A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> An active array processor is concerned with the problem of detecting a signal echo, reflected from a target, in the presence of reverberation (clutter). The processor can also be used to estimate target range and bearing. It is a priori not evident whether the optimum (likelihood ratio) detector can be factored into spatial and temporal operations, thus resulting in a simpler processor implementation. This paper studies this problem for a linear continuous array in a reverberation-limited environment. Conditions on signal, reverberation, and array parameters are derived under which the optimum detector is factorable. The validity of using factorability as a criterion of signal design is briefly examined. Finally, the relationship between space-time factorability and range-bearing estimates is pointed out. <s> BIB001 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> As a continuation of Part I, the spectral correlation function is presented for a variety of types of digitally modulated signals. These include digital pulse-amplitude, pulse-width, and pulse-position modulation, and various types of phase-shift keying and frequency-shift keying. The magnitudes of the spectral correlation functions are graphed as the heights of surfaces above a bifrequency plane, and these graphs are used as visual aids for comparison and contrast of the spectral correlation properties of different modulation types. <s> BIB002 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> It is shown that the cyclostationarity attribute, as it is reflected in the periodicities of (second-order) moments of the signal, can be interpreted in terms of the property that allows generation of spectral lines from the signal by putting it through a (quadratic) nonlinear transformation. The fundamental link between the spectral-line generation property and the statistical property called spectral correlation, which corresponds to the correlation that exists between the random fluctuations of components of the signal residing in distinct spectral bands, is explained. The effects on the spectral-correlation characteristics of some basic signal processing operations, such as filtering, product modulation, and time sampling, are examined. It is shown how to use these results to derive the spectral-correlation characteristics for various types of man-made signals. Some ways of exploiting the inherent spectral redundancy associated with spectral correlation to perform various signal processing tasks involving detection and estimation of highly corrupted man-made signals are described. > <s> BIB003 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> The standard analysis of the radiometric detectability of a spread-spectrum signal assumes a background of stationary, white Gaussian noise whose power spectral density can be measured very accurately. This assumption yields a fairly high probability of interception, even for signals of short duration. By explicitly considering the effect of uncertain knowledge of the noise power density, it is demonstrated that detection of these signals by a wideband radiometer can be considerably more difficult in practice than is indicated by the standard result. Worst-case performance bounds are provided as a function of input signal-to-noise ratio (SNR), time-bandwidth (TW) product and peak-to-peak noise uncertainty. The results are illustrated graphically for a number of situations of interest. It is also shown that asymptotically, as the TW product becomes large, the SNR required for detection becomes a function of noise uncertainty only and is independent of the detection parameters and the observation interval. > <s> BIB004 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> 1 Introduction.- 1.1 Distributed Detection Systems.- 1.2 Outline of the Book.- 2 Elements of Detection Theory.- 2.1 Introduction.- 2.2 Bayesian Detection Theory.- 2.3 Minimax Detection.- 2.4 Neyman-Pearson Test.- 2.5 Sequential Detection.- 2.6 Constant False Alarm Rate (CFAR) Detection.- 2.7 Locally Optimum Detection.- 3 Distributed Bayesian Detection: Parallel Fusion Network.- 3.1 Introduction.- 3.2 Distributed Detection Without Fusion.- 3.3 Design of Fusion Rules.- 3.4 Detection with Parallel Fusion Network.- 4 Distributed Bayesian Detection: Other Network Topologies.- 4.1 Introduction.- 4.2 The Serial Network.- 4.3 Tree Networks.- 4.4 Detection Networks with Feedback.- 4.5 Generalized Formulation for Detection Networks.- 5 Distributed Detection with False Alarm Rate Constraints.- 5.1 Introduction.- 5.2 Distributed Neyman-Pearson Detection.- 5.3 Distributed CFAR Detection.- 5.4 Distributed Detection of Weak Signals.- 6 Distributed Sequential Detection.- 6.1 Introduction.- 6.2 Sequential Test Performed at the Sensors.- 6.3 Sequential Test Performed at the Fusion Center.- 7 Information Theory and Distributed Hypothesis Testing.- 7.1 Introduction.- 7.2 Distributed Detection Based on Information Theoretic Criterion.- 7.3 Multiterminal Detection with Data Compression.