A Self-Adaptive Progressive Support Selection Scheme for Collaborative Wideband Spectrum Sensing.

Zhuhua Hu,Yaochi Zhao,Mingshan Xie,Mengxing Huang,Yong Bai

doi:10.3390/s18093011

Abstract

The sampling rate of wideband spectrum sensing for sparse signals can be reduced by sub-Nyquist sampling with a Modulated Wideband Converter (MWC). In collaborative spectrum sensing, the fusion center recovers the spectral support from observation and measurement matrices reported by a network of CRs, to improve the precision of spectrum sensing. However, the MWC has a very high hardware complexity due to its parallel structure; it sets a fixed threshold for a decision without considering the impact of noise intensity, and needs a priori information of signal sparsity order for signal support recovery. To address these shortcomings, we propose a progressive support selection based self-adaptive distributed MWC sensing scheme (PSS-SaDMWC). In the proposed scheme, the parallel hardware sensing channels are scattered on secondary users (SUs), and the PSS-SaDMWC scheme takes sparsity order estimation, noise intensity, and transmission loss into account in the fusion center. More importantly, the proposed scheme uses a support selection strategy based on a progressive operation to reduce missed detection probability under low SNR levels. Numerical simulations demonstrate that, compared with the traditional support selection schemes, our proposed scheme can achieve a higher support recovery success rate, lower sampling rate, and stronger time-varying support recovery ability without increasing hardware complexity.

Highlights

Spectrum resources have become increasingly scarce with emerging wireless services.assigned radio spectrums to authorized users are mostly underutilized
(1) The recovery success rate of support: When Pf is less than or equal to the upper bound, we refer to the successful recovery criteria in [16]; that is, when the estimated support Λand the actual support Λ meet the constraint condition given by Equation (22), where Λ ⊇ Λ, and Φ↓Λis with full column rank, it is considered a successful reconstruction
Where kΛk0 10 is the potential of the recovery reconstructed spectral that is,PSS-SaDMWC

Summary

Introduction

Spectrum resources have become increasingly scarce with emerging wireless services.assigned radio spectrums to authorized users are mostly underutilized. Spectrum resources have become increasingly scarce with emerging wireless services. As a solution to this problem, cognitive radio (CR) technology can reuse spectrum resources by utilizing spectrum sensing to intelligently recognize idle frequency bands [1]. Traditional spectrum sensing methods, such as energy detection [2], cyclostationary feature detection [3], and matched filter detection [4], mainly exploit spectral opportunities over a narrow frequency range. The research of wideband compressed spectrum sensing (WCSS) is motivated by the desire to support wireless multimedia communications in CR networks [5,6]. In WCSS, compressed sensing (CS) theory [7,8] can be applied to reduce the sampling rate and the hardware complexity of the CR transceivers, such as by using a wideband antenna, wideband filter, and high speed analogue-to-digital converter (ADC)

Methods

Results

Discussion

Conclusion