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WIDEBAND SPECTRUM SENSING FOR COGNITIVE RADIO NETWORKS: A SURVEY
"... Cognitive radio has emerged as one of the most promising candidate solutions to improve spectrum utilization in next generation cellular networks. A crucial requirement for future cognitive radio networks is wideband spectrum sensing: secondary users reliably detect spectral opportunities across a w ..."
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Cognitive radio has emerged as one of the most promising candidate solutions to improve spectrum utilization in next generation cellular networks. A crucial requirement for future cognitive radio networks is wideband spectrum sensing: secondary users reliably detect spectral opportunities across a wide frequency range. In this article, various wideband spectrum sensing algorithms are presented, together with a discussion of the pros and cons of each algorithm and the challenging issues. Special attention is paid to the use of subNyquist techniques, including compressive sensing and multichannel subNyquist sampling techniques.
1 Frugal Sensing: Wideband Power Spectrum Sensing from Few Bits
"... Abstract—Wideband spectrum sensing is a key requirement for cognitive radio access. It now appears increasingly likely that spectrum sensing will be performed using networks of sensors, or crowdsourced to handheld mobile devices. Here, a network sensing scenario is considered, where scattered lowe ..."
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Abstract—Wideband spectrum sensing is a key requirement for cognitive radio access. It now appears increasingly likely that spectrum sensing will be performed using networks of sensors, or crowdsourced to handheld mobile devices. Here, a network sensing scenario is considered, where scattered lowend sensors filter and measure the average signal power across a band of interest, and each sensor communicates a single bit (or coarsely quantized level) to a fusion center, depending on whether its measurement is above a certain threshold. The focus is on the underdetermined case, where relatively few bits are available at the fusion center. Exploiting nonnegativity and the linear relationship between the power spectrum and the autocorrelation, it is shown that adequate power spectrum sensing is possible from few bits, even for dense spectra. The formulation can be viewed as generalizing classical nonparametric power spectrum estimation to the case where the data is in the form of inequalities, rather than equalities.
Joint Spectrum Sensing and Access Evolutionary Game in Cognitive Radio Networks
"... Abstract—Many spectrum sensing methods and dynamic access algorithms have been proposed to improve the secondary users ’ opportunities of utilizing the primary users ’ spectrum resources. However, few of them have considered to integrate the design of spectrum sensing and access algorithms together ..."
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Abstract—Many spectrum sensing methods and dynamic access algorithms have been proposed to improve the secondary users ’ opportunities of utilizing the primary users ’ spectrum resources. However, few of them have considered to integrate the design of spectrum sensing and access algorithms together by taking into account the mutual influence between them. In this paper, we propose to jointly analyze the spectrum sensing and access problem by studying two scenarios: synchronous scenario where the primary network is slotted and nonslotted asynchronous scenario. Due to selfish nature, secondary users tend to act selfishly to access the channel without contribution to the spectrum sensing. Moreover, they may take outofequilibrium strategies because of the uncertainty of others ’ strategies. To model the complicated interactions among secondary users, we formulate the joint spectrum sensing and access problem as an evolutionary game and derive the evolutionarily stable strategy (ESS) that no one will deviate from. Furthermore, we design a distributed learning algorithm for the secondary users to converge to the ESS. With the proposed algorithm, each secondary user senses and accesses the primary channel with the probabilities learned purely from its own past utility history, and finally achieves the desired ESS. Simulation results shows that our system can quickly converge to the ESS and such an ESS is robust to the sudden unfavorable deviations of the selfish secondary users. Index Terms—Cognitive radio, joint spectrum sensing and access, evolutionary game theory, replicator dynamics. I.
Sparsity Order Estimation and its Application in Compressive Spectrum Sensing for Cognitive Radios
"... Abstract—Compressive sampling techniques can effectively reduce the acquisition costs of highdimensional signals by utilizing the fact that typical signals of interest are often sparse in a certain domain. For compressive samplers, the number of samples Mr needed to reconstruct a sparse signal is d ..."