- Selected Bibliography. <s> BIB005 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> Software radios are emerging as platforms for multiband multimode personal communications systems. Radio etiquette is the set of RF bands, air interfaces, protocols, and spatial and temporal patterns that moderate the use of the radio spectrum. Cognitive radio extends the software radio with radio-domain model-based reasoning about such etiquettes. Cognitive radio enhances the flexibility of personal services through a radio knowledge representation language. This language represents knowledge of radio etiquette, devices, software modules, propagation, networks, user needs, and application scenarios in a way that supports automated reasoning about the needs of the user. This empowers software radios to conduct expressive negotiations among peers about the use of radio spectrum across fluents of space, time, and user context. With RKRL, cognitive radio agents may actively manipulate the protocol stack to adapt known etiquettes to better satisfy the user's needs. This transforms radio nodes from blind executors of predefined protocols to radio-domain-aware intelligent agents that search out ways to deliver the services the user wants even if that user does not know how to obtain them. Software radio provides an ideal platform for the realization of cognitive radio. <s> BIB006 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> We derive Cramer-Rao bound (CRB) expressions for the range (time delay), velocity (Doppler shift), and direction of a point target using an active radar or sonar array. First, general CRB expressions are derived for a narrowband signal and array model and a space-time separable noise model that allows both spatial and temporal correlation. We discuss the relationship between the CRB and ambiguity function for this model. Then, we specialize our CRB results to the case of temporally white noise and the practically important signal shape of a linear frequency modulated (chirp) pulse sequence. We compute the CRB for a three-dimensional (3-D) array with isotropic sensors in spatially white noise and show that it is a function of the array geometry only through the "moments of inertia" of the array. The volume of the confidence region for the target's location is proposed as a measure of accuracy. For this measure, we show that the highest (and lowest) target location accuracy is achieved if the target lies along one of the principal axes of inertia of the array. Finally, we compare the location accuracies of several array geometries. <s> BIB007 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> It has recently been shown that multiple-input multiple-output (MIMO) antenna systems have the potential to improve dramatically the performance of communication systems over single antenna systems. Unlike beamforming, which presumes a high correlation between signals either transmitted or received by an array, the MIMO concept exploits the independence between signals at the array elements. In conventional radar, target scintillations are regarded as a nuisance parameter that degrades radar performance. The novelty of MIMO radar is that it takes the opposite view; namely, it capitalizes on target scintillations to improve the radar's performance. We introduce the MIMO concept for radar. The MIMO radar system under consideration consists of a transmit array with widely-spaced elements such that each views a different aspect of the target. The array at the receiver is a conventional array used for direction finding (DF). The system performance analysis is carried out in terms of the Cramer-Rao bound of the mean-square error in estimating the target direction. It is shown that MIMO radar leads to significant performance improvement in DF accuracy. <s> BIB008 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a software-defined radio, is defined as an intelligent wireless communication system that is aware of its environment and uses the methodology of understanding-by-building to learn from the environment and adapt to statistical variations in the input stimuli, with two primary objectives in mind: /spl middot/ highly reliable communication whenever and wherever needed; /spl middot/ efficient utilization of the radio spectrum. Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks. 1) Radio-scene analysis. 2) Channel-state estimation and predictive modeling. 3) Transmit-power control and dynamic spectrum management. This work also discusses the emergent behavior of cognitive radio. <s> BIB009 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> In this paper we consider the problem of detecting whether a frequency band is being used by a known primary user. We derive fundamental bounds on detection performance in low SNR in the presence of noise uncertainty - the noise is assumed to be white, but we know its distribution only to within a particular set. For clarity of analysis, we focus on primary transmissions that are BPSK-modulated random data without any pilot tones or training sequences. The results should all generalize to more general primary transmissions as long as no deterministic component is present. Specifically, we show that for every 'moment detector' there exists an SNR below which detection becomes impossible in the presence of noise uncertainty. In the neighborhood of that SNR wall, we show how the sample complexity of detection approaches infinity. We also show that if our radio has a finite dynamic range (upper and lower limits to the voltages we can quantize), then at low enough SNR, any detector can be rendered useless even under moderate noise uncertainty. <s> BIB010 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> In this paper, signal detection methods for spectrum sensing in IEEE 802.22 wireless RAN systems are discussed. As most of the manmade signals can be treated as a cyclostationary random process, the spectral correlation function is effective for the detection of these signals. In WRAN systems, due to the specific detection environment, the computational complexity of the spectral correlation based method is significantly reduced. Peak detection in the high SNR environment together with contour figure based unique patterns search method in the low SNR environment are proposed for the primary user signal detection. <s> BIB011 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> In cognitive radio networks, the first cognitive task preceding any form of dynamic spectrum management is the sensing and identification