Exploiting wideband spectrum occupancy heterogeneity for weighted compressive spectrum sensing

TL;DR

This paper introduces a weighted $ ext{l}_1$-minimization method for compressive wideband spectrum sensing that leverages the inherent heterogeneity and block-like structure of spectrum occupancy to improve recovery stability and performance.

Contribution

It proposes a novel weighted $ ext{l}_1$-minimization algorithm tailored for heterogeneous spectrum, outperforming existing methods in stability and accuracy.

Findings

Better spectrum recovery stability compared to existing methods

Enhanced performance in heterogeneous spectrum environments

Effective exploitation of spectrum occupancy block structure

Abstract

Compressive sampling has shown great potential for making wideband spectrum sensing possible at sub-Nyquist sampling rates. As a result, there have recently been research efforts that aimed to develop techniques that leverage compressive sampling to enable compressed wideband spectrum sensing. These techniques consider homogeneous wideband spectrum where all bands are assumed to have similar PU traffic characteristics. In practice, however, wideband spectrum is not homogeneous, in that different spectrum bands could have different PU occupancy patterns. In fact, the nature of spectrum assignment, in which applications of similar types are often assigned bands within the same block, dictates that wideband spectrum is indeed heterogeneous, as different application types exhibit different behaviors. In this paper, we consider heterogeneous wideband spectrum, where we exploit this inherent, block-like structure of wideband spectrum to design efficient compressive spectrum sensing techniques that are well suited for heterogeneous wideband spectrum. We propose a weighted $\ell_1$-minimization sensing information recovery algorithm that achieves more stable recovery than that achieved by existing approaches while accounting for the variations of spectrum occupancy across both the time and frequency dimensions. Through intensive numerical simulations, we show that our approach achieves better performance when compared to the state-of-the-art approaches.