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Abstract—Compressive sampling techniques can effectively reduce the acquisition costs of highdimensional signals by utilizing the fact that typical signals of interest are often sparse in a certain domain. For compressive samplers, the number of samples Mr needed to reconstruct a sparse signal is determined by the actual sparsity order Snz of the signal, which can be much smaller than the signal dimension N. However,Snz is often unknown or dynamically varying in practice, and the practical sampling rate has to be chosen conservatively according to an upper bound Smax of the actual sparsity order in lieu of Snz, which can be unnecessarily high. To circumvent such wastage of the sampling resources, this paper introduces the concept of sparsity order estimation, which aims to accurately acquire Snz prior to sparse signal recovery, by using a very small number of samples Me less than Mr. A statistical learning methodology is used to quantify
Collecting Detection Diversity and Complexity Gains in Cooperative Spectrum Sensing
"... Abstract—In cognitive radio (CR) networks, multiCR cooperation is required during spectrum sensing in order to cope with wireless fading effects and the hidden terminal problem. User cooperation offers not only channel diversity gain against fading, but also complexity gain in terms of reduced samp ..."
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Abstract—In cognitive radio (CR) networks, multiCR cooperation is required during spectrum sensing in order to cope with wireless fading effects and the hidden terminal problem. User cooperation offers not only channel diversity gain against fading, but also complexity gain in terms of reduced sampling costs per CR. The latter is particularly useful when the monitored spectrum has very wide bandwidth and yet individual CRs only have limited hardware capability. To jointly collect both diversity gain and complexity gain, this paper develops a novel cooperative spectrum sensing technique based on matrix rank minimization. Subject to samplingrate limitations, CRs individually collect digital measurements from a segment of the wide spectrum via coordinated selective filtering, with optional compressive sampling to further reduce the sampling rates. The solutions representing the measurements of all users are modeled to possess a lowrank property, and the rank order is the same as the size of the nonzero support of the monitored wide spectrum. Accordingly, a nuclear norm minimization problem is formulated to jointly identify the nonzero support and hence the overall wideband spectrum occupancy. Both tradeoff evaluation and simulation results corroborate that the proposed cooperative sensing technique outperforms traditional averagingbased cooperative schemes given the same sampling costs, because the lowrank property enables efficient utilization and tradeoff of the user diversity in the absence of any channel knowledge. Index Terms—Cooperative spectrum sensing, support detection, lowrank property, matrix rank minimization, cognitive radio. I.
Parametric frugal sensing of power spectra for moving average models
 IEEE Trans. Signal Process
, 2015
"... Abstract—Wideband spectrum sensing is a fundamental component of cognitive radio and other applications. A novel frugal sensing schemewas recently proposed as ameans of crowdsourcing the task of spectrum sensing. Using a network of scattered lowend sensors transmitting randomly filtered power meas ..."
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Abstract—Wideband spectrum sensing is a fundamental component of cognitive radio and other applications. A novel frugal sensing schemewas recently proposed as ameans of crowdsourcing the task of spectrum sensing. Using a network of scattered lowend sensors transmitting randomly filtered power measurement bits to a fusion center, a nonparametric approach to spectral estimation was adopted to estimate the ambient power spectrum. Here, a parametric spectral estimation approach is considered within the context of frugal sensing. Assuming a MovingAverage (MA) representation for the signal of interest, the problem of estimating admissible MA parameters, and thus the MA power spectrum, from single bit quantized data is formulated. This turns out being a nonconvex quadratically constrained quadratic program (QCQP), which is NP–Hard in general. Approximate solutions can be obtained via semidefinite relaxation (SDR) followed by randomization; but this rarely produces a feasible solution for this particular kind of QCQP. A new Sequential Parametric Convex Approximation (SPCA) method is proposed for this purpose, which can be initialized from an infeasible starting point, and yet still produce a feasible point for the QCQP, when one exists, with high probability. Simulations not only reveal the superior performance of the parametric techniques over the globally optimum solutions obtained from the nonparametric formulation, but also the better performance of the SPCA algorithm over the SDR technique. Index Terms—Cognitive radio, distributed spectrum sensing, parametric spectral analysis, movingaverage processes, quantization, quadratically constrained quadratic programming (QCQP), semidefinite programming (SDP) relaxation. I.