of spectrum holes in wireless environments. This paper develops a wavelet approach to efficient spectrum sensing of wideband channels. The signal spectrum over a wide frequency band is decomposed into elementary building blocks of subbands that are well characterized by local irregularities in frequency. As a powerful mathematical tool for analyzing singularities and edges, the wavelet transform is employed to detect and estimate the local spectral irregular structure, which carries important information on the frequency locations and power spectral densities of the subbands. Along this line, a couple of wideband spectrum sensing techniques are developed based on the local maxima of the wavelet transform modulus and the multi-scale wavelet products. The proposed sensing techniques provide an effective radio sensing architecture to identify and locate spectrum holes in the signal spectrum. <s> BIB012 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> Cognitive Radios have been advanced as a technology for the opportunistic use of under-utilized spectrum since they are able to sense the spectrum and use frequency bands if no Primary user is detected. However, the required sensitivity is very demanding since any individual radio might face a deep fade. We propose light-weight cooperation in sensing based on hard decisions to mitigate the sensitivity requirements on individual radios. We show that the "link budget" that system designers have to reserve for fading is a significant function of the required probability of detection. Even a few cooperating users (~10-20) facing independent fades are enough to achieve practical threshold levels by drastically reducing individual detection requirements. Hard decisions perform almost as well as soft decisions in achieving these gains. Cooperative gains in a environment where shadowing is correlated, is limited by the cooperation footprint (area in which users cooperate). In essence, a few independent users are more robust than many correlated users. Unfortunately, cooperative gain is very sensitive to adversarial/failing Cognitive Radios. Radios that fail in a known way (always report the presence/absence of a Primary user) can be compensated for by censoring them. On the other hand, radios that fail in unmodeled ways or may be malicious, introduce a bound on achievable sensitivity reductions. As a rule of thumb, if we believe that 1/N users can fail in an unknown way, then the cooperation gains are limited to what is possible with N trusted users. <s> BIB013 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> Today's wireless networks are characterized by a fixed spectrum assignment policy. However, a large portion of the assigned spectrum is used sporadically and geographical variations in the utilization of assigned spectrum ranges from 15% to 85% with a high variance in time. The limited available spectrum and the inefficiency in the spectrum usage necessitate a new communication paradigm to exploit the existing wireless spectrum opportunistically. This new networking paradigm is referred to as NeXt Generation (xG) Networks as well as Dynamic Spectrum Access (DSA) and cognitive radio networks. The term xG networks is used throughout the paper. The novel functionalities and current research challenges of the xG networks are explained in detail. More specifically, a brief overview of the cognitive radio technology is provided and the xG network architecture is introduced. Moreover, the xG network functions such as spectrum management, spectrum mobility and spectrum sharing are explained in detail. The influence of these functions on the performance of the upper layer protocols such as routing and transport are investigated and open research issues in these areas are also outlined. Finally, the cross-layer design challenges in xG networks are discussed. <s> BIB014 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> Spectral sensing for the presence of ATSC DTV signals in VHF/UHF TV bands under a very low SNR environment is one of the core technologies in IEEE 802.22 WRAN. There are multiple signatures embedded in the ATSC DTV signals and these signatures can be utilized to perform spectrum sensing. Signature-based spectrum sensing algorithms have an advantage in that when the signals other than noise are detected, we are almost sure that they are signals we want to detect. Thus, in this paper, several signature based spectrum sensing algorithms are presented. The false alarm rate for detectors employing different algorithms is analytically derived. The operating characteristic curves for different detectors are determined from computer simulations using ATSC A/74 DTV signal captures as a test database. <s> BIB015 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> Sensing (signal detection) is a fundamental problem in cognitive radio. In this paper, a new method is proposed based on the eigenvalues of the covariance matrix of the received signal. It is shown that the ratio of the maximum eigenvalue to the minimum eigenvalue can be used to detect the signal existence. Based on some latest random matrix theories (RMT), we can quantize the ratio and find the threshold. The probability of false alarm is also found by using the RMT. The proposed method overcomes the noise uncertainty difficulty while keeps the advantages of the energy detection. The method can be used for various sensing applications without knowledge of the signal, the channel and noise power. Simulations based on randomly generated signals and captured ATSC DTV signals are presented to verify the methods. <s> BIB016 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> Sensing (signal detection) is a fundamental problem in cognitive radio. The statistical covariances of signal and noise are usually