Cooperative Compressive Power Spectrum Estimation

TL;DR

This paper proposes a cooperative method for power spectrum estimation using multiple sensors that each estimate different correlation lags, reducing sampling rates and enabling accurate spectrum reconstruction through a fusion center.

Contribution

It introduces a novel cooperative framework that combines partial correlation estimates from sensor groups to efficiently estimate the power spectrum with reduced sampling.

Findings

System matrix can have full column rank under certain conditions

Fusion of diverse correlation estimates enables accurate power spectrum reconstruction

Reduces sampling rate requirements per sensor

Abstract

We examine power spectrum estimation from wide-sense stationary signals received at different wireless sensors. We organize multiple sensors into several groups, where each group estimates the temporal correlation only at particular lags, which are different from group to group. A fusion centre collects all the correlation estimates from different groups of sensors, and uses them to estimate the power spectrum. This reduces the required sampling rate per sensor. We further investigate the conditions required for the system matrix to have full column rank, which allows for a least-squares reconstruction method.