Ultra Low-Complexity Detection of Spectrum Holes in Compressed Wideband Spectrum Sensing

TL;DR

This paper introduces a low-complexity, sub-Nyquist sampling method for detecting spectrum holes in wideband spectrum sensing, reducing hardware and computational requirements in cognitive radios.

Contribution

It proposes a novel zero-block detection scheme using a block sparse sensing matrix and an analog-to-information converter, focusing on detecting vacant spectrum segments efficiently.

Findings

Effective detection of spectrum holes at very low undersampling ratios

Significantly lower complexity compared to existing methods

Validated through analytical and simulation results

Abstract

Wideband spectrum sensing is a significant challenge in cognitive radios (CRs) due to requiring very high-speed analog- to-digital converters (ADCs), operating at or above the Nyquist rate. Here, we propose a very low-complexity zero-block detection scheme that can detect a large fraction of spectrum holes from the sub-Nyquist samples, even when the undersampling ratio is very small. The scheme is based on a block sparse sensing matrix, which is implemented through the design of a novel analog-to- information converter (AIC). The proposed scheme identifies some measurements as being zero and then verifies the sub-channels associated with them as being vacant. Analytical and simulation results are presented that demonstrate the effectiveness of the proposed method in reliable detection of spectrum holes with complexity much lower than existing schemes. This work also introduces a new paradigm in compressed sensing where one is interested in reliable detection of (some of the) zero blocks rather than the recovery of the whole block sparse signal.