Maximum Likelihood Passive and Active Sensing of Wideband Power Spectra From Few Bits
"... Abstract—Wideband power spectrum sensing is essential for cognitive radio and many other applications. Aiming to crowdsource spectrum sensing operations, a novel frugal sensing framework was recently proposed, employing a network of low dutycycle sensors (e.g., running in background mode on consum ..."
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Abstract—Wideband power spectrum sensing is essential for cognitive radio and many other applications. Aiming to crowdsource spectrum sensing operations, a novel frugal sensing framework was recently proposed, employing a network of low dutycycle sensors (e.g., running in background mode on consumer devices) reporting randomly filtered broadband power measurement bits to a fusion center, which in turn estimates the ambient power spectrum. Frugal sensing is revisited here from a statistical estimation point of view. Taking into account fading and insufficient sample averaging considerations, maximum likelihood (ML) formulations are developed which outperform the original minimum power and interior point solutions when the soft power estimates prior to thresholding are noisy. Assuming availability of a downlink channel that the fusion center can use to send threshold information, active sensing strategies are developed that quickly narrow down and track the power spectrum estimate, using ideas borrowed from cutting plane methods to develop active ML solutions. Simulations show that satisfactory wideband power spectrum estimates can be obtained with passive ML sensing from few bits, and much better performance can be attained using active sensing. Various other aspects, such as known emitter spectral shapes and different types of nonnegativity constraints, are also considered. Index Terms—Cognitive radio, collaborative sensing, spectral analysis, spectrum sensing. I.
A Practical Subspace Multiple Measurement Vectors Algorithm for Cooperative Spectrum Sensing
"... Abstract—Cooperative spectrum sensing (CSS) in cognitive radio networks conducts cooperation among sensing users to jointly sense the sparse spectrum and utilize available spectrums. Greedy multiple measurement vectors (MMVs) algorithm in the context of compressed sensing can ideally model the wideb ..."
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Abstract—Cooperative spectrum sensing (CSS) in cognitive radio networks conducts cooperation among sensing users to jointly sense the sparse spectrum and utilize available spectrums. Greedy multiple measurement vectors (MMVs) algorithm in the context of compressed sensing can ideally model the wideband CSS scenario to efficiently solve the support detection problem for identification of occupied channels. Actually, the number of sparsity is unknown, and most of greedy algorithms for MMVs lack for a (robust) stopping criterion of determining when the greedy algorithm should terminate. In this paper, we analyze and derive oracle stopping bounds for greedy MMVs algorithms without depending on prior information such as sparsity. Moreover, we introduce a practical subspace MMVs greedy algorithm that extends from a subspacebased sparse recovery method to a more practical setting, in which no prior information are required. Extensive simulations confirm the feasibility of the proposed stopping criteria and our sparse recovery algorithm.
Joint Wideband Spectrum Sensing in Frequency Overlapping Cognitive Radio Networks Using Distributed Compressive Sensing
"... Abstract—The emerging paradigm of open spectrum market calls for quick, efficient and dynamic approach for spectrum sensing. Conventional spectrum sensing methods for cognitive radios are capitalized over the narrow band sensing without addressing the wideband spectrum sensing. In wideband networks, ..."
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Abstract—The emerging paradigm of open spectrum market calls for quick, efficient and dynamic approach for spectrum sensing. Conventional spectrum sensing methods for cognitive radios are capitalized over the narrow band sensing without addressing the wideband spectrum sensing. In wideband networks, one by one scanning of spectrum is unattractive because of its complexity, agility constraints, and data acquisition cost. Existing wideband spectrum sensing schemes do not exploit the gain of joint sparsity in the frequency overlapping networks. In this paper, wideband spectrum sensing for frequency overlapping cognitive radio networks using the emerging compressive sensing paradigm and joint reconstruction is proposed. Simulation results verify the effectiveness of proposed joint spectrum sensing approach in jointly sparse frequency overlapping cognitive radio networks. I.