A Compressive Method for Centralized PSD Map Construction

TL;DR

This paper introduces a novel distributed compressive sensing method to efficiently compress and reconstruct power spectral density maps in cognitive radio networks, reducing data transmission overhead and improving spectrum utilization.

Contribution

It proposes a new compressive sensing approach leveraging shared components among sensors for better PSD map reconstruction under imperfect data transmission conditions.

Findings

Effective PSD compression demonstrated in simulations

Reduced data transmission overhead achieved

Improved PSD recovery accuracy

Abstract

Spectrum resources are facing huge demands and cognitive radio (CR) can improve the spectrum utilization. Recently, power spectral density (PSD) map is defined to enable the CR to reuse the frequency resources regarding to the area. For this reason, the sensed PSDs are fused by a Fusion Center (FC) which the sensed PSDs are collected by the distributed sensors in the area. But, for a given zone, the sensed PSD by neighbor CR sensors may contain a shared common component for a while. This component can be exploited in the theory of the distributed source coding (DSC) to compress sensing data more. In this paper based on the distributed compressive sensing (DCS) a method is proposed to compress and reconstruct the PSDs of the sensors when the data transmission is slightly imperfect. Simulation results show the advantages of using proposed method in compressing, reducing overhead and also recovering PSDs. % Proposed method can be used to develop a framework when the holding times of the users are large in comparison with the rate of the spectrum sensing.