Distributed largest eigenvalue detection

Ainomae, Ahti; Trump, Tonu; Bengtsson, Mats

doi:10.1109/icassp.2017.7952811

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Because of the central processing unit, high communication overhead and enormous computing power are needed [ 13 ]. To solve these problems, decentralized cooperative sensing methods have been proposed, such as consensus algorithms [ 14 , 15 ], belief propagation [ 16 ] and diffusion methods [ 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…Some recent literature [ 17 , 18 , 19 ] has proposed distributed diffusion methods (diffusion least mean square (DLMS)), which allow each CU to collect data from its neighbors and make detection decisions based on these data without any central processing unit. As it is a distributed and adaptive diffusion scheme, DLMS has an ability to enhance network failure resistance.…”

Section: Introductionmentioning

confidence: 99%

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Diffusion Maximum Correntropy Criterion Based Robust Spectrum Sensing in Non-Gaussian Noise Environments

Zhao

et al. 2018

Entropy

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Spectrum sensing is the most important task in cognitive radio (CR). In this paper, a new robust distributed spectrum sensing approach, called diffusion maximum correntropy criterion (DMCC)-based robust spectrum sensing, is proposed for CR in the presence of non-Gaussian noise or impulsive noise. The proposed distributed scheme, which does not need any central processing unit, is characterized by an adaptive diffusion model. The maximum correntropy criterion, which is insensitive to impulsive interference, is introduced to deal with the effect of non-Gaussian noise. Simulation results show that the DMCC-based spectrum sensing algorithm has an excellent robust property with respect to non-Gaussian noise. It is also observed that the new method displays a considerably better detection performance than its predecessor (i.e., diffusion least mean square (DLMS)) in impulsive noise. Moreover, the mean and variance convergence analysis of the proposed algorithm are also carried out.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Diffusion Maximum Correntropy Criterion Based Robust Spectrum Sensing in Non-Gaussian Noise Environments

Zhao

et al. 2018

Entropy

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Distributed Adaptive Largest Eigenvalue Detection with SNR Weighted Observations

Ainomae

Trump

Moullec

2018

2018 16th Biennial Baltic Electronics Conference (BEC)

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The Improved Adaptive Silence Period Algorithm over Time-Variant Channels in the Cognitive Radio System

2018

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Abstract:In the field of cognitive radio spectrum sensing, the adaptive silence period management mechanism (ASPM) has improved the problem of the low time-resource utilization rate of the traditional silence period management mechanism (TSPM). However, in the case of the low signal-to-noise ratio (SNR), the ASPM algorithm will increase the probability of missed detection for the primary user (PU). Focusing on this problem, this paper proposes an improved adaptive silence period management (IA-SPM) algorithm which can adaptively adjust the sensing parameters of the current period in combination with the feedback information from the data communication with the sensing results of the previous period. The feedback information in the channel is achieved with frequency resources rather than time resources in order to adapt to the parameter change in the time-varying channel. The Monte Carlo simulation results show that the detection probability of the IA-SPM is 10-15% higher than that of the ASPM under low SNR conditions.

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Distributed largest eigenvalue detection

Cited by 3 publications

References 23 publications

Diffusion Maximum Correntropy Criterion Based Robust Spectrum Sensing in Non-Gaussian Noise Environments

Diffusion Maximum Correntropy Criterion Based Robust Spectrum Sensing in Non-Gaussian Noise Environments

Distributed Adaptive Largest Eigenvalue Detection with SNR Weighted Observations

The Improved Adaptive Silence Period Algorithm over Time-Variant Channels in the Cognitive Radio System

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