Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010, Article ID 381465, 15 pages doi:10.1155/2010/381465 Review Article A Review on Spectrum Sensing for Cognitive Radio: Challenges and Solutions Yonghong Zeng, Ying-Chang Liang, Anh Tuan Hoang, and Rui Zhang Institute for Infocomm Research, A���STAR, Singapore 138632 Correspondence should be addressed to Yonghong Zeng, yhzeng@i2r.a-star.edu.sg Received 13 May 2009 Accepted 9 October 2009 Academic Editor: Jinho Choi Copyright �� 2010 Yonghong Zeng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Cognitive radio is widely expected to be the next Big Bang in wireless communications. Spectrum sensing, that is, detecting the presence of the primary users in a licensed spectrum, is a fundamental problem for cognitive radio. As a result, spectrum sensing has reborn as a very active research area in recent years despite its long history. In this paper, spectrum sensing techniques from the optimal likelihood ratio test to energy detection, matched filtering detection, cyclostationary detection, eigenvalue-based sensing, joint space-time sensing, and robust sensing methods are reviewed. Cooperative spectrum sensing with multiple receivers is also discussed. Special attention is paid to sensing methods that need little prior information on the source signal and the propagation channel. Practical challenges such as noise power uncertainty are discussed and possible solutions are provided. Theoretical analysis on the test statistic distribution and threshold setting is also investigated. 1. Introduction It was shown in a recent report [1] by the USA Federal Communications Commission (FCC) that the conventional fixed spectrum allocation rules have resulted in low spectrum usage e���ciency in almost all currently deployed frequency bands. Measurements in other countries also have shown similar results [2]. Cognitive radio, first proposed in [3], is a promising technology to fully exploit the under-utilized spectrum, and consequently it is now widely expected to be the next Big Bang in wireless communications. There have been tremendous academic researches on cognitive radios, for example, [4, 5], as well as application initiatives, such as the IEEE 802.22 standard on wireless regional area network (WRAN) [6, 7] and the Wireless Innovation Alliance [8] including Google and Microsoft as members, which advocate to unlock the potential in the so-called ���White Spaces��� in the television (TV) spectrum. The basic idea of a cognitive radio is spectral reusing or spectrum sharing, which allows the secondary networks/users to communicate over the spectrum allocated/licensed to the primary users when they are not fully utilizing it. To do so, the secondary users are required to frequently perform spectrum sensing, that is, detecting the presence of the primary users. Whenever the primary users become active, the secondary users have to detect the presence of them with a high probability and vacate the channel or reduce transmit power within certain amount of time. For example, for the upcoming IEEE 802.22 standard, it is required for the secondary users to detect the TV and wireless microphone signals and vacant the channel within two seconds once they become active. Furthermore, for TV signal detection, it is required to achieve 90% probability of detection and 10% probability of false alarm at signal-to-noise ratio (SNR) level as low as ���20 dB. There are several factors that make spectrum sensing practically challenging. First, the required SNR for detection may be very low. For example, even if a primary transmitter is near a secondary user (the detection node), the transmitted signal of the primary user can be deep faded such that the primary signal���s SNR at the secondary receiver is well below ���20 dB. However, the secondary user still needs to detect the primary user and avoid using the channel because it may strongly interfere with the primary receiver if it transmits. A practical scenario of this is a wireless microphone operating in TV bands, which only transmits with a power less than 50 mW and a bandwidth less than

2 EURASIP Journal on Advances in Signal Processing 200 KHz. If a secondary user is several hundred meters away from the microphone device, the received SNR may be well below ���20 dB. Secondly, multipath fading and time dispersion of the wireless channels complicate the sensing problem. Multipath fading may cause the signal power to fluctuate as much as 30 dB. On the other hand, unknown time dispersion in wireless channels may turn the coherent detection unreliable. Thirdly, the noise/interference level may change with time and location, which yields the noise power uncertainty issue for detection [9���12]. Facing these challenges, spectrum sensing has reborn as a very active research area over recent years despite its long history. Quite a few sensing methods have been proposed, including the classic likelihood ratio test (LRT) [13], energy detection (ED) [9, 10, 13, 14], matched filtering (MF) detec- tion [10, 13, 15], cyclostationary