Compressive Spectrum Sensing with Temporal-Correlated Prior Knowledge Mining

Jianfeng Liu,Xin-Lin Huang,Ping Wang,Xin Liu

doi:10.1155/2021/5539697

Jianfeng Liu, Xin-Lin Huang + Show 2 more

Open Access

https://doi.org/10.1155/2021/5539697

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Abstract

Cognitive radio (CR) has been proposed to mitigate the spectrum scarcity issue to support heavy wireless services on sub-3GHz. Recently, broadband spectrum sensing becomes a hot topic with the help of compressive sensing technology, which will reduce the high-speed sampling rate requirement of analog-to-digital converter. This paper considers sequential compressive spectrum sensing, where the temporal correlation information between neighboring compressive sensing data will be exploited. Different from conventional compressive sensing, the previous compressive sensing data will be fused into prior knowledge in current spectrum estimation. The simulation results show that the proposed scheme can achieve 98.7% detection probability under 3.5% false alarm probability and performs the best compared with the typical BPDN and OMP schemes.

Highlights

In recent years, with the explosive growth of wireless devices, the scarcity of spectrum has become an important challenge to support high-speed services to numerous users
In spectrum sensing, the spectrum in the previous time slot is very similar to the spectrum in the current time slot, so previous spectrum can be used as the prior information for current spectrum estimation
We have proposed a compressive spectrum sensing scheme for CRN with temporal-correlated prior knowledge mining

Summary

Introduction

With the explosive growth of wireless devices, the scarcity of spectrum has become an important challenge to support high-speed services to numerous users In this case, cognitive radio (CR) is considered as an important solution to the problem of spectrum scarcity. The concept of cognitive radio was firstly proposed in the reference [1] in the late 1990s This technology can alleviate the contradiction between scarce spectrum resources and a large number of wireless services. This paper proposes a convex optimization model to reconstruct the target spectrum by maximizing the inner product of the prior spectrum and the target spectrum and minimizing the l1-norm of the target spectrum We adopt this CS-based model in spectrum sensing, and the number of sampling rate is reduced with compressive sensing technology.

Cognitive Radio Compressive Spectrum Sensing

Spectrum Sensing Scheme with TemporalCorrelated Prior Knowledge

Method MC

Performance Analysis

Findings

Conclusions and Future Works