Adaptive spectrum hole detection using Sequential Compressive Sensing

Elzanati, Ahmed M.; Abdelkader, Mohamed F.; Seddik, Karim G.; Ghuniem, Atef M.

doi:10.1109/iwcmc.2014.6906505

Cited by 8 publications

(8 citation statements)

References 14 publications

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“…In the case of a soft cooperative scheme, the CS further helps to reduce the cooperative burden as well as the number of cooperative nodes and in the hard cooperative scheme, the CS is more useful for local sensing. Several works exist in the literature in the context of applying CS for cooperative sensing in centralized [64], [68], [71] and distributed [67], [69], [83], [124] settings. In Section III-B1 and Section III-B2, we provide a detailed discussion on the application of CS in centralized and distributed cooperative SS by referring to the current state of the art.…”

Section: Wideband Spectrum Sensingmentioning

confidence: 99%

“…Subsequently, the FC calculates the cross-spectra between all measurements and then the power spectrum of the received signals is estimated by exploiting the wide sense stationary property of the PU signals. Furthermore, authors in [68] propose an adaptive sequential CS approach to recover spectrum holes and further propose several fusion techniques to apply the proposed approach in a collaborative manner.…”

Section: ) Matrix Completion Problemmentioning

confidence: 99%

See 1 more Smart Citation

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%

Section: ) Matrix Completion Problemmentioning

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 centralized detection performance [72] could be further improved by formulating spectrum detection problem as support recovery. This improvement could be achieved using several fusion schemes such as decision, quantized data and data fusion in FC with no a prior knowledge of channel state condition and noise statistics.…”

Section: Detection Based Compressive Cooperative Schemesmentioning

confidence: 99%

“…On the other hand, Farrag et al [54] proposed algorithm based on distributed detection matrix targets to increase detection performance of PU with low sensing node complexity among clusters of sensing nodes rather than individuals. In contrast to [72] where each node sends its measurements as a sign vector only to FC. In [54], each sensing node uses its own local detection matrix to sense the wideband spectrum.…”

Section: Detection Based Compressive Cooperative Schemesmentioning

confidence: 99%

Wideband Cognitive Radio Networks Based Compressed Spectrum Sensing: A Survey

Abo-Zahhad¹,

Ahmed²,

Farrag³

et al. 2018

JSIP

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Spectrum sensing is a core function at cognitive radio systems to have spectrum awareness. This could be achieved by collecting samples from the frequency band under observation to make a conclusion whether the band is occupied, or it is a spectrum hole. The task of sensing is becoming more challenging especially at wideband spectrum scenario. The difficulty is due to conventional sampling rate theory which makes it infeasible to sample such very wide range of frequencies and the technical requirements are very costly. Recently, compressive sensing introduced itself as a pioneer solution that relaxed the wideband sampling rate requirements. It showed the ability to sample a signal below the Nyquist sampling rate and reconstructed it using very few measurements. In this paper, we discuss the approaches used for solving compressed spectrum sensing problem for wideband cognitive radio networks and how the problem is formulated and rendered to improve the detection performance.

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“…Several approaches were proposed to increase the effectiveness of the compressive spectrum sensing by exploiting additional redundancy in order to further reduce the required sampling rate. This includes using the spatial correlation in cooperative networks [8]- [10] or the geo-location database information [11].…”

Section: Introductionmentioning

confidence: 99%

Incorporating Primary Occupancy Patterns in Compressive Spectrum Sensing

2019

Self Cite

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Wideband spectrum sensing remains one of the challenging problems facing the wide deployment of cognitive radio networks. Compressive sensing (CS) was proposed as a promising approach to this problem by utilizing the sparse structure of the underutilized spectrum to capture the spectrum with fewer measurements and simpler hardware requirements. Most of the work in compressive spectrum sensing solely exploits the spatial-and frequency-domain structure of the spectrum neglecting the temporal structure arising from the regularity of primary user (PU) traffic patterns. In this paper, we explore the effectiveness of incorporating PU traffic patterns in compressive spectrum sensing. This achieves improved sensing performance by exploiting the statistics of the PU activity in the CS recovery algorithms. The experimental analysis through simulation shows that the proposed schemes can substantially improve the receiver operating characteristic performance at lower sampling rate noisy spectrum measurements.

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Adaptive spectrum hole detection using Sequential Compressive Sensing

Cited by 8 publications

References 14 publications

Application of Compressive Sensing in Cognitive Radio Communications: A Survey

Application of Compressive Sensing in Cognitive Radio Communications: A Survey

Wideband Cognitive Radio Networks Based Compressed Spectrum Sensing: A Survey

Incorporating Primary Occupancy Patterns in Compressive Spectrum Sensing

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