this paper are described asfollows. First, unlike the separate analysis of the spectrumhole detection and data transmission phases [5] - [23], wejointly consider the two parts to examine the impact of thespectrum sensing overhead on the overall system performance.Second, we focus on the minimization of outage probabilityof secondary transmissions under a required probability ofdetection of primary users over Rayleigh fading channels,differing from [24] - [27] where the research is to either mini-mize the spectrum sensing time or to maximize the secondarythroughput. Third, we derive a closed-form expression ofthe overall outage probability over Rayleigh fading channels,
Fig. 1. (a) Coexistence of a primary wireless network and a cognitiveradio network; (b) the allocation of time durations: hole detection versus datatransmission.
which accounts for both the probability of no spectrum holedetected and the probability of a channel outage, for cognitivetransmissions. Finally, in order to improve the system perfor-mance, we propose a cognitive relay transmission scheme andpresent its outage probability analysis over Rayleigh fadingchannels.
The remainder of this paper is organized as follows. Sec-tion II describes the system model of cognitive transmissionthat considers both the spectrum hole detection and datatransmission phases, followed by the outage analysis in Sec-tion III, where the corresponding numerical evaluations arealso provided to show the system performance of cognitivetransmission. In Section IV, we propose a cognitive relaytransmission scheme along with its performance analysis.Numerical results are also presented in this section. Finally,we make some concluding remarks in Section V.
II. SYSTEM MODEL
Consider a cognitive radio network where a cognitive source(CS) is sending data to a cognitive destination (CD) over aspectrum hole unoccupied by a primary user (PU), as shown inFig. 1 (a). Specifically, if CS detects an idle licensed frequencychannel (that is not occupied currently by PU), it will usethis channel for its data transmission (secondary data trans-mission); otherwise, CS will continue detecting the licensedfrequency band to seek an available transmission opportunity.From Fig. 1, one can see that the whole cognitive transmissionprocess is divided into two phases: 1) hole detection of thelicensed band and 2) data transmission from CS to CD. Theallocation of time durations between the two phases is depictedin Fig. 1 (b), where the detection phase and the transmissionphase occupy πΌ and 1-πΌ fractions, respectively, of one timeslot, and πΌ is referred to as spectrum sensing overhead thatcan be varied to optimize the system performance. Notice thatsuch a cognitive transmission protocol will be extended to acognitive relay network in Section IV.
In Fig. 1 (a), each transmission link between any twonodes is modeled as a Rayleigh fading process and,moreover, the fading channel is considered as constant
2678 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 9, NO. 8, AUGUST 2010
during one time slot. The transmit power of PU and CSare ππ and ππ , respectively. For notational convenience, letπ»π(π, 1) and π»π(π, 2) denote whether or not the licensedband is occupied by PU in the first and second phases,respectively, of time slot π, i.e, π»π(π, 1) = π»π(π, 2) = π»0
represents the band being unoccupied by PU and,otherwise, π»π(π, 1) = π»π(π, 2) = π»1. A Bernoullidistribution with parameter Pa is used to model therandom variable π»π(π, 1), i.e., Pr (π»π(π, 1) = π»0) = Pa
and Pr (π»π(π, 1) = π»1) = 1 β Pa. Moreover, thetransition between π»π(π, 1) and π»π(π, 2) is modeled asa Markov chain with parameter 1 β exp[β(1β πΌ)π],i.e., Pr (π»π(π, 2) = π»1β£π»π(π, 1) = π»0) =Pr (π»π(π, 2) = π»0β£π»π(π, 1) = π»1) = 1 β exp[β(1β πΌ)π]and Pr (π»π(π, 2) = π»0β£π»π(π, 1) = π»0) =Pr (π»π(π, 2) = π»1β£π»π(π, 1) = π»1) = exp[β(1β πΌ)π],where π is the characteristic parameter. Thus, the signaldetected by CS from PU can be expressed as
π¦π (π) = βππ (π)βπππ(π, 1) + ππ (π) (1)
where βππ (π) is the fading coefficient of the channel from PUto CS at time slot π, ππ (π) is AWGN with zero mean and thepower spectral density π0, and π(π, 1) is defined as
π(π, 1) =
{0, π»π(π, 1) = π»0
π₯π(π, 1), π»π(π, 1) = π»1
where π₯π(π, 1) is the transmission signal of PU during the firstphase (namely hole detection phase) of time slot π. Based on(1), CS will make a decision οΏ½ΜοΏ½π (π) on whether the licensedband is occupied by PU. Specifically, οΏ½ΜοΏ½π (π) = π»0 consid-ers that the band is available for secondary transmissions;οΏ½ΜοΏ½π (π) = π»1 considers the band is unavailable. Therefore,the signal received at CD from CS can be written as
π¦π(π) = βπ π(π)βππ π½(π) + βππ(π)
βπππ(π, 2) + ππ(π) (2)
where βπ π(π) and βππ(π) are the fading coefficients of thechannel from CS to CD and that from PU to CD, respectively.In addition, π½(π) and π(π, 2) are defined as follows,
π½(π) =
{π₯π (π), οΏ½ΜοΏ½π (π) = π»0
0, οΏ½ΜοΏ½π (π) = π»1
and
π(π, 2) =
{0, π»π(π, 2) = π»0
π₯π(π, 2), π»π(π, 2) = π»1
where π₯π (π) and π₯π(π, 2) are the transmission signals of CSat time slot π and that of PU at the second phase of time slotπ, respectively.
III. OUTAGE ANALYSIS OF COGNITIVE TRANSMISSIONS
IN RAYLEIGH FADING CHANNELS
In this section, we start with the outage analysis of cognitivetransmissions and then conduct the numerical evaluations toshow the impact of spectrum sensing overhead.
