Windowed Compressed Spectrum Sensing with Block sparsity

TL;DR

This paper investigates the impact of spectral leakage on compressed spectrum sensing and introduces a block-sparse model with windowing techniques to improve signal reconstruction, balancing leakage suppression and measurement matrix stability.

Contribution

It proposes a novel block-sparse CSS model considering windowing effects, introduces EZC and WSC metrics, and analyzes the trade-offs affecting RIP and reconstruction stability.

Findings

Window functions reduce spectral leakage.

Excessively small EZC and WSC impair RIP and stability.

Block-sparse approach enhances reconstruction in noisy, super-sparse signals.

Abstract

Compressed Spectrum Sensing (CSS) is widely employed in spectral analysis due to its sampling efficiency. However, conventional CSS assumes a standard sparse spectrum, which is affected by Spectral Leakage (SL). Despite the widespread use of CSS, the impact of SL on its performance has not been systematically and thoroughly investigated. This study addresses this research gap by analyzing the Restricted Isometry Property (RIP) of windowed Gaussian measurement matrices and proposing a novel block-sparse CSS model. We introduce the Edge Zeroing Coefficient (EZC) to evaluate SL suppression and RIP impact, and the Window Scaling Coefficient (WSC) to quantify the effect on RIP. Our research investigates the influence of Window Function (WF) on signal sparsity and measurement matrices, and presents a block-sparse CSS model that considers component frequency distribution, signal length, windowing, and noise floor. Based on subspace counting theory, we derive sample bound for our model. The findings demonstrate that while WFs reduce SL, excessively small EZC and WSC values can negatively affect RIP quality and cause numerical instability during signal reconstruction. This highlights the delicate balance required when applying WFs in CSS. Our block-sparse approach enables precise compression and reconstruction, particularly for high noise floor and super-sparse signals. This study provides a framework for optimizing CSS performance when dealing with SL and sparse signals, offering insights for improving signal reconstruction quality in various applications