detection (CSD) [16���19], and some newly emerging methods such as eigenvalue-based sensing [6, 20���25], wavelet-based sensing [26], covariance- based sensing [6, 27, 28], and blindly combined energy detection [29]. These methods have different requirements for implementation and accordingly can be classified into three general categories: (a) methods requiring both source signal and noise power information, (b) methods requiring only noise power information (semiblind detection), and (c) methods requiring no information on source signal or noise power (totally blind detection). For example, LRT, MF, and CSD belong to category A ED and wavelet-based sensing methods belong to category B eigenvalue-based sensing, covariance-based sensing, and blindly combined energy detection belong to category C. In this paper, we focus on methods in categories B and C, although some other methods in category A are also discussed for the sake of completeness. Multiantenna/receiver systems have been widely deployed to increase the channel capacity or improve the transmission reliability in wireless communications. In addition, multiple antennas/receivers are commonly used to form an array radar [30, 31] or a multiple-input multiple-output (MIMO) radar [32, 33] to enhance the performance of range, direction, and/or velocity estimations. Consequently, MIMO techniques can also be applied to improve the performance of spectrum sensing. Therefore, in this paper we assume a multi-antenna system model in general, while the single-antenna system is treated as a special case. When there are multiple secondary users/receivers dis- tributed at different locations, it is possible for them to cooperate to achieve higher sensing reliability. There are various sensing cooperation schemes in the current literature [34���44]. In general, these schemes can be classified into two categories: (A) data fusion: each user sends its raw data or processed data to a specific user, which processes the data collected and then makes the final decision (B) decision fusion: multiple users process their data independently and send their decisions to a specific user, which then makes the final decision. In this paper, we will review various spectrum sensing methods from the optimal LRT to practical joint space-time sensing, robust sensing, and cooperative sensing and discuss their advantages and disadvantages. We will pay special attention to sensing methods with practical application potentials. The focus of this paper is on practical sensing algorithm designs for other aspects of spectrum sensing in cognitive radio, the interested readers may refer to other resources like [45���52]. The rest of this paper is organized as follows. The system model for the general setup with multiple receivers for sensing is given in Section 2. The optimal LRT-based sensing due to the Neyman-Pearson theorem is reviewed in Section 3. Under some special conditions, it is shown that the LRT becomes equivalent to the estimator-correlator detection, energy detection, or matched filtering detection. The Bayesian method and the generalized LRT for sensing are discussed in Section 4. Detection methods based on the spatial correlations among multiple received signals are discussed in Section 5, where optimally combined energy detection and blindly combined energy detection are shown to be optimal under certain conditions. Detection methods combining both spatial and time correlations are reviewed in Section 6, where the eigenvalue-based and covariance-based detections are discussed in particular. The cyclostationary detection, which exploits the statistical features of the pri- mary signals, is reviewed in Section 7. Cooperative sensing is discussed in Section 8. The impacts of noise uncertainty and noise power estimation to the sensing performance are analyzed in Section 9. The test statistic distribution and threshold setting for sensing are reviewed in Section 10, where it is shown that the random matrix theory is very useful for the related study. The robust spectrum sensing to deal with uncertainties in source signal and/or noise power knowledge is reviewed in Section 11, with special emphasis on the robust versions of LRT and matched filtering detection methods. Practical challenges and future research directions for spectrum sensing are discussed in Section 12. Finally, Section 13 concludes the paper. 2. System Model We assume that there are M ��� 1 antennas at the receiver. These antennas can be su���ciently close to each other to form an antenna array or well separated from each other. We assume that a centralized unit is available to process the signals from all the antennas. The model under consideration is also applicable to the multinode cooperative sensing [34��� 44, 53], if all nodes are able to send their observed signals to a central node for processing. There are two hypotheses: H0, signal absent, and H1, signal present. The received signal at antenna/receiver i is given by H0 : xi(n) = ��i(n), H1 : xi(n) = si(n) + ��i(n), i = 1, . . . , M. (1) In hypothesis H1, si(n) is the received source signal at antenna/receiver i, which may include the channel multipath and fading effects. In general, si(n) can be expressed as si(n) = K k=1 qik l=0 hik(l)sk(n ��� l), (2)