different. In this paper, this property is used to differentiate signal from noise. The sample covariance matrix of the received signal is computed and transformed based on the receiving filter. Then two detection methods are proposed based on the transformed sample covariance matrix. One is the covariance absolute value (CAV) detection and the other is the covariance Frobenius norm (CFN) detection. Theoretical analysis and threshold setting for the algorithms are discussed. Both methods do not need any information of the signal, the channel and noise power as a priori. Simulations based on captured ATSC DTV signals are presented to verify the methods. <s> BIB017 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> Superiority of multiple-input multiple-output (MIMO) radars over conventional phased array radars has been recently shown in many aspects. MIMO radars have better detection performance and can extract target information more precisely than phased array radars. In this paper, the problem of target detection using temporal coherent pulse terrain is considered for MIMO radars. We have introduced a structure that is suitable for coherent processing. Based on this structure, we have formulated this problem as a hypothesis test. One decision rule is developed for detection of target with unknown amplitude embedded in additive white gaussian noise (AWGN) with known power using the generalized likelihood ratio test (GLRT). Another adaptive scheme for the case of unknown power is presented. We have also proposed an ad-hoc constant false alarm rate (CFAR) detector against AWGN with unknown power. Performance of these detectors have been evaluated analytically. The results show the superiority of the MIMO radars with temporal coherent processing over the conventional phased arrays. <s> BIB018 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> In cognitive radio networks, cognitive (unlicensed) users need to continuously monitor spectrum for the presence of primary (licensed) users. In this paper, we illustrate the benefits of cooperation in cognitive radio. We show that by allowing the cognitive users operating in the same band to cooperate we can reduce the detection time and thus increase the overall agility. We first consider a two-user cognitive radio network and show how the inherent asymmetry in the network can be exploited to increase the agility. We show that our cooperation scheme increases the agility of the cognitive users by as much as 35%. We then extend our cooperation scheme to multicarrier networks with two users per carrier and analyze asymptotic agility gain. In Part II of our paper [1], we investigate multiuser single carrier networks. We develop a decentralized cooperation protocol which ensures agility gain for arbitrarily large cognitive network population. <s> BIB019 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> In cognitive radio networks, cognitive (unlicensed) users need to continuously monitor spectrum for the presence of primary (licensed) users. In this paper, we illustrate the benefits of cooperation in cognitive radio. We show that by allowing the cognitive users operating in the same band to cooperate we can reduce the detection time and thus increase the overall agility. We first consider a two-user cognitive radio network and show how the inherent asymmetry in the network can be exploited to increase the agility. We show that our cooperation scheme increases the agility of the cognitive users by as much as 35%. We then extend our cooperation scheme to multicarrier networks with two users per carrier and analyze asymptotic agility gain. In Part II of our paper [1], we investigate multiuser single carrier networks. We develop a decentralized cooperation protocol which ensures agility gain for arbitrarily large cognitive network population. <s> BIB020 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> In cognitive radio systems, secondary users can be coordinated to perform cooperative spectrum sensing so as to detect the primary user more accurately. However, when the sensing observations are forwarded to a common receiver through fading channels, the sensing performance can be severely degraded. In this paper, we propose a cluster-based cooperative spectrum sensing method to improve the sensing performance. By separating all the secondary users into a few clusters and selecting the most favorable user in each cluster to report to the common receiver, the proposed method can exploit the user selection diversity so that the sensing performance can be enhanced. Furthermore, decision fusion and energy fusion are both studied and the analytical performance results are given. Numerical results show that the sensing performance is improved significantly as opposed to conventional spectrum sensing. <s> BIB021 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> In this letter, we consider cooperative spectrum sensing based on energy detection in cognitive radio networks. Soft combination of the observed energies from different cognitive radio users is investigated. Based on the Neyman-Pearson criterion, we obtain an optimal soft combination scheme that maximizes the detection probability for a given false alarm probability. Encouraged by the performance gain of soft combination, we further propose a new softened hard combination scheme with two-bit overhead for each user and achieve a good tradeoff between detection performance and complexity. <s> BIB022 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> In cognitive radio networks, the secondary users can use the frequency bands when the primary users are not present. Hence secondary users need to constantly sense the presence of the primary users. When the primary users are detected, the secondary users have to vacate