A. Outage Probability Analysis
As is known [31], [16], [19], an outage event occurs whenthe channel capacity falls below the data rate π . Following(2), the outage probability of cognitive transmissions is givenby (3) at the top of the following page, where the coefficient1βπΌ is due to the fact that only 1βπΌ fraction of a time slotis utilized for the CSβs data transmission phase. Consideringconditional probabilities, (3) can be expanded as (4) at thetop of the following page, where Ξ = [2π /(1βπΌ) β 1]/πΎπ ,πΎπ = ππ /π0 and πΎπ = ππ/π0. For notational convenience,let Pa = Pr(π»π(π, 1) = π»0) be the probability that there isa hole, Pf = Pr(οΏ½ΜοΏ½π (π) = π»1β£π»π(π, 1) = π»0) and Pd =Pr(οΏ½ΜοΏ½π (π) = π»1β£π»π(π, 1) = π»1) be the probability of falsealarm and the probability of detection of the primary-user-presence, respectively. Accordingly, (4) can be rewritten as (5)at the top of the following page. Note that there are severalapproaches available for the spectrum hole detection, such asenergy detector, matched filter detector and cyclostationaryfeature detector as shown in [5] - [13]. Throughout this paper,we consider the use of an energy detector for the spectrumsensing performance analysis. Accordingly, considering anenergy detection method and following (1), the output statisticof energy detector at time slot π is given by
π (π¦) =1
π
πβπ=1
β£π¦π (π, π)β£2 (6)
where π = πΌπππ is the number of samples, π and ππ are thetime slot length and sample frequency, respectively. From (6),the probability of false alarm and the probability of detection,Pf(π) and Pd(π), of the primary-user-presence at time slot πare calculated as
Pf(π) = Pr(οΏ½ΜοΏ½π (π) = π»1β£π»π(π, 1) = π»0)
= Pr{π (π¦) > πΏβ£π»π(π, 1) = π»0}(7)
and
Pd(π) = Pr(οΏ½ΜοΏ½π (π) = π»1β£π»π(π, 1) = π»1)
= Pr{π (π¦) > πΏβ£π»π(π, 1) = π»1}(8)
where πΏ is the energy detection threshold. Using the results ofAppendix A, we obtain
Pout =Pa exp[β(1β πΌ)π](1 β Pf)[1β exp(β Ξ
π2π π
)]
+ Pa[1β exp(β(1β πΌ)π)](1 β Pf)
Γ [1β π2π π
π2πππΎπΞ + π2
π π
exp(β Ξ
π2π π
)]
+ (1β Pa)[1β exp(β(1β πΌ)π)](1 β Pd)
Γ [1β exp(β Ξ
π2π π
)]
+ (1β Pa) exp[β(1β πΌ)π](1 β Pd)
Γ [1β π2π π
π2πππΎπΞ + π2
π π
exp(β Ξ
π2π π
)]
+ PaPf + (1β Pa)Pd
(9)
ZOU et al.: OUTAGE PROBABILITY ANALYSIS OF COGNITIVE TRANSMISSIONS: IMPACT OF SPECTRUM SENSING OVERHEAD 2679
Pout = Pr
{(1β πΌ) log
(1 +
β£βπ π(π)β£2 ππ β£π½(π)β£2β£βππ(π)β£2 ππ β£π(π, 2)β£2 +π0
)< π
}(3)
Pout =Pr(οΏ½ΜοΏ½π (π) = π»0, π»π(π, 1) = π»0, π»π(π, 2) = π»0) Pr{β£βπ π(π)β£2 < Ξ}+ Pr(οΏ½ΜοΏ½π (π) = π»0, π»π(π, 1) = π»0, π»π(π, 2) = π»1) Pr{β£βπ π(π)β£2 β β£βππ(π)β£2πΎπΞ < Ξ}+ Pr(οΏ½ΜοΏ½π (π) = π»0, π»π(π, 1) = π»1, π»π(π, 2) = π»0) Pr{β£βπ π(π)β£2 < Ξ}+ Pr(οΏ½ΜοΏ½π (π) = π»0, π»π(π, 1) = π»1, π»π(π, 2) = π»1) Pr{β£βπ π(π)β£2 β β£βππ(π)β£2πΎπΞ < Ξ}+ Pr(οΏ½ΜοΏ½π (π) = π»1)
(4)
Pout =Pa exp[β(1β πΌ)π](1 β Pf) Pr{β£βπ π(π)β£2 < Ξ}+ Pa[1β exp(β(1β πΌ)π)](1 β Pf) Pr{β£βπ π(π)β£2 β β£βππ(π)β£2πΎπΞ < Ξ}+ (1 β Pa)[1 β exp(β(1 β πΌ)π)](1 β Pd) Pr{β£βπ π(π)β£2 < Ξ}+ (1 β Pa) exp[β(1β πΌ)π](1 β Pd) Pr{β£βπ π(π)β£2 β β£βππ(π)β£2πΎπΞ < Ξ}+ PaPf + (1 β Pa)Pd
(5)
where the false alarm probability Pf is constrained to thedetection probability Pd as given below
Pf =
β§β¨β©Pd, Pd = π(ββ
π)
Pd βπ(πβ1(Pd) +1
π2ππ π
) exp(π), otherwise
(10)where π = πΎππ
β1(Pd) +βππΎπ, π = πβ1(Pd)
π2psπ
+ 12π4
psπ 2 , and
the number of samples π should satisfy π β₯ [πβ1(Pd)]2. As
can be observed from (9), the derived closed-from expressionof outage probability accounts for both the probability ofno available channel detected in the spectrum hole detectionphase and the probability of channel outage occurred in thesubsequent data transmission phase, and thus we call it overalloutage probability. Following (9) and letting πΎπ β +β, weare able to obtain an overall outage probability floor
Pout,floor = PaPf + (1β Pa)Pd (11)
which can also be explained as follows. Once CS detects aspectrum hole (no matter it is a correct or a false detection)in the first phase, a channel outage would not occur in thesubsequent data transmission phase due to πΎπ β +β. Inother words, when πΎπ β +β, the outage occurs only whenCS detects the presence of PU (i.e., no hole is detected)no matter whether PU really presents or not. Combining (9)and (10), one can see that the overall outage probability ofthe cognitive transmission is a function of {Pa,Pd,Pf , π ,π2ππ , π
2ππ, π
2π π, πΎπ , πΎπ, πΌ}, where the data rate π may often be
set by the system in accordance with the QoS requirement andthe network environment, and the spectrum sensing overheadπΌ is a parameter that can be adapted to optimize the systemperformance. To obtain a general expression for an optimal πΌβ
as a function of the other parameters is very complicated andinfeasible. Here, we consider an asymptotic outage probabilityanalysis to decide the optimal value πΌβ. Considering the caseof Pa = 1 and π = 0 and letting πΎπ β β, we can obtain
from (9) using the Taylor approximation as
PoutβΌ= (1β Pf)
Ξ
π2π π
+ Pf (12)
Letting πΎπ β β, we can similarly apply the Taylor approxi-mation to (10) and obtain
PfβΌ= Pd β Pd(1 +
πβ1(Pd)
[πβ1(Pd) +βπ ]π2
ππ
β 1
πΎπ)
= β Pdπβ1(Pd)
[πβ1(Pd) +βπΌπππ ]π2
ππ
β 1
πΎπ
(13)
In obtaining the first equation of (13), we have ignoredthe term 1/(2π4
ππ π 2), since it is a higher-order infinitesimal
compared to the term πβ1(Pd)/(π2ππ π ) for πΎπ β β. Differ-
entiating (12) with respective to the spectrum sensing overheadπΌ yields
βPout
βπΌ= (1β Pf)
βΞ
π2π πβπΌ
+ (1 β Ξ
π2π π
)βPf
βπΌ
βΌ= βΞ
π2π πβπΌ
+βPf
βπΌ
(14)
where the second equation is obtained due to the fact thatPf and Ξ are infinitesimal when πΎπ, πΎπ β β. Meanwhile,βPf/βπΌ and βΞ/βπΌ are given by
βPf
βπΌ=
Pdπβ1(Pd)
βπππ
2πΎππ2ππ
βπΌ[πβ1(Pd) +
βπΌπππ ]2
(15)
andβΞ
βπΌ=
π 2π /(1βπΌ) ln 2
(1β π)2πΎπ (16)
By substituting βPf/βπΌ and βΞ/βπΌ from (15) and (16) into(14), the optimal spectrum sensing overhead should satisfy thefollowing equation
2π /(1βπΌβ)βπΌβ[βπΌβπππ +πβ1(Pd)]
2π + (1 β πΌβ)2π = 0(17)
2680 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 9, NO. 8, AUGUST 2010
TABLE ILIST OF THE OPTIMAL SPECTRUM SENSING OVERHEAD πΌβ UNDER THE
DIFFERENT PRIMARY AND SECONDARY TRANSMIT SIGNAL-TO-NOISERATIOS (πΎπ , πΎπ).
