Blind Orthogonal Least Squares based Compressive Spectrum Sensing

TL;DR

This paper introduces a blind orthogonal least squares algorithm for compressive spectrum sensing that does not require prior information like spectrum sparsity or noise variance, improving robustness and efficiency.

Contribution

It proposes a novel blind stopping rule for OLS in CSS, eliminating the need for prior spectrum or noise information, based only on mutual incoherence property.

Findings

The B-OLS-CSS achieves better SNR requirements than benchmark methods.

Theoretical analysis confirms relaxed SNR conditions for accurate sensing.

Simulation results verify the effectiveness of the proposed algorithm.

Abstract

As an enabling technique of cognitive radio (CR), compressive spectrum sensing (CSS) based on compressive sensing (CS) can detect the spectrum opportunities from wide frequency bands efficiently and accurately by using sub-Nyquist sampling rate. However, the sensing performance of most existing CSS excessively relies on the prior information such as spectrum sparsity or noise variance. Thus, a key challenge in practical CSS is how to work effectively even in the absence of such information. In this paper, we propose a blind orthogonal least squares based CSS algorithm (B-OLS-CSS), which functions properly without the requirement of prior information. Specifically, we develop a novel blind stopping rule for the OLS algorithm based on its probabilistic recovery condition. This innovative rule gets rid of the need of the spectrum sparsity or noise information, but only requires the computational-feasible mutual incoherence property of the given measurement matrix. Our theoretical analysis indicates that the signal-to-noise ratio required by the proposed B-OLS-CSS for achieving a certain sensing accuracy is relaxed than that by the benchmark CSS using the OMP algorithm, which is verified by extensive simulation results.