An In-band spec- trum sensing ... [3] will be constructed for spectrum management and future spectrum ... Actually, correlation among the samples of s(n), e.g. due to multipath ..... 0120-00-0000 Samsung SSS-Based Channel Sensing.doc.
Spectrum Sensing based on Linear Self-signal Elimination for Opportunistic Spectrum Access Dong Chen, Jiandong Li, Jing Ma Broadband Wireless Communications Laboratory, Information Science Institute State Key Laboratory of Integrated Service Networks, Xidian University, Xian 710071, China Email: {dchen,jdli,jingma}@mail.xidian.edu.cn
Abstract—We discuss the spectrum sensing problem which is crucial in opportunistic spectrum access. An In-band spectrum sensing method based on linear self-signal elimination is proposed. In this method, quiet period is not needed so that transmission performance can be improved without the interruption of on-going transmission. According to the AR model of the wireless fading channel, the adjacent symbols are linear weighted to eliminate the self-signal. Further the optimal detector under Neyman-Pearson criterion is used on the residual signal after self-signal elimination to provide better detection performance. Finally, performance is evaluated with simulations which show that our method can provide better performance in the IEEE802.22 environment.
I. I NTRODUCTION Opportunistic spectrum access (OSA), which is a hot research field in Cognitive radio (CR) [1], has been paid a lot of attentions since the spectrum resources are becoming scarcer with the development of wireless communication. The main purpose of opportunistic spectrum access is to reuse the temporary vacant spectrum in time domain and space domain which will dramatically enhance spectrum efficiency. In opportunistic spectrum access, the service of the licensed users (referred to as primary users) must be well protected. During this realization of secondary spectrum access, spectrum sensing should be continuously performed to avoid harmful interference with primary users. As a result, sensing problem in the detection of the primary user’s activity should be solved in advance. According to the purpose, there are two types of spectrum sensing [2], Out-of-band sensing and In-band sensing. Outof-band sensing takes charge of the detection of spectrum besides current channel. From the collected information of spectrum usage from Out-of-band sensing, a spectrum pool [3] will be constructed for spectrum management and future spectrum utilization. Once the activity of primary users in current channel presents, In-band sensing should detect such an event with a high probability and the system will switch to another idle channel selected from the spectrum pool in finite seconds. In-band sensing brings more challenges. The interference from the on-going transmission of the secondary users themselves (also called self-signal) during In-band sensing makes things complicated. The traditional thinking is the requirement for quiet period(QP) [2] to cease transmission during In-band sensing. Quiet period can be arranged in the
978-1-4244-2644-7/08/$25.00 © 2008 IEEE
following two ways [2]:Periodically/opportunistically scheduling quiet periods and Dynamic Hopping. It is an effectual way to give better In-band sensing performance. With quiet period, various methods for spectrum sensing such as energy detection methods and feature detection methods can be applied. However, quiet period will bring a lot of overheads and cause frequent interruption to the on-going transmissions. Several approaches to enhance the inefficiency in quietperiod based sensing is proposed by researchers recently. In IEEE802.22 [2], Baowei Ji proposed an In-band sensing algorithm based on Self-Signal Suppression (SSS) [4]. The received signal is reconstructed at the receiver according to the estimation of channel and the demodulated signals. Then minus the reconstructed signal from the original received signal to eliminate the effect of interference from the network itself. Another way to perform In-band sensing without quiet period is proposed by Linjun Lv [5]. The interference from the network itself is suppressed with the advantage of orthogonality of the preambles and adjacent OFDM symbols. In [6], Complementary Symbol Couples (CSC) in OFDM are used to filter out secondary signals. The proposed scheme measures the power of residual signal at several subcarrier positions and compares it with the predefined threshold to determine the existence of primary user. In this paper, we exploit the spectrum sensing with selfsignal elimination to improve the performance of In-band detection without quiet period. The requirement of spectrum sensing in cognitive radio is discussed. In section III, an Inband spectrum sensing method based on linear self-signal elimination is proposed. In this method, quiet period is not needed so that transmission performance can be improved without the interruption of on-going transmission. According to the AR model of the wireless fading channel, the adjacent symbols are linear weighted to eliminate the self-signal. Further the optimal detector under Neyman-Pearson criterion is used on the residual signal after self-signal elimination to provide better detection performance. Finally, performance is evaluated in section IV with simulations which show that our method can provide better performance in the IEEE802.22 environment. II. S YSTEM M ODEL In the traditional spectrum sensing with quiet period, the received signal at the secondary user will not consist the self-
signal. When primary signal appears, the recieved signal y(n) is formulated as
Generally, the smaller r (n) is, the better sensing performance can be achieved.