wherein π and π are given by
π = π2ππ πΎππ ln 4; π = π2
π ππΎπ βπππ Pdπ
β1(Pd) (18)
An optimal spectrum sensing overhead πΌβ can be easily deter-mined by using (17). In Table 1, we show the optimal sensingoverhead under different values of the primary and secondarytransmit powers (πΎπ , πΎπ) with Pd = 0.99, π2
ππ = π2π π = 1,
π = 1 bit/s/Hz, π = 25 ms and ππ = 100 kHz. As shownin the first two columns of Table 1, as the primary transmitpower πΎπ increases, the optimal spectrum sensing overheadπΌβ decreases, which is due to the fact that less time durationis required for the spectrum hole detection phase with anincreasing primary transmit power. From the last two columnsof Table 1, one can also see that the optimal spectrum sensingoverhead increases when the secondary transmit power πΎπ increases from πΎπ = 30 dB to πΎπ = 40 dB. This is becausethat as the secondary transmit power increases, less time isneeded for the data transmission phase, thus resulting in alonger time duration available for the spectrum sensing phase.
B. Numerical Results and Analysis
In this subsection, we focus on the numerical evaluations toshow the impact of spectrum sensing overhead on the overalloutage probability performance. Notice that the primary userwould be interfered by the cognitive user when CS does notdetect the presence of PU given that PU is active. Therefore,the probability of detection of the primary-user-presence Pd
shall be set to a required threshold by the cognitive systemto guarantee PUβs QoS. Throughout this paper, we will usePd = 0.99 for the numerical evaluations.
Fig. 2 depicts the probability of detection versus the prob-ability of false alarm using (10). It is shown from Fig. 2 that,under a required detection probability, as the spectrum sensingoverhead πΌ increases, the false alarm probability decreases,i.e., CS can detect the hole of licensed band more accurately.This result is easy to understand since increasing πΌ indicatesthe enhancement of the signal energy received at CS from PU,which thus leads to the detection performance improvement.
Fig. 3 plots (9) as a function of the transmit SNR atCS πΎπ for different spectrum sensing overhead values. Thecorresponding outage probability floors for the three cases(i.e., πΌ = 0.1, πΌ = 0.2 and πΌ = 0.5) are plotted using (8).Also, the simulated results are provided in this figure by usinga typical link-level simulation. As shown in Fig. 3, the curves
10β3
10β2
10β1
0.4
0.5
0.6
0.7
0.8
0.9
1
The probability of false alarm (Pf)
The
pro
babi
lity
of d
etec
tion
(Pd)
Ξ±=0.5
Ξ±=0.2
Ξ±=0.1
Fig. 2. The probability of detection versus the probability of false alarmfor different values of the spectrum sensing overhead πΌ with πΎπ = 10 dB,π = 20 ms, ππ = 100 kHz, and π2
ππ = 1.
0 5 10 15 20 25 300.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
0.7
Transmit SNR at CS (Ξ³s)
Ove
rall
outa
ge p
roba
bilit
y
Ξ±=0.1 theoretical
Ξ±=0.1 simulated
Ξ±=0.2 theoretical
Ξ±=0.2 simulated
Ξ±=0.4 theoretical
Ξ±=0.4 simulated
Outage probability floors for the three given cases.
Fig. 3. Overall outage probability versus the transmit SNR at CS πΎπ fordifferent values of the spectrum sensing overhead πΌ with Pa = 0.8, π = 0.1,Pd = 0.99, π = 0.5 b/s/Hz, πΎπ = 5 dB, π = 20 ms, ππ = 25 kHz,π2ππ = π2
ππ = 0.5, and π2π π = 1.
of the overall outage probability converge to the correspondingfloors in high πΎπ regions. The occurrence of outage floor isdue to the fact that when the transmit SNR πΎπ is very high,the overall outage probability is dominated by the probabilityof no spectrum hole detected that is independent from πΎπ .Moreover, the floors observed are reduced with the increaseof the overhead πΌ, which is resulted from the improvement ofthe detection probability of spectrum holes as the overheadπΌ increases. In addition, the simulation results match theanalytical results very well.
In Fig. 4, we show the overall outage probability versusthe spectrum sensing overhead πΌ for different values of thedata rate, where the performance curves plotted correspondto π = 0.4 b/s/Hz and π = 0.8 b/s/Hz, respectively. All thecurves plotted in Fig. 4 demonstrate that there always existsan optimal spectrum sensing overhead πΌβ, i.e., a minimized
ZOU et al.: OUTAGE PROBABILITY ANALYSIS OF COGNITIVE TRANSMISSIONS: IMPACT OF SPECTRUM SENSING OVERHEAD 2681
0 0.2 0.4 0.6 0.8 10.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Spectrum sensing overhead (Ξ±)
Ove
rall
outa
ge p
roba
bilit
y
R=0.8 b/s/HzR=0.4 b/s/Hz
Fig. 4. Overall outage probability versus the spectrum sensing overhead πΌfor different values of the data rate π with Pa = 0.8, π = 0.1, Pd = 0.99,πΎπ = 10 dB, πΎπ = 5 dB, π = 20 ms, ππ = 25 kHz, π2
ππ = π2ππ = 0.5,
and π2π π = 1.
Fig. 5. (a) Coexistence of a primary wireless network and a cognitive relaynetwork; (b) The allocation of time durations: Hole detection versus datatransmission.
outage probability can be achieved through a tradeoff indetermining the time durations for the hole detection andthe data transmission phases. It is noted that for any givenparameter set, an optimal spectrum sensing overhead πΌβ canbe determined by minimizing the overall outage probability asgiven by (9).
