Wideband Spectrum Sensing Based on Sub-Nyquist Sampling

Yen, Chia-Pang; Tsai, Yingming; Wang, Xiaodong

doi:10.1109/tsp.2013.2251342

Cited by 124 publications

(80 citation statements)

References 30 publications

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“…The problem of sampling a signal at the minimal rate and reconstructing the original spectrum from the compressive measurements has been discussed in [111]- [113]. 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.…”

Section: Wideband Spectrum Sensingmentioning

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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Section: Wideband Spectrum Sensingmentioning

confidence: 99%

Application of Compressive Sensing in Cognitive Radio Communications: A Survey

Sharma

Lagunas

Chatzinotas

et al. 2016

IEEE Commun. Surv. Tutorials

210

112

View full text Add to dashboard Cite

show abstract

“…The proposition and development of compressed sensing (CS), provide a new scheme for wideband spectrum sensing with low-speed sampling rate [4]. Classical reconstruction algorithms are applied to wideband spectrum sensing for single node [5] [6] [7], which can recovery signal with sub-Nyquist rate samples. To overcome the negative influence of wireless fading, many compressed wideband sensing methods in cooperative cognitive radio networks have been developed [8][9] [10].…”

Section: Introductionmentioning

confidence: 99%

Wideband Spectrum Compressed Blind Sensing without Reconstruction Based on Higher-order Moment

Jiao¹,

Li²,

Wang³

2017

Proceedings of the 3rd International Conference on Wireless Communication and Sensor Networks (WCSN 2016)

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Abstract-To improve the detection performance and detection speed of wideband spectrum sensing algorithms with little priori knowledge, a scheme of cooperative wideband compressed blind sensing without reconstruction based on higher-order moment (CWCBS-HOM for short) is proposed. The test statistic is extracted from third-order moment of the compressed samples. The proposed method is blind in the sense since it need neither signal reconstruction, nor the prior knowledge of the primary user (PU) signal. Theoretical analysis and simulation results show that the proposed method can enhance the spectrum sensing capability with low computational complexity and high speed.

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“…Covariance estimation has been widely considered across different fields of statistical signal processing, such as power spectrum estimation [1], [2], [3], [4], [5], economics and financial time series analysis [6], machine learning [7], [8], and phaseless measurements [3], [9]. In all these examples, secondorder statistics suffice for the task at hand and estimation of the signal itself is unnecessary.…”

Section: Introductionmentioning

confidence: 99%

Universal lower bounds on sampling rates for covariance estimation

Cohen

Eldar

Leus

2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Abstract-Covariance estimation from compressive samples has become particularly attractive for two main reasons. First, many applications do not require the signal itself, and secondorder statistics are oftentimes sufficient. The resulting requirement on the sampling rate of the original signal can therefore be reduced. Second, signal recovery from compressive samples leads to underdetermined systems which require additional constraints, such as the popular sparsity assumption. In contrast, covariance estimation can yield overdetermined problems, even from compressive samples, so that the additional constraints on the signal can be dropped. In this paper, we provide a unified framework for deriving lower bounds on the sampling rate required for covariance estimation of stationary signals, by deriving the lower Beurling density of the difference set associated with the original sampling set. A general sampling scheme is first considered, followed by the analysis of multicoset sampling. We prove that, in both cases, the sampling rate can be arbitrarily low, as was remarked extensively in the literature.

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Wideband Spectrum Sensing Based on Sub-Nyquist Sampling

Cited by 124 publications

References 30 publications

Application of Compressive Sensing in Cognitive Radio Communications: A Survey

Application of Compressive Sensing in Cognitive Radio Communications: A Survey

Wideband Spectrum Compressed Blind Sensing without Reconstruction Based on Higher-order Moment

Universal lower bounds on sampling rates for covariance estimation

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