y (n) = As (n) + z (n)
A. Linear Self-signal Elimination Considering the memory effect imported by the channel correlation, the fading channel is modeled as a first-order AR model [10] shown as √ √ (5) h (n + k) = αh (n) + 1 − αw (n + k)
(1)
where n indicates the nth sample. Let s(n) denote the primary signal and A is its corresponding channel gain which is assumed to be constant during the detection. The primary signal is further corrupted by the zero-mean additive complex white Gaussian noise z(n). The primary signal can be known signal such as pilots, or unknown signal which is an aggregation of multiple primary signals. For the latter case, we assume that s(n) follow an zero-mean complex Gaussian distribution with variance σs2 . The samples of s(n) is said to be independent. Actually, correlation among the samples of s(n), e.g. due to multipath channel memory effect, will only improve the sensing performance [7]. Signal from primary users As(n) Secondary transmitter
x(n)
w(n) y(n)
Channel h(n)
Fig. 1.
Noise
Secondary receiver
System Model
Considering a pair of secondary transceivers in Fig.1, the received signal y(n) in (1) will be further corrupted by the self-signal when no quiet period is used during the detection. y (n) = h(n)x(n) + As (n) + z (n)
(2)
where h(n)x(n) is the self-signal and h(n) is the channel gain. To protect those primary hidden terminals, the secondary user should have the ability to detect very low primary signal(SNR = -22dB in IEEE802.22 [8], [9]). So in such a situation, the secondary users’ transmission is decodable. According to the appearance of primary signal, the received signal in (2) can be expressed in the following two test hypotheses H0 : y (n) = h (n) x (n) + z (n) H1 : y (n) = h (n) x (n) + As (n) + z (n)
(3)
where H0 denotes the absence of primary signal and H1 denotes the appearance. III. I N - BAND S PECTRUM S ENSING WITH L INEAR S ELF - SIGNAL E LIMINATION The primary signal intensity is much lower than the secondary signals. Obviously, the existence of secondary signal will have negative effect on In-band spectrum sensing. The self-signal is firstly eliminated before detection which will make the test statistic more clean. After self-signal elimination, the test hypotheses in (3) can be rewritten in the following form H0 : y (n) = r (n) + z (n) H1 : y (n) = r (n) + As (n) + z (n)
(4)
where h(n) and W (n + k) are independent identically distributed complex Gaussian random variables. And w(n + k) is independent from sample to sample. According to the Jakes model [11], 2 (6) α = J0 (2πkf ) where J0 (·) is the zero-order Bessel function of the first kind. f = fd Ts , fd is the maximum Doppler frequency in the fading environment, and Ts is the sampling period. From (5), the smaller is the distance between samples, the greater is the correlation. Based on this knowledge, (7) gives the expression of the proposed linear weighted self-signal elimination. 1 (7) y (n) = (ay (n − 1) + y (n) + by (n + 1)) c Notice that the z(n − 1), z(n) and z(n + 1) are independent between each other, the above equation can be written in probability that ay (n − 1) + y (n) + by (n + 1) ⇒ (ah (n − 1) x (n − 1) + h (n) x (n) + bh (n + 1) x (n + 1)) +A (as (n − 1) + s (n) + bs (n + 1)) + |a|2 + |b|2 + 1z (n) (8) 2 2 Let c = |a| + |b| + 1. Comparing with (4), we have 1 ah(n − 1)x(n − 1) + h(n)x(n) r(n) = |a|2 + |b|2 + 1 +bh(n + 1)x(n + 1) (9) 1 (as (n − 1) + s (n) + bs (n + 1)) (10) c To minimize the variance of r (n), one should solve equations with two elements after substituting (5) into (9). The solution is too complex to be given. Here we show a suboptimal solution in the condition that α ≈ 1. √ √ ( α1 + α2 )x(n) (11) a= − x(n − 1) x(n) b = (12) x(n − 1) s (n) =
2
2
where α1 = J0 (2πf ) and α2 = J0 (4πf ) . Then the final expression of linear weighted self-signal elimination is 1 × (13) y (n) = √ √ ( α1 + α2 )x(n) 2 x(n) 2 + x(n−1) + 1 x(n−1) √ √ ( α1 + α2 )x(n) x(n) y (n − 1) + y (n) + y (n + 1) − x(n − 1) x(n − 1)
YH K2 S can be viewed as a generalized matched filter [15]. Its performance is γ ) (20) PF = Q( T S K2 S/2 PD = Q Q−1 (PF ) − A 2ST K2 S (21)
B. Optimal Detector asd fasd fasdsdf Rewrite (4) into the vector form, H0 : Y = R + Z H1 : Y = R + AS + Z
(14)