IV. COMBINED COGNITIVE RELAY AND COGNITIVE
TRANSMISSION
In this section, we consider a cognitive relay network asshown in Fig. 5 (a), where a cognitive relay (CR) assists CSfor the data transmission. Fig. 5 (b) illustrates the proposedcognitive relay transmission process, which is described indetail as follows. At the first phase of time slot 2π β 1, CSdetects the status of the licensed band. If a spectrum hole isdetected, CS transmits its signal to both CR and CD during
the second phase of the same time slot. Next, CR decodesits received signal from CS during the first phase of timeslot 2π and, meanwhile, detects whether or not there is aspectrum hole. If the signal is decoded successfully and a holeis detected, CR will notify CS and forward its decoded signalto CD during the next phase. Otherwise, CS will determinewhether or not to repeat its signal transmission directly to CDdepending on its hole detection result at the second phase oftime slot 2π.
As shown in Fig. 6, we present a block diagram ofthe general framework for implementation of the proposedcognitive relay transmission scheme. An original bit streamare processed in FED (forward error-detecting, e.g., a cyclicredundancy check (CRC) code) and FEC (forward error-correcting, e.g., Turbo code) encoders at time slot 2π β 1,giving a encoded bit stream that would be transmitted to bothCR and CD when a spectrum hole is detected through thehole detection module. At the next time slot 2π, CR performsFEC decoding and FED checking on the received bits. If theFED check is successful and a spectrum hole is detected, CRwould then transmit the decoded bits to CD. Otherwise, CSwould determine whether or not to repeat the transmissionof the original bits depending on its hole detection result attime slot 2π. Finally, CD combines the two copies of receivedsignals by using maximum ratio combining (MRC) method,and gives an estimated bit stream of the original bits after aFEC decoder.
Let π»π(2π β 1) and π»π(2π) denote the statuses oflicensed band at time slots 2π β 1 and 2π, respectively.Similarly, let οΏ½ΜοΏ½π (2π β 1) and οΏ½ΜοΏ½π(2π) be the decisionresults made by CS and CR, respectively, as to whetherthere is a spectrum hole. For simplicity, we assume herethat the primary traffic status does not change during onetime slot. Notice that following the procedures as stated inSection III, the analysis can be extended with additionalprimary traffic states to the scenario where the primary trafficstatus may change between the spectrum hole detection anddata transmission phases, for which similar performancecharacteristics can be obtained. Here, we use a Bernoullidistribution with parameter Pa to model the randomvariable π»π(2π β 1), i.e., Pr (π»π(2π β 1) = π»0) = Pa
and Pr (π»π(2π β 1) = π»1) = 1 β Pa. Moreover,a Markov chain with a transition probabilityPt = 1 β exp(βπ) is used to model the process ofprimary traffic arrival/departure in two adjacent timeslots, i.e., Pr (π»π(2π) = π»1β£π»π(2π β 1) = π»0) =Pr (π»π(2π) = π»0β£π»π(2π β 1) = π»1) = Pt
and Pr (π»π(2π) = π»0β£π»π(2π β 1) = π»0) =Pr (π»π(2π) = π»1β£π»π(2π β 1) = π»1) = 1 β Pt. Besides,each wireless link as depicted in Fig. 5 is modeled as aRayleigh block fading channel, i.e., the channel fading isinvariant during one whole block consisting of two time slots.
A. Signal Modeling and Outage Analysis
Following [16], [19] and considering for practicability, wewould adopt half-duplex relay to model and analyze thecognitive relay transmission scheme. At time slot 2πβ 1, the
2682 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 9, NO. 8, AUGUST 2010
Fig. 6. A block diagram of the general framework for implementation of the proposed cognitive relay transmission scheme.
CS signals received at CR and CD can be expressed as
π¦π(2π β 1) =βπ π(π)βππ π½(2π β 1)
+ βππ(π)βπππ(2π β 1, 2) + ππ(2π β 1)
(19)
and
π¦π(2π β 1) =βπ π(π)βππ π½(2π β 1)
+ βππ(π)βπππ(2π β 1, 2) + ππ(2π β 1)
(20)
where
π½(2π β 1) =
{π₯π (π), οΏ½ΜοΏ½π (2π β 1) = π»0
0, οΏ½ΜοΏ½π (2π β 1) = π»1
π(2π β 1, 2) =
{0, π»π(2π β 1) = π»0
π₯π(2π β 1, 2), π»π(2π β 1) = π»1
where π₯π(2πβ1, 2) is the transmit signal of PU at the secondphase of time slot 2πβ1. As depicted in Fig. 5 (b), there existtwo data transmission cases (dependent on whether or not CRcan decode successfully and detect a spectrum hole) at timeslot 2π, which are denoted by Ξ = 1 and Ξ = 2, respectively.If CR succeeds in decoding its received signal and detects aspectrum hole at time slot 2π, it can be described as (21) at thetop of the following page (in an information-theoretic sense[31]). where the factor 1/2 in front of log(β ) is resulted froma half-duplex relay constraint [16]. Then, at the next phase oftime slot 2π, CR would forward its correctly decoded resultοΏ½ΜοΏ½π (π) = π₯π (π) to CD. Thus, the signal received at CD can bewritten as
π¦π(2π,Ξ = 1) =βππ(π)βππ π₯π (π)
+ βππ(π)βπππ(2π, 2) + ππ(2π)
(22)
where
π(2π, 2) =
{0, π»π(2π) = π»0
π₯π(2π, 2), π»π(2π) = π»1
Note that π½(2π β 1) must be equal to π₯π (π) in the case ofCR succeeding in decoding the received signal; otherwise, CRwill not be able to decode successfully. By substituting thisresult into (20) and combining that with (22), CD achievesan enhanced signal version as given by (23), from which thesignal-to-interference-and-noise ratio (SINR) is calculated as
SINRd(Ξ = 1)
=β£βπ π(π)β£2 πΎπ + β£βππ(π)β£2 πΎπ
β£βππ(π)β£2 πΎπ β£π(2π β 1, 2)β£2 + β£βππ(π)β£2 πΎπ β£π(2π, 2)β£2 + 2(24)
