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52
Distributed compressive spectrum sensing in cooperative multihop cognitive networks
 IEEE Journal of Selected Topics in Signal Processing
, 2010
"... Abstract—In wideband cognitive radio (CR) networks, spectrum sensing is an essential task for enabling dynamic spectrum sharing, but entails several major technical challenges: very high sampling rates required for wideband processing, limited power and computing resources per CR, frequencyselectiv ..."
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Abstract—In wideband cognitive radio (CR) networks, spectrum sensing is an essential task for enabling dynamic spectrum sharing, but entails several major technical challenges: very high sampling rates required for wideband processing, limited power and computing resources per CR, frequencyselective wireless fading, and interference due to signal leakage from other coexisting CRs. In this paper, a cooperative approach to wideband spectrum sensing is developed to overcome these challenges. To effectively reduce the data acquisition costs, a compressive sampling mechanism is utilized which exploits the signal sparsity induced by network spectrum underutilization. To collect spatial diversity against wireless fading, multiple CRs collaborate during the sensing task by enforcing consensus among local spectral estimates; accordingly, a decentralized consensus optimization algorithm is derived to attain high sensing performance at a reasonable computational cost and power overhead. To identify spurious spectral estimates due to interfering CRs, the orthogonality between the spectrum of primary users and that of CRs is imposed as constraints for consensus optimization during distributed collaborative sensing. These decentralized techniques are developed for both cases of with and without channel knowledge. Simulations testify the effectiveness of the proposed cooperative sensing approach in multihop CR networks. Index Terms—Collaborative sensing, compressive sampling, consensus optimization, distributed fusion, spectrum sensing. I.
Valaee “Compressive Detection for WideBand Spectrum
 Sensing,” IEEE International Conference on Acoustics Speech and Signal Proc. (ICASSP
, 2010
"... In this paper we propose a novel wideband spectrum sensing scheme using compressive sensing. The wideband signal is fed into a number of wideband filters and the outputs of the filters are used to reconstruct the vector of channel energies through the compressive sensing’s ℓ1 norm minimization. A ..."
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In this paper we propose a novel wideband spectrum sensing scheme using compressive sensing. The wideband signal is fed into a number of wideband filters and the outputs of the filters are used to reconstruct the vector of channel energies through the compressive sensing’s ℓ1 norm minimization. An energy detection is then performed by comparing the obtained vector to a vector of energy thresholds to decide about the occupancy of each channel. Performance of the proposed approach is compared to the current wideband spectrum sensing algorithms as well as the conventional channelbychannel scanning method. Index Terms — compressive detection, compressive sensing, wideband spectrum sensing 1.
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.
Power spectrum blind sampling
 IEEE Signal Process. Lett
, 2011
"... Abstract—Power spectrum blind sampling (PSBS) consists of a sampling procedure and a reconstructionmethod that is capable of perfectly reconstructing the unknown power spectrum of a signal from the obtained samples. In this letter, we propose a solution to the PSBS problem based on a periodic sampli ..."
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Abstract—Power spectrum blind sampling (PSBS) consists of a sampling procedure and a reconstructionmethod that is capable of perfectly reconstructing the unknown power spectrum of a signal from the obtained samples. In this letter, we propose a solution to the PSBS problem based on a periodic sampling procedure and a simple least squares (LS) reconstruction method. For this PSBS technique, we derive the lowest possible average sampling rate, which is much lower than the Nyquist rate of the signal. Note the difference with spectrum blind sampling (SBS) where the goal is to perfectly reconstruct the spectrum and not the power spectrum of the signal, in which case subNyquist rate sampling is only possible if the spectrum is sparse. In the current work, we can perform subNyquist rate sampling without making any constraints on the power spectrum, because we try to reconstruct the power spectrum and not the spectrum. In many applications, such as spectrum sensing for cognitive radio, the power spectrum is of interest and estimating the spectrum is basically overkill. Index Terms—Cognitive radio, , compressive sampling, power spectrum estimation. I.
