# A Compressive Method for Centralized PSD Map Construction with Imperfect Reporting Channel

## TL;DR

This paper proposes a robust distributed compressive sensing method for constructing PSD maps in cognitive radio networks, effectively handling imperfect reporting channels caused by fading and shadowing.

## Contribution

It introduces a novel distributed compressive sensing approach that compensates for transmission imperfections in PSD map construction.

## Key findings

- The proposed method maintains high reconstruction accuracy despite channel imperfections.
- Simulation results confirm the robustness of the scheme under various channel conditions.
- The approach improves spectrum management efficiency in cognitive radio networks.

## Abstract

Spectrum resources management of growing demands is a challenging problem and Cognitive Radio (CR) known to be capable of improving 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 collected by the distributed sensors in the area and fused by a Fusion Center (FC). But, for a given zone, the sensed PSDs 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 make the sensors transmission data more compressed. However, uncertain channel fading and random shadowing would lead to varying signal strength at different CRs, even placed close to each other. Hence, existence of some perturbations in the transmission procedure yields to some imperfection in the reporting channel and as a result it degrades the performance remarkably. The main focus of this paper is to be able to reconstruct the PSDs of sensors \textit{robustly} based on the Distributed Compressive Sensing (DCS) when the data transmission is slightly imperfect. Simulation results verify the robustness of the proposed scheme.