where γ is the detection threshold. For a Constant False Alarm Rate(CFAR) detector, the threshold can be determined according to (20). As Q(·), the standard Gaussian complementary CDF, is a monotone decreasing function, the detection performance improves with the increase of YH K2 S as shown in (21). Although in some cases, the parameter A can be found with actual measurements, the actual value of A can not be learned to infinite precision due to fading in the realworld situation. In the shadow fading, A is said to follow a lognormal distribution with mean μA and variance 2 (σA = 5.5dB for DTV signals at 615MHz) [13]. So σA we have the average detection performance shown as
where Y = [y (1) , · · · , y (N )] , R = [r (1) , · · · , r(N )] , T T Z = [z (1) , · · · , z(N )] and S = [s (1) , · · · , s (N )] . The covariance matrix of Z is CZ = σz2 I. And we assume the coT variance matrix of H (X) = [h (1) x (1) , . . . , h (N ) x (N )] is CX . We can calculate CR according to the relationship in (9). In an ideal situation in which channel keeps the same between adjacent samples, the self-signal can be completely eliminated. Based on the test hypotheses in (14), we will further discuss the detection after self-signal elimination. Under NeymanPearson criterion [12], the performance is evaluated with two conditional probabilities, probability of false alarm PF and probability of detection PD . The optimal decision rule is given by the following Likelihood Ratio Test(LRT). T
Λ(Y) =
T
f (Y|H1 ) ≷ η f (Y|H0 ) H0 H1
det (CR + CZ ) exp YH K1 Y (16) det (CR + CS + CZ ) −1
−1
where K1 = (CR + CZ ) − (CR + CS + CZ ) . Take the natural logarithm on both sides of (15) and rearrange the equation, the LRT becomes H1 det (CR + CS + CZ ) H Y K1 Y ≷ ln η + ln (17) det (CR + CZ ) H0
•
As YH Y indicates the energy of Y, we view YH KY as a weighted signal energy similarly. However, it performance is not easy to analyze. Known s(n)
Λ (Y) = exp 2AYH K2 S − A2 SH K2 S
(18)
−1
where K2 = (CR + CZ ) . Take the natural logarithm on both sides of (15) and rearrange the equation, the LRT becomes
H1 1 ln η + ASH K2 S Re Y K2 S ≷ H0 2A
H
=
(15)
The quantity on the left-hand side is the likelihood ratio, and on the right-hand side η is the optimum threshold for given probability of false alarm PF . According to the awareness of s(n), we have the following two situations. • Unknown s(n) After fading channel, s(n) is thought to be complex Gaussian random variables. The covariance matrix of AS is CS . According to the assumption in section II, CS = σs2 I. Then Λ (Y) =
+∞ average PD
Q Q−1 (PF ) − A 2ST K2 S f (A) dA
−∞
where f (A) =
⎧ ⎨
√ 1 2πσA
⎩0
(22) μA )2 exp − (ln A−ln 2 2σ
,A ≥ 0 , otherwise (23)
C. Algorithm Summary The main idea and analysis of our proposed algorithm are discussed in above sections. The procedure of spectrum sensing based on linear self-signal elimination is summarized as 1) Receive N + 2 continuous samples, y (0) , y (1) , . . ., y (N + 1). 2) Eliminate the self-signal with equation (13) to get Y = T [y (1) , y (2) , · · · , y (N )] . 3) If primary signal s(n) is unknown, detection statistics H K1 Y; Otherwise, detection statistics is is T (Y) = Y H T (Y) = Re Y K2 S . 4) Compare T (Y) with detection γ, if T (Y) > γ, the activity of primary user is said to be present, otherwise not present. IV. P ERFORMANCE E VALUATION In this section, we will evaluate the proposed algorithm with computer simulation. The channel parameters are shown in Table I which is a channel model define in IEEE802.22 [14]. The sample rate is 6 × 8/7MHz. For convenience, we define SNR = and
(19)
A
INR =
Secondary user signal Noise Primary user signal Noise
TABLE I C HANNEL PARAMETER Path
1
2
3
4
5
6
Excess delay(μsec) Relative amplitude(dB) Doppler frequency(Hz)
0 0 0
3 -7 0.10
8 -15 2.5
11 -22 0.13
13 -24 0.17
21 -19 0.37
sensing performance. Both our methods work well, while the method with unknown s(n) is a little better than the method in [6]. The probability of detection is greater than 0.8, when INR = −16dB and PF = 0.1. The sensing requirement can be further satisfied by increasing the samples number. 1
Samples number in the simulation is 8194. And the following results are obtained with Monte Carlo simulations of 10000 times.