If CR fails to decode its received signal or to detect a spectrumhole at time slot 2π, such an event can be described as (25). Inthis case, CS, instead of CR, would determine whether or notto repeat the transmission of signal π₯π (π) to CD depending onits hole detection result at time slot 2π. In the case of Ξ = 2,the signal received at CD from CS during the time slot 2π canbe written as
π¦π(2π,Ξ = 2) =βπ π(π)βππ π½(2π) + βππ(π)
βπππ(2π, 2)
+ ππ(2π)(26)
where π½(2π) is defined as
π½(2π) =
{π₯π (π), οΏ½ΜοΏ½π (2π) = π»0
0, οΏ½ΜοΏ½π (2π) = π»1
Combining (20) and (26) with MRC method, CD can achievean enhanced signal version as given by
π¦π(2π,Ξ = 2) = βπ π(π)βππ π½(2π β 1) + βπ π(π)
βππ π½(2π)
+ βππ(π)βπππ(2π β 1, 2) + βππ(π)
βπππ(2π, 2)
+ ππ(2π β 1) + ππ(2π)(27)
ZOU et al.: OUTAGE PROBABILITY ANALYSIS OF COGNITIVE TRANSMISSIONS: IMPACT OF SPECTRUM SENSING OVERHEAD 2683
Ξ = 1 :(1β πΌ)
2log
(1 +
β£βπ π(π)β£2 πΎπ β£π½(2π β 1)β£2β£βππ(π)β£2 πΎπ β£π(2π β 1, 2)β£2 + 1
)> π and οΏ½ΜοΏ½π(2π) = π»0 (21)
π¦π(2π,Ξ = 1) = βπ π(π)βππ π₯π (π)+βππ(π)
βππ π₯π (π)+βππ(π)
βπππ(2πβ1, 2)+βππ(π)
βπππ(2π, 2)+ππ(2πβ1)+ππ(2π)
(23)
Ξ = 2 :(1β πΌ)
2log
(1 +
β£βπ π(π)β£2 πΎπ β£π½(2π β 1)β£2β£βππ(π)β£2 πΎπ β£π(2π β 1, 2)β£2 + 1
)< π or οΏ½ΜοΏ½π(2π) = π»1 (25)
from which the corresponding SINR can be calculated as
SINRd(Ξ = 2)
=β£βπ π(π)β£2 πΎπ β£π½(2π β 1)β£2 + β£βπ π(π)β£2 πΎπ β£π½(2π)β£2
β£βππ(π)β£2 πΎπ β£π(2π β 1, 2)β£2 + β£βππ(π)β£2 πΎπ β£π(2π, 2)β£2 + 2(28)
Now, we have formulated the signal models for the cognitiverelay transmission, based on which a detailed analysis of theoverall outage probability is presented in the following, andalso a performance comparison with the non-relay transmis-sion is conducted to illustrate the advantage of the proposedcognitive relay scheme. According to the coding theorem [31],an outage event is deemed to occur when the channel capacityfalls below the data rate π . Thus, we can calculate the overalloutage probability of the cognitive relay transmission as
Pout,relay = Pr
{1β πΌ
2log (1 + SINRd) < π
}
= Pr
{1β πΌ
2log (1 + SINRd(Ξ = 1)) < π ,Ξ = 1
}
+ Pr
{1β πΌ
2log (1 + SINRd(Ξ = 2)) < π ,Ξ = 2
}
= Pr {SINRd(Ξ = 1) < πΎπ Ξ,Ξ = 1}+ Pr {SINRd(Ξ = 2) < πΎπ Ξ,Ξ = 2}
(29)
where Ξ = [22π /(1βπΌ) β 1]/πΎπ . Using the results fromAppendix B, we can obtain
Pout,relay = B2(a)+B2(b)+B2(c)+B2(d)+B12(a)βB12(b)(30)
Closed-form expressions of B2(a), B2(b), B2(c), B2(d),B12(a) and B12(b) are presented in Appendix B as givenby (B.5), (B.7), (B.8), (B.9), (B.13) and (B.14), respectively.As can be seen from (30), obtaining a general expression foran optimal spectrum sensing overhead πΌ as a function of theother parameters is very difficult. However, an optimal πΌ valuecan be determined through numerical computation.
B. Numerical Results and Analysis
Fig. 7 shows the overall outage probability versus thetransmit SNR πΎπ using (9) and (30), respectively, in whichthe simulated results are also given by employing a typicallink-level simulation approach. From Fig. 7, one can see thatin low SNR regions, the outage performance of the cognitiverelay scheme performs worse than the non-relay transmission.This is because that out of consideration for practicability,
0 5 10 15 20 25 300.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Transmit SNR at CS (Ξ³s)
Ove
rall
outa
ge p
roba
bilit
y
Nonβrelay transmission (theoretical)Nonβrelay transmission (simulated)Cognitive relay (theoretical)Cognitive relay (simulated)
Fig. 7. Outage performance versus the transmit SNR at CS πΎπ of the non-relay and the cognitive relay schemes with Pa = 0.8, π = 0.1, Pd,s =Pd,r = 0.99, πΌ = 0.2, π = 0.5 b/s/Hz, πΎπ = 5 dB, π = 20 ms, ππ =25 kHz, π2
ππ = π2ππ = π2
ππ = 0.2, π2π π = 1, and π2
π π = π2ππ = 1.2.
we adopt a half-duplex relay, rather than a full-duplex relay,which would degrade the system performance. However, inhigher SNR regions, the benefits achieved overtake the costsand the performance of cognitive relay transmission outper-forms the non-relay scheme. Note that in practical wirelesscommunication systems, the transmit SNR shall be requiredto meet a satisfactory QoS and thus overly low transmit SNRis not practical. Accordingly, the advantage of the cognitiverelay scheme is achieved in practical communication systems.It is worth mentioning that the overall outage probability floorof the cognitive relay scheme is reduced noticeably comparedwith the non-relay scheme, demonstrating the advantage ofthe cognitive relay scheme. Besides, one can see from Fig. 7that the simulation results match the theoretical results well.
Fig. 8 illustrates the overall outage probability for thecognitive relay transmission by plotting (30) as a functionof πΎπ for different spectrum sensing overhead πΌ values. Asshown in Fig. 8, the curves approach to their respective outageprobability floors in high SNR regions and, also, the floors godown with the increase of spectrum sensing overhead πΌ. Thisis due to the fact that at high πΎπ values, the outage probabilityis dominantly dependent on the detection probability that canbe improved as the overhead πΌ increases. Besides, one cansee from Fig. 8 that in lower SNR regions, the relationship
2684 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 9, NO. 8, AUGUST 2010
0 5 10 15 20 25 300.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Transmit SNR at CS (Ξ³s)
Ove
rall
outa
ge p
roba
bilit
y
Ξ± =0.1
Ξ± =0.2
Ξ± =0.5
Fig. 8. Overall outage probability versus the transmit SNR at CS πΎπ fordifferent values of the spectrum sensing overhead πΌ with Pa = 0.8, π =0.1, Pd,s = Pd,r = 0.99, π = 0.5 b/s/Hz, πΎπ = 5 dB, π = 20 ms,ππ = 25 kHz, π2
ππ = π2ππ = π2
ππ = 0.2, π2π π = 1, and π2
π π = π2ππ = 0.8.