On the use of compressive sampling for wideband spectrum sensing
 Proc. IEEE Int. Symp. Signal Processing and Inf. Tech. (ISSPIT
, 2010
"... In a scenario where a cognitive radio unit wishes to transmit, it needs to know over which frequency bands it can operate. It can obtain this knowledge by estimating the power spectral density from a Nyquistrate sampled signal. For wideband signals sampling at the Nyquist rate is a major challenge ..."
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Cited by 7 (5 self)
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In a scenario where a cognitive radio unit wishes to transmit, it needs to know over which frequency bands it can operate. It can obtain this knowledge by estimating the power spectral density from a Nyquistrate sampled signal. For wideband signals sampling at the Nyquist rate is a major challenge and may be unfeasible. In this paper we accurately detect spectrum holes in subNyquist frequencies without assuming wide sense stationarity in the compressed sampled signal. A novel extension to further reduce the subNyquist samples is then presented by introducing a memory based compressed sensing that relies on the spectrum to be slowly varying. Index Terms — Compressive sampling, Cognitive radio, power spectrum estimation, subNyquist sampling.
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
MULTICOSET SAMPLING FOR POWER SPECTRUM BLIND SENSING
"... Power spectrum blind sampling (PSBS) consists of a sam pling procedure and a reconstruction method that is able to recover the unknown power spectrum of a random signal from the obtained subNyquistrate samples. It differs from spec trum blind sampling (SBS) that aims to recover the spectrum instea ..."
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Cited by 4 (1 self)
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Power spectrum blind sampling (PSBS) consists of a sam pling procedure and a reconstruction method that is able to recover the unknown power spectrum of a random signal from the obtained subNyquistrate samples. It differs from spec trum blind sampling (SBS) that aims to recover the spectrum instead of the power spectrum of the signal. In this paper, a PSBS solution is first presented based on a periodic sampling procedure. Then, a multicoset implementation for this sam pling procedure is developed by solving the socalled minimal sparse ruler problem, and the coprime sampling technique is tailored to fit into the PSBS framework as well. It is shown that the proposed multicoset implementation based on mini mal sparse rulers offers advantages over coprime sampling in terms of reduced sampling rates, increased flexibility and an extended range of estimated autocorrelation lags. These ben efits arise without putting any sparsity constraint on the power spectrum. Application to sparse power spectrum recovery is also illustrated. Index Terms Multicoset sampling, sparse ruler, co prime sampling
Enhanced compressive wideband frequency spectrum sensing for dynamic spectrum access
 EURASIP Journal on Advances in Signal Processing
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Edge based Wideband Sensing for Cognitive Radio: Algorithm and Performance Evaluation
"... Abstract — Since a cognitive radio does not have fixed spectra, it may need to sense a very large frequency range to find an available band. The sensed aggregate bandwidth could be as large as several GHz. This is especially challenging if the center frequencies and bandwidths of the sensed signals ..."
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Abstract — Since a cognitive radio does not have fixed spectra, it may need to sense a very large frequency range to find an available band. The sensed aggregate bandwidth could be as large as several GHz. This is especially challenging if the center frequencies and bandwidths of the sensed signals are unknown and need to be detected. In this paper, an edge based wideband sensing is proposed. The method first uses the product of wavelet transforms at different scales to detect the edges (sharp changing points) of the power spectral density (PSD) of the received signal. It then forms the possible bands based on the detected edges. Thereafter, it applies a multiband detection scheme to classify the bands as occupied or vacant. Finally, the signal to noise ratio (SNR) of each occupied band is estimated. Performance evaluation is also a complicated issue for wideband sensing. Other than the conventional metrics as probability of detection and probability of false alarm, three new criteria are proposed to evaluate the performance of a wideband sensing. Simulations are provided to verify the methods. Key words: Cognitive radio, spectrum sensing, wideband sensing, multiband sensing, subcarrier sensing, signal detection, edge detection, wavelet I.