0.9 0.8
A. Effects of Channel Correlation PD
0.7
The residual signal after self-signal elimination is related to the channel correlation α defined in (6). Fig.2 shows the detection performance under different α of CFAR detection. In the simulation, INR = −16dB, SNR = 10dB. And PF is kept to 0.1. Meanwhile, we also plot the curves of the method proposed in [6] for comparison. In Fig.2, the detection performance worsen greatly with the decrease of α. And both two detection methods proposed overcome the performance of method in [6]. When s(n) is known, the performance is better. From this results, we know that our methods is more suitable for fast fading channel compared with method in [6].
0.6 0.5 Our Method, unknown s(n),INR = −16dB, SNR = 10dB Our Method, unknown s(n),INR = −18dB, SNR = 10dB Our Method, known s(n),INR = −16dB, SNR = 10dB Our Method, known s(n),INR = −18dB, SNR = 10dB Method in [6],INR = −16dB, SNR = 10dB Method in [6],INR = −18dB, SNR = 10dB
0.4 0.3 0.2
0
0.1
0.2
Fig. 3.
0.3
0.4 PF
0.5
0.6
0.7
0.8
ROCs comparison under different INRs
INR = −16dB, SNR = 10dB, Samples Number = 8294 1 0.9
C. Receiver operating characteristics under different SNRs
0.8
The residual signal is also related to the self-signal itself. Fig.4 gives the ROC curves comparison between different SNRs. We omit the curves of the method in [6] to give better vision. Obviously, the performance under 10dB and 20dB are very similar which means that SNR has little effect on the detection. The conclusion is that our method is a robust sensing method without quiet period.
Probability
0.7 0.6 Our Method, unknown s(n), PD
0.5
Our Method, unknown s(n), PF
0.4
Our Method, known s(n), PD
0.3
Our Method, known s(n), PF
0.2
Method in [6], PD 1
Method in [6], PF
0.1
0.95 0.8
Fig. 2.
0.85 0.9 Channel Correlation α
0.95
0.9
1
0.85
α effects on detection performance
We notice that α in (6) is determined by the Doppler frequency. In the environment of IEEE802.22 in which the Doppler frequency is much small as shown in Table I, our algorithm can provide good performance. In the following simulation, we will have the detection in the environment of IEEE802.22 discussed. B. Receiver operating characteristics under different INRs Receiver operating characteristics(ROC) curve is usually used in the performance evaluation with Neyman-Pearson criterion. Fig.3 shows the performance comparison under different INRs. It is clear that INR has great effect on the
0.8 PD
0 0.75
0.75 0.7 0.65 Our Method, unknown s(n),INR = −16dB, SNR = 20dB Our Method, unknown s(n),INR = −16dB, SNR = 10dB Our Method, known s(n),INR = −16dB, SNR = 20dB Our Method, known s(n),INR = −16dB, SNR = 10dB
0.6 0.55 0.5
0
0.05
0.1
Fig. 4.
