Optimal Spectral Feature Detection for Spectrum Sensing at Very Low SNR

Quan, Zhi; Zhang, Wenyi; Shellhammer, Stephen J.; Sayed, Ali H.

doi:10.1109/tcomm.2010.112310.090306

Cited by 67 publications

(54 citation statements)

References 17 publications

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“…The performance gains of the new detectors are in the range between 2 dB and 6 dB near the SNR value of -20 dB. These are significant in this operating region [23], [24]. Fig.…”

Section: Numerical Results and Discussionmentioning

confidence: 83%

Novel <inline-formula> <tex-math notation="TeX">${\rm S}\alpha{\rm S} $</tex-math></inline-formula> PDF Approximations and Their Applications in Wireless Signal Detection

Chen

2015

IEEE Trans. Wireless Commun.

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“…The performance gains of the new detectors are in the range between 2 dB and 6 dB near the SNR value of -20 dB. These are significant in this operating region [23], [24]. Fig.…”

Section: Numerical Results and Discussionmentioning

confidence: 83%

Novel <inline-formula> <tex-math notation="TeX">${\rm S}\alpha{\rm S} $</tex-math></inline-formula> PDF Approximations and Their Applications in Wireless Signal Detection

Chen

2015

IEEE Trans. Wireless Commun.

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“…This is not surprising, as the correlation matching behaves as an equivalence to ML in the low SNR regime [33]. Similarly, the recent work [8] has shown that exploiting spectral features, i.e., a second order statistic of the legacy signal, is optimal for asymptotically low SNR regimes.…”

Section: A Glrt Signal Detectormentioning

confidence: 84%

Nonuniform Sampling Walls in Wideband Signal Detection

Font-Segura

Vazquez

Riba

2014

IEEE Trans. Signal Process.

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Abstract-This work shows the existence of sampling walls in detection of wideband signals from Bernoulli nonuniform sampling (BNS) in the presence of noise uncertainty. A sampling wall is defined as the sampling density below which the target error probabilities, i.e., the missed detection and false alarm probabilities, cannot be guaranteed at a given signal to noise ratio (SNR) regardless the number of acquired samples. The BNS is adopted because it exhibits good tradeoff properties between complexity and performance. It is shown that BNS suffers from noise enhancement, which translates into a whitening effect in the correlation of the legacy signal. Contrarily to the existing literature, the signal detection problem is addressed without having to reconstruct neither the signal nor its spectrum. More specifically, the optimal low SNR detector is formulated as a generalized likelihood ratio test (GLRT) to exploit the available side information of the problem, i.e., the noise variance, the sampling density and the legacy signal autocorrelation. By deriving the asymptotic performance of the GLRT in the presence of noise uncertainty, explicit expressions for sampling walls are obtained as a function of the legacy signal occupancy, the SNR and the noise uncertainty. Further, numerical results are provided to assess the behavior of the sampling walls and signal detection performance.

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“…Further, power spectrum estimation methods based on sub-Nyquist rate samples were presented in [114], [115], where the spectrum of the uncompressed signal is retrieved by concentrating on the autocorrelation function instead of the original signal itself. Moreover, CS-based correlation matching approaches for identification of the PUs were presented in [116]- [118] in the context of a CR.…”

Section: Wideband Spectrum Sensingmentioning

confidence: 99%

“…Cyclostationary detection [57] and correlation matching detection [116]- [118] are particularly appealing because of their ability to distinguish the primary signal from the interference and noise. They can work in a very low SNR region due to their noise rejection capability but sometimes they are computationally complex and requires significantly long observation time.…”

Section: Comparison Of Cs and Non-cs Detectorsmentioning

confidence: 99%

Application of Compressive Sensing in Cognitive Radio Communications: A Survey

Sharma

Lagunas

Chatzinotas

et al. 2016

IEEE Commun. Surv. Tutorials

210

112

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Abstract-Compressive Sensing (CS) has received much attention in several fields such as digital image processing, wireless channel estimation, radar imaging, and Cognitive Radio (CR) communications. Out of these areas, this survey paper focuses on the application of CS in CR communications. Due to the underutilization of the allocated radio spectrum, spectrum occupancy is usually sparse in different domains such as time, frequency and space. Such a sparse nature of the spectrum occupancy has inspired the application of CS in CR communications. In this regard, several researchers have already applied the CS theory in various settings considering the sparsity in different domains. In this direction, this survey paper provides a detailed review of the state of the art related to the application of CS in CR communications. Starting with the basic principles and the main features of CS, it provides a classification of the main usage areas based on the radio parameter to be acquired by a wideband CR. Subsequently, we review the existing CSrelated works applied to different categories such as wideband sensing, signal parameter estimation and Radio Environment Map (REM) construction, highlighting the main benefits and the related issues. Furthermore, we present a generalized framework for constructing the REM in compressive settings. Finally, we conclude this survey paper with some suggested open research challenges and future directions.

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Optimal Spectral Feature Detection for Spectrum Sensing at Very Low SNR

Cited by 67 publications

References 17 publications

Novel <inline-formula> <tex-math notation="TeX">${\rm S}\alpha{\rm S} $</tex-math></inline-formula> PDF Approximations and Their Applications in Wireless Signal Detection

Novel <inline-formula> <tex-math notation="TeX">${\rm S}\alpha{\rm S} $</tex-math></inline-formula> PDF Approximations and Their Applications in Wireless Signal Detection

Nonuniform Sampling Walls in Wideband Signal Detection

Application of Compressive Sensing in Cognitive Radio Communications: A Survey

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