that channel. This makes the probability of detection important to the primary users as it indicates their protection level from secondary users. When the secondary users detect the presence of a primary user which is in fact not there, it is referred to as false alarm. The probability of false alarm is important to the secondary users as it determines their usage of an unoccupied channel. Depending on whose interest is of priority, either a targeted probability of detection or false alarm shall be set. After setting one of the probabilities, the other can be optimized through cooperative sensing. In this paper, we show that cooperating all secondary users in the network does not necessary achieve the optimum performance, but instead, it is achieved by cooperating a certain number of users with the highest primary user's signal to noise ratio. Computer simulations have shown that the Pd can increase from 92.03% to 99.88% and Pf can decrease from 6.02% to 0.06% in a network with 200 users. <s> BIB023 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> We study the 24-hour spectrum usage pattern in Singapore in the frequency bands ranging from 80 MHz to 5850 MHz. The objectives are to find how the scarce radio spectrum allocated to different services is utilized in Singapore and identify the bands that could be accessed for future opportunistic use due to their low or no active utilization. The results from the spectrum measurements taken over 12 weekday periods reveal that a significant amount of spectrum in Singapore has very low occupancy all the time. The occupancy is quantified as the amount of spectrum detected above a certain received power threshold. The outcome of this study suggests that Singapore has a great potential for employing emerging spectrum sharing technology such as the cognitive radio technology to accommodate enormous demands for future wireless services. However, this study of spectrum survey is preliminary in its nature and future long term studies need to be performed to determine any potential secondary usage on those channels that have low or no active utilization. <s> BIB024 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> This paper considers the detection of the presence/absence of signals in uncertain low SNR environments. Small modeling uncertainties are unavoidable in any practical system and so robustness to them is a fundamental performance metric. The impact of these modeling uncertainties can be quantified by the position of the "SNR wall" below which a detector will fail to be robust, no matter how long it can observe the channel. We propose simple mathematical models for the uncertainty in the noise and fading processes. These are used to show what aspects of the model lead to SNR walls for differing levels of knowledge of the signal to be detected. These results have implications for wireless spectrum regulators. The context is opportunistically sharing spectrum with primary users that must be detected in order to avoid causing harmful interference on a channel. Ideally, a secondary system would be able to detect primaries robustly without having to know much about their signaling strategies. We argue that the tension between primary and secondary users is captured by the technical question of computing the optimal tradeoff between the primary user's capacity and the secondary user's sensing robustness as quantified by the SNR wall. This is an open problem, but we compute this tradeoff for some simple detectors. <s> BIB025 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> In this letter, a method is proposed to optimally combine the received signal samples in space and time based on the principle of maximizing the signal-to-noise ratio (SNR). After the combining, energy detection (ED) is used. However, optimal combining needs information of the source signal and channel, which is usually unknown. To overcome this difficulty, a method is proposed to blindly combine the signal samples. Similar to energy detection, blindly combined energy detection (BCED) does not need any information of the source signal and the channel a priori. BCED can be much better than ED for highly correlated signals, and most importantly, it does not need noise power estimation and overcomes ED's susceptibility to noise uncertainty. Also, perfect synchronization is not required. Simulations based on wireless microphone signals and randomly generated signals are presented to verify the methods. <s> BIB026 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> One of the main requirements of cognitive radio systems is the ability to reliably detect the presence of licensed primary transmissions. Previous works on the problem of detection for cognitive radio have suggested the necessity of user cooperation in order to be able to detect at the low signal-to-noise ratios experienced in practical situations. We consider a system of cognitive radio users who cooperate with each other in trying to detect licensed transmissions. Assuming that the cooperating nodes use identical energy detectors, we model the received signals as correlated log-normal random variables and study the problem of fusing the decisions made by the individual nodes. We design a linear-quadratic (LQ) fusion strategy based on a deflection criterion for this problem, which takes into account the correlation between the nodes. Using simulations we show that when the observations at the sensors are correlated, the LQ detector significantly outperforms the counting rule, which is the fusion rule that is obtained by ignoring the correlation. <s> BIB027 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> Cognitive radio technology has been proposed to improve spectrum efficiency by having the cognitive radios act as secondary users to