0.15
0.2
0.25 PF
0.3
0.35
0.4
ROCs comparison under different SINRs
0.45
0.5
V. C ONCLUSION In this paper, we exploit the spectrum sensing with selfsignal eliminated to improve the performance of In-band detection without quiet period. The requirement of spectrum sensing in cognitive radio is discussed. An In-band spectrum sensing method based on linear self-signal elimination is proposed. In this method, quiet period is not needed so that transmission performance can be improved without the interruption of ongoing transmission. We shows that our method works well in the IEEE802.22 environment. We know that the performance of detection without quiet period is worse than those with quiet period. How we arrange the detection when both methods are used in the system to improve the detection performance or system throughput need further discussion. ACKNOWLEDGMENT This research was supported by National Science Fund for Distinguished Young Scholars (60725105), the sixth project of the Key Project of National Nature Science Foundation of China (No.60496316), the state 863 projects (No.2007AA01Z288), the National Nature Science Foundation of China (No.60572146). the Research Fund for the Doctoral Program of Higher Education (No.20050701007), the Teaching and Research Award Program for Outstanding Young Teachers in Higher Education Institutions of MOE, P. R. China, the Key Project of Chinese Ministry of Education (No. 107103), the 111 Project (No.B08038). R EFERENCES [1] S. Haykin. Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 2005, 23(2):201-220 [2] IEEE. IEEE P802.221.22TM/D0.1 Draft Standard for Wireless Regional Area Networks Part 22: Cognitive Wireless RAN Medium Access Control (MAC) and Physical Layer (PHY) specifications: Policies and procedures for operation in the TV Bands, IEEE. 2006
[3] D. Cabric, S. Mishra, and R. Brodersen. Implementation issues in spectrum sensing for cognitive radios. In Signals, Systems and Computers, 2004. Conference Record of the Thirty-Eighth Asilomar Conference on, 2004, page(s):772C776. [4] Baowei Ji, Yinong Ding, David Mazzarese, Channel Sensing Based on Self-Signal Suppression (SSS), IEEE 802.22-06/0120r1, Jul.2006. Available: http://www.ieee802.org/22/Meeting documents/ 2006 July/22-060120-00-0000 Samsung SSS-Based Channel Sensing.doc [5] Linjun Lv, Soo-Young Chang, et.al, Interference Detection for Sensing, IEEE 802.22-06/0125r0, Jul.2006. Available: http://www.ieee802.org/22/ Meeting documents/2006 July/22-06-0125-00-0000 Huawei Interference Detection Sensing.ppt [6] Dong Chen, Jiandong Li, JingMa, In-Band Sensing without Quiet Period in Cognitive Radio, in the proceedings of IEEE Wireless Communications & Networking Conference 2008, Las Vegas, 2008 [7] Tang, H.,Some physical layer issues of wide-band cognitive radio systems, New Frontiers in Dynamic Spectrum Access Networks, 2005 First IEEE International Symposium on, 2005, page(s):151-159 [8] S. Shellhammer, Numerical Spectrum Sensing Requirements], IEEE Std. 802.22-06/0088r0, Jun. 2006. [9] C. R. Stevenson, C. Cordeiro, E. Sofer, and G. Chouinard, Functional Requirements for the 802.22 WRAN Standard, IEEE Std. 802.22-05/0007r46, Sep. 2005. [10] Christian B. Peel and A. Lee Swindlehurst., Effective SNR for SpaceTime Modulation Over a Time-Varying Rician Channel, IEEE Transactions on Communications. 2004. 52(1), page(s):17-23 [11] W. C. Jakes, Microwave Mobile Comunications, New York: IEEE Press, 1993 [12] L. Ludeman, Random Processes: Filtering, Detection, Estimation. Hoboken, NJ: John Wiley and Sons, 2003 [13] Shellhammer, S.J. and Tandra, R. and Tomcik, J.. Performance of power detector sensors of DTV signals in IEEE 802.22 WRANs. Proceedings of the first international workshop on Technology and policy for accessing spectrum, ACM Press New York, NY, USA, 2006 [14] Eli Sofer, WRAN Channel Modeling, IEEE 802.22-05/0055r0, Jul. 2005. Available: http://www.ieee802.org/22/Meeting documents/2005 July/2205-0055-00-0000 WRAN Channel Modeling.doc [15] Steven M Kay. Fundamentals of statistical signal processing. Englewood Cliffs, NJ: Prentice-Hall, 1993