0 0.2 0.4 0.6 0.8 10.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Spectrum sensing overhead (Ξ±)
Ove
rall
outa
ge p
roba
bilit
y
R=0.8 b/s/HzR=0.4 b/s/Hz
Fig. 9. Overall outage probability versus the spectrum sensing overhead πΌfor different values of the data rate π with Pa = 0.8, π = 0.1, Pd,s =Pd,r = 0.99, πΎπ = 10 dB, πΎπ = 5 dB, π = 20 ms, ππ = 25 kHz, π2
ππ =π2ππ = π2
ππ = 0.5, π2π π = 1.5, and π2
π π = π2ππ = 1.
between the overall outage probability and spectrum sensingoverhead πΌ is not simply increasing or decreasing. In Fig.9, we further show the overall outage probability versus thespectrum sensing overhead πΌ. As can be observed from Fig.9, the optimal values of πΌ corresponding to π = 0.4 b/s/Hzand π = 0.8 b/s/Hz are approximately equal to 0.3 and 0.15,respectively. It is pointed out that for any given parameter set,an optimal spectrum sensing overhead πΌβ can be determinedthrough numerical computation.
V. CONCLUSION
Cognitive radio has been proposed as an effective meansto promote the utilization of valuable wireless spectrum re-sources. In this paper, we have presented a comprehensiveanalysis for the cognitive transmission, where a cognitive
source first detects the available spectrum holes through spec-trum sensing and then transmits its data over the detectedholes. A closed-form expression of the overall outage proba-bility for cognitive transmissions has been obtained to showthe impact of spectrum sensing overhead. Numerical resultshave shown that minimum overall outage probability can beachieved through a tradeoff between the hole detection andthe data transmission phases.
Furthermore, we have proposed a cognitive relay transmis-sion scheme, in which a cognitive relay is used to assist a cog-nitive source for the data transmission in an adaptive manner.In particular, cognitive relay forwards its decoded result onlywhen it can decode the received signal successfully and candetect a spectrum hole; otherwise, the cognitive source, insteadof the cognitive relay, would determine whether or not torepeat the signal transmission depending on its hole detectionresult. Also, a closed-form expression of the overall outageprobability has been derived for the proposed cognitive relayscheme. Numerical evaluations have been conducted to showthe advantage of the cognitive relay transmission scheme.
APPENDIX ADERIVATION OF (9)
Considering the central limit theorem (CLT), for a largenumber π , random variable π (π¦) given π»π(π, 1) = π»0
follows a Gaussian distribution with mean π0 and varianceπ2
0 /π (see the proposition 1 in [25] for proof). Therefore,the false alarm probability Pf(π) at time slot π is obtainedfrom (7) as
Pf(π) βΌ=β« +β
πΏ
βπβ
2ππ0
exp[βπ(π₯βπ0)2
2π20
]ππ₯
= π
((πΏ
π0β 1)
βπ
) (A.1)
where π(β ) is defined as
π(π₯) =1β2π
β« +β
π₯
exp(βπ¦2
2)ππ¦ (A.2)
Without loss of generality, we consider that the primary signalfollows a complex symmetric Gaussian distribution, whicha typical OFDM (orthogonal frequency-division multiplex)modulated signal [32]. Note that we can similarly extend to theother modulation types, such as the BPSK, QPSK or 16QAMmodulated primary signal. According to the proposition 2 in[25], for a large number π , random variable π (π¦) givenπ»π(π, 1) = π»1 follows a Gaussian distribution with mean(β£βππ (π)β£2πΎπ + 1)π0 and variance (β£βππ (π)β£2πΎπ + 1)2π2
0 /π ,where βππ (π) is a fading coefficient of the channel from PUto CS at time slot π. Hence, the detection probability Pd(π)at time slot π is calculated from (8) as
Pd(π) = π
(πΏβπ
π0(β£βππ (π)β£2πΎπ + 1)ββπ
)(A.3)
Given a target detection probability Pd and fading coefficientβππ (π), the instantaneous false alarm probability Pf(π) iscalculated from (A.1) and (A.3) as
Pf(π) = π(π β£βππ (π)β£2 +πβ1(Pd)
)(A.4)
ZOU et al.: OUTAGE PROBABILITY ANALYSIS OF COGNITIVE TRANSMISSIONS: IMPACT OF SPECTRUM SENSING OVERHEAD 2685
where π = πΎππβ1(Pd) +
βππΎπ and πβ1(β ) is an inverse
π(β ) function. Notice that RV π = β£βππ (π)β£2 follows anexponential distribution with parameter 1/π2
ππ . Therefore, theaverage false alarm probability Pf can be calculated as
Pf =
β« β
0
π(π π₯+πβ1(Pd)
) 1
π2ππ
exp(β π₯
π2ππ
)ππ₯
=
β«β«Ξ
1
π2ππ
exp(β π₯
π2ππ
)1β2π
exp(βπ¦2
2)ππ₯ππ¦
(A.5)
where Ξ ={(π₯, π¦)β£0 < π₯ < β, π π₯+πβ1(Pd) < π¦ < β}.
Integrating (A.5) first with respect to π₯, then with respect toπ¦, we obtain
Pf =
β§β¨β©
Pd + [1βπ(πβ1(Pd) +1
π2ππ π
)] exp(π), π(ββπ) < Pd β€ 1
Pd, Pd = π(ββπ)
Pd βπ(πβ1(Pd) +1
π2ππ π
) exp(π), 0 β€ Pd < π(ββπ)
(A.6)
where π = πβ1(Pd)π2psπ
+ 12π4
psπ 2 . From detection theory, for any
reasonable detector, Pf is always smaller than or equal to Pd,or else it is worse than tossing a coin. Therefore, the numberof samples π should satisfy
π β₯ [πβ1(Pd)]2 (A.7)
which is due to the fact that from central limit theorem, thenumber of samples should be sufficiently large so that the out-put statistic π (π¦) of the energy detector can be approximatedto a Gaussian distribution. Combining (A.6) and (A.7) yields
Pf =
β§β¨β©Pd, Pd = π(ββ
π)
Pd βπ(πβ1(Pd) +1
π2ππ π
) exp(π), otherwise
(A.8)Note that RVs β£βπ π(π)β£2 and β£βππ(π)β£2 follow the exponentialdistribution with parameters 1/π2
π π and 1/π2ππ, respectively,
and are independent of each other. Thus, the probabilityintegrals given in (5) can be calculated as
Pr{β£βπ π(π)β£2 < Ξ
}= 1β exp(β Ξ
π2π π
) (A.9)
and
Pr{β£βπ π(π)β£2 β β£βππ(π)β£2 πΎπΞ < Ξ
}=
β«β«Ξ©
1
π2π π
exp(β π₯
π2π π
)1
π2ππ
exp(β π¦
π2ππ
)ππ₯ππ¦(A.10)
where Ξ© = {(π₯, π¦)β£π₯β π¦πΎπΞ < Ξ}. Eq. (A.10) can be derivedas
Pr{β£βπ π(π)β£2 β β£βππ(π)β£2 πΎπΞ < Ξ
}= 1β π2
π π
π2πππΎπΞ + π2
π π
exp(β Ξ
π2π π
)(A.11)
Substituting (A.9) and (A.11) into (5) yields (9).