opportunistically access under-utilized frequency bands. Spectrum sensing, as a key enabling functionality in cognitive radio networks, needs to reliably detect signals from licensed primary radios to avoid harmful interference. However, due to the effects of channel fading/shadowing, individual cognitive radios may not be able to reliably detect the existence of a primary radio. In this paper, we propose an optimal linear cooperation framework for spectrum sensing in order to accurately detect the weak primary signal. Within this framework, spectrum sensing is based on the linear combination of local statistics from individual cognitive radios. Our objective is to minimize the interference to the primary radio while meeting the requirement of opportunistic spectrum utilization. We formulate the sensing problem as a nonlinear optimization problem. By exploiting the inherent structures in the problem formulation, we develop efficient algorithms to solve for the optimal solutions. To further reduce the computational complexity and obtain solutions for more general cases, we finally propose a heuristic approach, where we instead optimize a modified deflection coefficient that characterizes the probability distribution function of the global test statistics at the fusion center. Simulation results illustrate significant cooperative gain achieved by the proposed strategies. The insights obtained in this paper are useful for the design of optimal spectrum sensing in cognitive radio networks. <s> BIB028 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> Opportunistic unlicensed access to the (temporarily) unused frequency bands across the licensed radio spectrum is currently being investigated as a means to increase the efficiency of spectrum usage. Such opportunistic access calls for implementation of safeguards so that ongoing licensed operations are not compromised. Among different candidates, sensing-based access, where the unlicensed users transmit if they sense the licensed band to be free, is particularly appealing due to its low deployment cost and its compatibility with the legacy licensed systems. The ability to reliably and autonomously identify unused frequency bands is envisaged as one of the main functionalities of cognitive radios. In this article we provide an overview of the regulatory requirements and major challenges associated with the practical implementation of spectrum sensing functionality in cognitive radio systems. Furthermore, we outline different design trade-offs that have to be made in order to enhance various aspects of the system's performance. <s> BIB029 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> Measurements performed at several locations clearly show that frequency spectrum is under-utilized. Cognitive radio is a strong candidate to ensure better spectrum utilization by providing access in an opportunistic manner. Simulations performed on the algorithm that we proposed show promising results in sensing vacant channels in TV bands. Here we present the work, which implements the algorithm on a real-time prototype, and measure its sensing performance in actual environments. Results show that our algorithm is robust under realistic conditions. This confirmation will give much needed confidence in the capability of cognitive radio systems to detect the operation of primary users and protect their use of spectrum. <s> BIB030 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> Spectrum sensing is a fundamental component in a cognitive radio. In this paper, we propose new sensing methods based on the eigenvalues of the covariance matrix of signals received at the secondary users. In particular, two sensing algorithms are suggested, one is based on the ratio of the maximum eigenvalue to minimum eigenvalue; the other is based on the ratio of the average eigenvalue to minimum eigenvalue. Using some latest random matrix theories (RMT), we quantify the distributions of these ratios and derive the probabilities of false alarm and probabilities of detection for the proposed algorithms. We also find the thresholds of the methods for a given probability of false alarm. The proposed methods overcome the noise uncertainty problem, and can even perform better than the ideal energy detection when the signals to be detected are highly correlated. The methods can be used for various signal detection applications without requiring the knowledge of signal, channel and noise power. Simulations based on randomly generated signals, wireless microphone signals and captured ATSC DTV signals are presented to verify the effectiveness of the proposed methods. <s> BIB031 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> In this contribution, we propose a new technique for collaborative sensing based on the analysis of the normalized (by the trace) largest eigenvalues of the sample covariance matrix. Assuming that several base stations are cooperating and without the knowledge of the noise variance, the test is able to determine the presence of mobile users in a network when only few samples are available. Unlike previous heuristic techniques, we show that the test has roots within the Generalized Likelihood Ratio Test and provide an asymptotic random matrix analysis enabling to determine adequate threshold detection values (probability of false alarm). Simulations sustain our theoretical claims. <s> BIB032 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> Recent advances in random matrix theory have spurred the adoption of eigenvalue-based detection techniques for cooperative spectrum sensing in cognitive radio. These techniques use the ratio between the largest and the smallest eigenvalues of the received signal covariance matrix to infer the presence or absence of the primary signal. The results derived