EURASIP Journal on Advances in Signal Processing 3 where K denotes the number of primary user/antenna signals, sk(n) denotes the transmitted signal from primary user/antenna k, hik(l) denotes the propagation channel coe���cient from the kth primary user/antenna to the ith receiver antenna, and qik denotes the channel order for hik. It is assumed that the noise samples ��i(n)���s are independent and identically distributed (i.i.d) over both n and i. For simplicity, we assume that the signal, noise, and channel coe���cients are all real numbers. The objective of spectrum sensing is to make a decision on the binary hypothesis testing (choose H0 or H1) based on the received signal. If the decision is H1, further information such as signal waveform and modulation schemes may be classified for some applications. However, in this paper, we focus on the basic binary hypothesis testing problem. The performance of a sensing algorithm is generally indicated by two metrics: probability of detection, Pd, which defines, at the hypothesis H1, the probability of the algorithm correctly detecting the presence of the primary signal and probability of false alarm, P f a , which defines, at the hypothesis H0, the probability of the algorithm mistakenly declaring the presence of the primary signal. A sensing algorithm is called ���optimal��� if it achieves the highest Pd for a given P f a with a fixed number of samples, though there could be other criteria to evaluate the performance of a sensing algorithm. Stacking the signals from the M antennas/receivers yields the following M �� 1 vectors: x(n) = x1(n) ������ xM(n) T , s(n) = s1(n) ������ sM(n) T , ��(n) = ��1(n) ������ ��M(n) T . (3) The hypothesis testing problem based on N signal samples is then obtained as H0 : x(n) = ��(n), H1 : x(n) = s(n) + ��(n), n = 0, . . . , N ��� 1. (4) 3. Neyman-Pearson Theorem The Neyman-Pearson (NP) theorem [13, 54, 55] states that, for a given probability of false alarm, the test statistic that maximizes the probability of detection is the likelihood ratio test (LRT) defined as TLRT(x) = p(x | H1) p(x | H0) , (5) where p(��) denotes the probability density function (PDF), and x denotes the received signal vector that is the aggre- gation of x(n), n = 0, 1, . . . , N ��� 1. Such a likelihood ratio test decides H1 when TLRT(x) exceeds a threshold ��, and H0 otherwise. The major di���culty in using the LRT is its requirements on the exact distributions given in (5). Obviously, the distribution of random vector x under H1 is related to the source signal distribution, the wireless channels, and the noise distribution, while the distribution of x under H0 is related to the noise distribution. In order to use the LRT, we need to obtain the knowledge of the channels as well as the signal and noise distributions, which is practically di���cult to realize. If we assume that the channels are flat-fading, and the received source signal sample si(n)���s are independent over n, the PDFs in LRT are decoupled as p(x | H1) = N���1 n=0 p(x(n) | H1), p(x | H0) = N���1 n=0 p(x(n) | H0). (6) If we further assume that noise and signal samples are both Gaussian distributed, that is, ��(n) ��� N (0, ���� 2I) and s(n) ��� N (0, Rs ), the LRT becomes the estimator-correlator (EC) [13] detector for which the test statistic is given by TEC(x) = N���1 n=0 xT (n)Rs Rs + ���� 2I ���1 x(n). (7) From (4), we see that Rs (Rs + 2���� 2I)���1x(n) is actually the minimum-mean-squared-error (MMSE) estimation of the source signal s(n). Thus, TEC(x) in (7) can be seen as the correlation of the observed signal x(n) with the MMSE estimation of s(n). The EC detector needs to know the source signal covariance matrix Rs and noise power ���� 2. When the signal presence is unknown yet, it is unrealistic to require the source signal covariance matrix (related to unknown channels) for detection. Thus, if we further assume that Rs = ��s2I, the EC detector in (7) reduces to the well-known energy detector (ED) [9, 14] for which the test statistic is given as follows (by discarding irrelevant constant terms): TED(x) = N���1 n=0 xT (n)x(n). (8) Note that for the multi-antenna/receiver case, TED is actually the summation of signals from all antennas, which is a straightforward cooperative sensing scheme [41, 56, 57]. In general, the ED is not optimal if Rs is non-diagonal. If we assume that noise is Gaussian distributed and source signal s(n) is deterministic and known to the receiver, which is the case for radar signal processing [32, 33, 58], it is easy to show that the LRT in this case becomes the matched filtering-based detector, for which the test statistic is TMF(x) = N���1 n=0 sT (n)x(n). (9) 4. Bayesian Method and the Generalized Likelihood Ratio Test In most practical scenarios, it is impossible to know the likelihood functions exactly, because of the existence of