Compressive, collaborative spectrum sensing for wideband Cognitive Radios

Yenduri, Praveen K.; Gilbert, Anna C.

doi:10.1109/iswcs.2012.6328424

Cited by 8 publications

(7 citation statements)

References 17 publications

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“…The probability of excessive interference opportunity (EIO) is defined as the probability that the number of interference opportunities is greater than the desired level , i.e., (12) One example of the desired number of interference opportunities is , i.e., no interference opportunity to any channel is desired by the primary system. In this case, is exactly the probability of missed detection for wideband defined in [19]. Thus, and correspond to a broader range of performance metrics for wideband spectrum sensing.…”

Section: B Performance Metricsmentioning

confidence: 99%

“…Thus, the signals in the first bins are sufficient observations for spectrum sensing. The relationship between the frequency samples for IU and the frequency samples for wideband Nyquist sampling is (19) for , or equivalently, .…”

Section: Sampling Schemesmentioning

confidence: 99%

“…Instead, finding a fraction of them could be sufficient. In [19], the authors define the detection probability for wideband as the probability that all occupied channels are correctly detected, and the false alarm probability for wideband as the probability that any of the vacant channels are falsely detected as occupied. Other performance metrics proposed in the literature include, but are not limited to: the aggregated opportunistic throughput and the aggregated interference proposed in [20], the (empirical) probability of detecting a given number of ON channels [12], and so forth.…”

Section: Introductionmentioning

confidence: 99%

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Performance Metrics, Sampling Schemes, and Detection Algorithms for Wideband Spectrum Sensing

Sun

Laneman

2014

IEEE Trans. Signal Process.

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In this paper, we study the problem of wideband spectrum sensing for cognitive radio networks by partitioning it into four fundamental elements: system modeling, performance metrics, sampling schemes, and detection algorithms. Each element can potentially couple the individual channels so that designs for wideband spectrum sensing should consider the four elements jointly. We propose the -sparse model for the primary occupancies and study three uniform sampling schemes for wideband spectrum sensing, specifically, partial-band Nyquist sampling, sequential narrowband Nyquist sampling, and integer undersampling. We suggest new performance metrics more appropriate for wideband spectrum sensing, specifically, the probability of insufficient spectrum opportunity and the probability of excessive interference opportunity. We also develop detection algorithms that effectively tradeoff these metrics. Our results indicate that for performance metrics that couple the individual channels, multichannel detection algorithms have a significant advantage over channel-by-channel detection algorithms even for wideband Nyquist sampling. Furthermore, integer undersampling, which corresponds to the simplest sub-Nyquist sampling scheme, along with suitable detection algorithms exhibits appealing detection performance in the regime that better protects the primary system.

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Section: B Performance Metricsmentioning

confidence: 99%

Section: Sampling Schemesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Performance Metrics, Sampling Schemes, and Detection Algorithms for Wideband Spectrum Sensing

Sun

Laneman

2014

IEEE Trans. Signal Process.

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“…Existing sparse Fourier transform techniques have been applied to many signal processing problems including, e.g., GPS signal acquisition [16], analog-to-digital conversion [30,47], and wideband communication/spectrum sensing [20,46]. In all of these applications the signals under consideration are generally manmade and, therefore, structured in Fourier space.…”

Section: A General Class Of Functions With Structured Frequency Supportmentioning

confidence: 99%

A deterministic sparse FFT for functions with structured Fourier sparsity

2018

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“…Here, sparse FFT methods can be used for a faster computation, e.g. in the field of GHz-spectra [23] or cognitive radios which can detect vacant frequencies in order to use them for transmission [55]. Moreover, sparse FFT methods can be applied for 2D correlation spectroscopy of in vivo data, see [51].…”

Section: Introductionmentioning

confidence: 99%

Deterministic sparse FFT algorithms

Wannenwetsch

2015

Proc Appl Math and Mech

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In this paper we consider sparse signals ${\bf x}\in {\bf C}^N$ which are known to vanish outside a support interval of length bounded by m < N. For the case that m is known, we propose a deterministic algorithm of complexity ${\cal O}(m\log m)$ for reconstruction of x from its discrete Fourier transform $\widehat{\bf x}\in {\bf C}^N.$ (© 2015 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

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Compressive, collaborative spectrum sensing for wideband Cognitive Radios

Cited by 8 publications

References 17 publications

Performance Metrics, Sampling Schemes, and Detection Algorithms for Wideband Spectrum Sensing

Performance Metrics, Sampling Schemes, and Detection Algorithms for Wideband Spectrum Sensing

A deterministic sparse FFT for functions with structured Fourier sparsity

Deterministic sparse FFT algorithms

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