so far are based on asymptotical assumptions, due to the difficulties in characterizing the exact eigenvalues ratio distribution. By exploiting a recent result on the limiting distribution of the smallest eigenvalue in complex Wishart matrices, in this paper we derive an expression for the limiting eigenvalue ratio distribution, which turns out to be much more accurate than the previous approximations also in the non-asymptotical region. This result is then applied to calculate the decision sensing threshold as a function of a target probability of false alarm. Numerical simulations show that the proposed detection rule provides a substantial improvement compared to the other eigenvalue-based algorithms. <s> BIB033 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> Spectrum sensing, i.e., detecting the presence of primary users in a licensed spectrum, is a fundamental problem in cognitive radio. Since the statistical covariances of the received signal and noise are usually different, they can be used to differentiate the case where the primary user's signal is present from the case where there is only noise. In this paper, spectrum-sensing algorithms are proposed based on the sample covariance matrix calculated from a limited number of received signal samples. Two test statistics are then extracted from the sample covariance matrix. A decision on the signal presence is made by comparing the two test statistics. Theoretical analysis for the proposed algorithms is given. Detection probability and the associated threshold are found based on the statistical theory. The methods do not need any information about the signal, channel, and noise power a priori. In addition, no synchronization is needed. Simulations based on narrow-band signals, captured digital television (DTV) signals, and multiple antenna signals are presented to verify the methods. <s> BIB034 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> Wireless systems require spectrum to operate, but interference is likely if radios in physical proximity simultaneously operate on the same band. Therefore, spectrum is a potentially scarce resource; across the planet today, spectrum is regulated so that most bands are allocated exclusively to a single system licensed to use that band in any given location. However, such static spectrum allocation policies lead to significant underuse of spectrum [1]. This can be viewed as a kind of regulatory overhead that is paid to get reliable operation. With frequency-agile radios becoming commercially feasible within the next 5-10 years, Cognitive Radio is about making such radios smart enough to share spectrum and reduce the regulatory overhead. This is an impending wireless revolution that draws upon many signal-processing areas including robust detection, sensor networks, as well as the design of incentives and waveforms. This is a short column touching on the issues; further technical details/references can be found in [2]. <s> BIB035 </s> A review on spectrum sensing for cognitive radio: challenges and solutions <s> Introduction <s> Measurements performed at several locations clearly show that frequency spectrum is under-utilized. Cognitive radio (CR) is a strong candidate to ensure better spectrum utilization by providing access in an opportunistic manner, i.e., when the spectrum is temporary or spatially unutilized (a.k.a. white-space). The key enabling technology for realizing better frequency spectrum utilization in CR system is spectrum sensing. In particular, spectrum sensing in television (TV) bands is of great interest due to the potential availability of spectrum as a result of the planned migration of analog TV broadcasting to digital TV broadcasting. In this paper, we present the TV white-space prototype that we developed to detect vacant spectrum in TV bands. The prototype is implemented on a real-time platform and tested in practical environments. Results show that our prototype is able to detect vacant channels up to sensitivities from -116 to -120 dBm under realistic conditions. This confirmation will give the much needed confidence in the capability of CR systems to detect the operation of primary users and protect their use of spectrum. Copyright © 2008 John Wiley & Sons, Ltd. <s> BIB036
It was shown in a recent report  by the USA Federal Communications Commission (FCC) that the conventional fixed spectrum allocation rules have resulted in low spectrum usage efficiency in almost all currently deployed frequency bands. Measurements in other countries also have shown similar results BIB024 . Cognitive radio, first proposed in BIB006 , 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, BIB009  , as well as application initiatives, such as the IEEE 802.22 standard on wireless regional area network (WRAN)   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 BIB004  BIB010 BIB025 . 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)  , energy detection (ED) BIB004    , matched filtering (MF) detection   BIB015 , cyclostationary detection (CSD) BIB003  BIB002 BIB011 , and some newly emerging methods such as eigenvalue-based sensing   BIB016 BIB031 BIB032  BIB033 , wavelet-based sensing BIB012 , covariancebased sensing  BIB017 BIB034 , and blindly combined energy detection BIB026 . 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 BIB001 BIB007 or a multiple-input multiple-output (MIMO) radar BIB008 BIB018 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 distributed 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 BIB005 BIB027 BIB019 BIB020 BIB028 BIB013 BIB021 BIB022 BIB023   . 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 BIB014   BIB035 BIB029  BIB030 BIB036 . 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 primary 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.