APPENDIX BCALCULATION OF (17)
The term Pr {SINRd(Ξ = 1) < πΎπ Ξ,Ξ = 1} given in (29)can be rewritten as (B.1) at the top of the following page.By using conditional probabilities, (B.1) can be calculated as
(B.2), where Pf,s and Pd,s are, respectively, the probability offalse alarm and the probability of detection of the primary-user-presence by CS and, moreover, Pf,r and Pd,r are thecorresponding probabilities of primary user detection by CR.Similar to (A.9), it is easy to show
Pf,s =
β§β¨β©Pd,s, Pd,s = π(ββ
π)
Pd,s βπ(πβ1(Pd,s) +1
π2ππ π π
) exp(ππ ), otherwise
(B.3)and
Pf,r =
β§β¨β©Pd,r, Pd,r = π(ββ
π)
Pd,r βπ(πβ1(Pd,r) +1
π2ππ π π
) exp(ππ), otherwise
(B.4)where π π = πΎππ
β1(Pd,s) +βππΎπ, ππ =
πβ1(Pd,s)π2ππ π π
+
12π4
ππ π 2π , π π = πΎππ
β1(Pd,r) +βππΎπ ππ =
πβ1(Pd,r)π2πππ π
+1
2π4πππ
2π, and the number of samples satisfies π β₯
max{[Qβ1(Pd,s)]2, [Qβ1(Pd,r)]
2}. Clearly, the first term,B2(a), as given in (B.2) is given by
B2(a) = Pa(1βPt)(1 β Pf,s)(1βPf,r)exp(β Ξ
π2π π
)π (B.5)
where parameter π is given by
π =
β§β¨β©
1β (1 +2Ξ
π2π π
) exp(β 2Ξ
π2π π
), π2π π = π2
ππ
1β π2π π
π2π π β π2
ππ
exp(β 2Ξ
π2π π
)
β π2ππ
π2ππ β π2
π π
exp(β 2Ξ
π2ππ
), otherwise
(B.6)
Also, the second term, B2(b), in (B.2) is calculated as
B2(b) =Pt(1β Pd,r)
(1β Pt)(1 β Pf,r)B2(a)
+ PaPt(1β Pf,s)(1 β Pd,r)exp(β Ξ
π2π π
)π
(B.7)
where B2(a) is given in (B.5) and the parameter π is givenby
π =
β§β¨β©
K
Hπ π
(K
Hπ π+
2Ξ
π2π π
)exp(β 2Ξ
π2π π
), π2π π = π2
ππ
π2π πK
(π2π π β π2
ππ)Hπ πexp(β 2Ξ
π2π π
)
+π2ππK
(π2ππ β π2
π π)Hππexp(β 2Ξ
π2ππ
), otherwise
where Ξ = [22π /(1βπΌ)β1]/πΎπ , K = π2πππΎπΞ, Hπ π = K+π2
π π
and Hππ = K+ π2ππ. Similarly, the third term, B2(c), in (B.2)
is calculated as
B2(c) =(1β Pa)(1β Pd,s)(1β Pf,r)π
2π π
Pa(1β Pf,s)(1β Pd,r)(π2π π + π2
πππΎπΞ)Γ B2(b)
(B.8)where B2(b) is given in (B.7). Besides, the fourth term, B2(d),as given in (B.2) is calculated as
B2(d) = (1β Pa)(1β Pt)(1β Pd,s)(1β Pd,r)
Γ Pr{β£βπ π(π)β£2 β β£βππ(π)β£2 πΎπΞ > Ξ
} β β β β β β β β β β β β β β β B9(I)Γ Pr
{β£βπ π(π)β£2 + β£βππ(π)β£2 β 2 β£βππ(π)β£2 πΎπΞ < 2Ξ} β β β B9(II)
(B.9)
2686 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 9, NO. 8, AUGUST 2010
Pr {SINRd(Ξ = 1) < πΎπ Ξ,Ξ = 1}= Pr {SINRd(Ξ = 1) < πΎπ Ξ,Ξ = 1, π»π(2π β 1) = π»0, π»π(2π) = π»0}+ Pr {SINRd(Ξ = 1) < πΎπ Ξ,Ξ = 1, π»π(2π β 1) = π»0, π»π(2π) = π»1}+ Pr {SINRd(Ξ = 1) < πΎπ Ξ,Ξ = 1, π»π(2π β 1) = π»1, π»π(2π) = π»0}+ Pr {SINRd(Ξ = 1) < πΎπ Ξ,Ξ = 1, π»π(2π β 1) = π»1, π»π(2π) = π»1}
(B.1)
Pr {SINRd(Ξ = 1) < πΎπ Ξ,Ξ = 1}= Pa(1β Pt)(1β Pf,s)(1 β Pf,r) Pr
{β£βπ π(π)β£2 + β£βππ(π)β£2 < 2Ξ, β£βπ π(π)β£2 > Ξ
}+ PaPt(1 β Pf,s)(1β Pd,r) Pr
{β£βπ π(π)β£2 + β£βππ(π)β£2 β β£βππ(π)β£2 πΎπΞ < 2Ξ, β£βπ π(π)β£2 > Ξ
}+ (1β Pa)Pt(1β Pd,s)(1β Pf,r) Pr
{ β£βπ π(π)β£2 + β£βππ(π)β£2 β β£βππ(π)β£2 πΎπΞ < 2Ξ,
β£βπ π(π)β£2 β β£βππ(π)β£2 πΎπΞ > Ξ
}
+ (1β Pa)(1 β Pt)(1 β Pd,s)(1 β Pd,r) Pr
{ β£βπ π(π)β£2 + β£βππ(π)β£2 β 2 β£βππ(π)β£2 πΎπΞ < 2Ξ,
β£βπ π(π)β£2 β β£βππ(π)β£2 πΎπΞ > Ξ
}(B.2)
where B9(I) is calculated as
B9(I) =π2π π
π2πππΎπΞ+ π2
π π
exp(β Ξ
π2π π
) (B.10)
Furthermore, B9(II) can be derived as
B9(II) = π + π (B.11)
where the parameter π is given by (B.6) and π is
π =
β§β¨β©
KΜ
HΜπ π
(KΜ
HΜπ π
+2Ξ
π2π π
)exp(β 2Ξ
π2π π
), π2π π = π2
ππ
π2π πKΜ
(π2π π β π2
ππ)HΜπ π
exp(β 2Ξ
π2π π
)
+π2ππKΜ
(π2ππ β π2
π π)HΜππ
exp(β 2Ξ
π2ππ
), otherwise
where KΜ = 2π2πππΎπΞ, HΜπ π = KΜ + π2
π π and HΜππ = KΜ + π2ππ.
According to the definition of the case Ξ = 2 as given in (25),we have
Pr {SINRd(Ξ = 2) < πΎπ Ξ,Ξ = 2}= Pr {SINRd(Ξ = 2) < πΎπ Ξ} β β β β β β β β β B12(a)β Pr {SINRd(Ξ = 2) < πΎπ Ξ,Ξ = 1} β β β B12(b)
(B.12)
Using conditional probabilities, the terms B12(a) andB12(b) can be calculated as (B.13) and (B.14), re-spectively, at the top of the following page. and Notethat we can easily obtain the closed-form expressionsof all the probability integrals as given in (B.13) and(B.14). We have now derived the closed-form expres-sions for the terms Pr {SINRd(Ξ = 1) <πΎπ Ξ,Ξ = 1} andPr {SINRd(Ξ = 2) <πΎπ Ξ,Ξ = 2} as given in (B.2) and(B.12), respectively.
ACKNOWLEDGMENT
The authors are grateful to the anonymous reviewers fortheir valuable comments and constructive suggestions thathave indeed helped improve this paper significantly.
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ZOU et al.: OUTAGE PROBABILITY ANALYSIS OF COGNITIVE TRANSMISSIONS: IMPACT OF SPECTRUM SENSING OVERHEAD 2687
B12(a) =Pa(1β Pt)(1 β Pf,s)2 Pr
{β£βπ π(π)β£2 < Ξ
}+ (1 β Pa)(1 β Pt)(1 β Pd,s)
2 Pr{β£βπ π(π)β£2 β β£βππ(π)β£2 πΎπΞ < Ξ
}+ Pt(1β Pf,s)(1 β Pd,s) Pr
{2 β£βπ π(π)β£2 β β£βππ(π)β£2 πΎπΞ < 2Ξ
}+ 2Pa(1β Pt)(1 β Pf,s)Pf,sPr
{β£βπ π(π)β£2 < 2Ξ
}+ 2(1β Pa)(1β Pt)(1 β Pd,s)Pd,sPr
{β£βπ π(π)β£2 β 2 β£βππ(π)β£2 πΎπΞ < 2Ξ
}+ Pt(Pd,s + Pf,sβ2Pf,sPd,s) Pr
{β£βπ π(π)β£2 β β£βππ(π)β£2 πΎπΞ < 2Ξ
}+ Pa(1β Pt)P
2f,s + PtPd,sPf,s + (1β Pa)(1β Pt)P
2d,s
(B.13)
B12(b) =Pa(1 β Pt)(1 β Pf,s)2(1β Pf,r) Pr
{β£βπ π(π)β£2 < Ξ
}Pr{β£βπ π(π)β£2 > Ξ
}+ PaPt(1 β Pf,s)(1β Pd,s)(1β Pd,r) Pr
{2 β£βπ π(π)β£2 β β£βππ(π)β£2 πΎπΞ < 2Ξ
}Γ Pr
{β£βπ π(π)β£2 > Ξ
}+ (1β Pa)Pt(1β Pf,s)(1 β Pd,s)(1 β Pf,r) Pr
{2 β£βπ π(π)β£2 β β£βππ(π)β£2 πΎπΞ < 2Ξ
}Γ Pr
{β£βπ π(π)β£2 β β£βππ(π)β£2 πΎπΞ > Ξ
}+ (1β Pa)(1 β Pt)(1 β Pd,s)
2(1β Pd,r) Pr{β£βπ π(π)β£2 β β£βππ(π)β£2 πΎπΞ < Ξ
}Γ Pr
{β£βπ π(π)β£2 β β£βππ(π)β£2 πΎπΞ>Ξ
}+ Pa(1β Pt)(1β Pf,s)Pf,s(1 β Pf,r) Pr
{β£βπ π(π)β£2 < 2Ξ
}Pr{β£βπ π(π)β£2 > Ξ
}+ PaPt(1 β Pf,s)Pd,s(1β Pd,r) Pr
{β£βπ π(π)β£2 β β£βππ(π)β£2 πΎπΞ < 2Ξ
}Γ Pr
{β£βπ π(π)β£2 > Ξ
}+ (1β Pa)Pt(1β Pd,s)Pf,s(1β Pf,r) Pr
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}Γ Pr
{β£βπ π(π)β£2 β β£βππ(π)β£2 πΎπΞ>Ξ
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(B.14)
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2688 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 9, NO. 8, AUGUST 2010
Yulong Zou received his B.Eng. degree (with thehighest honor) in Information Engineering fromNanjing University of Posts and Telecommunica-tions (NUPT), Nanjing China, in 2006. He is cur-rently working toward his Ph.D. degrees, respec-tively, at the Institute of Signal Processing andTransmission of NUPT, Nanjing China, and theElectrical and Computer Engineering Departmentof Stevens Institute of Technology (SIT), NJ USA.His research interests span the broad area of scal-able wireless communication and networking, with
emphasis on cooperative relay technology. Recently, he focuses on theinvestigation of cooperative relay techniques in cognitive radio networks.
Yu-Dong Yao (Sβ88-Mβ88-SMβ94) has been withStevens Institute of Technology, Hoboken, New Jer-sey, since 2000 and is currently a professor anddepartment director of electrical and computer en-gineering. He is also a director of Stevensβ Wire-less Information Systems Engineering Laboratory(WISELAB). Previously, from 1989 and 1990, hewas at Carleton University, Ottawa, Canada, as aResearch Associate working on mobile radio com-munications. From 1990 to 1994, he was with SparAerospace Ltd., Montreal, Canada, where he was
involved in research on satellite communications. From 1994 to 2000, he waswith Qualcomm Inc., San Diego, CA, where he participated in research anddevelopment in wireless code-division multiple-access (CDMA) systems.
He holds one Chinese patent and twelve U.S. patents. His researchinterests include wireless communications and networks, spread spectrumand CDMA, antenna arrays and beamforming, cognitive and software definedradio (CSDR), and digital signal processing for wireless systems. Dr. Yao wasan Associate Editor of IEEE Communications Letters and IEEE Transactionson Vehicular Technology, and an Editor for IEEE Transactions on WirelessCommunications. He received the B.Eng. and M.Eng. degrees from NanjingUniversity of Posts and Telecommunications, Nanjing, China, in 1982 and1985, respectively, and the Ph.D. degree from Southeast University, Nanjing,China, in 1988, all in electrical engineering.
Baoyu Zheng received B.S. and M.S. degreesfrom the Department of Circuit and Signal System,NUPT, in 1969 and 1981, respectively. Since then,he has been engaged in teaching and researching atSignal and Information Processing. He is a full pro-fessor and doctoral advisor at NUPT. His researchinterests span the broad area of the intelligent signalprocessing, wireless network and signal processingfor modern communication, and the quantum signalprocessing.