A Wideband Spectrum Sensing Method for Cognitive Radio using Sub-Nyquist Sampling

TL;DR

This paper introduces a wideband spectrum sensing method for cognitive radio that employs sub-Nyquist sampling to reduce data rates while reliably detecting spectrum occupancy even at low SNR levels.

Contribution

It presents a novel wideband sensing approach using sub-Nyquist sampling and a subspace estimator that does not require prior signal property knowledge.

Findings

Reliable detection at low SNR

Effective with small sample sizes

Reduces sampling rate requirements

Abstract

Spectrum sensing is a fundamental component in cognitive radio. A major challenge in this area is the requirement of a high sampling rate in the sensing of a wideband signal. In this paper a wideband spectrum sensing model is presented that utilizes a sub-Nyquist sampling scheme to bring substantial savings in terms of the sampling rate. The correlation matrix of a finite number of noisy samples is computed and used by a subspace estimator to detect the occupied and vacant channels of the spectrum. In contrast with common methods, the proposedmethod does not need the knowledge of signal properties that mitigates the uncertainty problem. We evaluate the performance of this method by computing the probability of detecting signal occupancy in terms of the number of samples and the SNR of randomly generated signals. The results show a reliable detection even in low SNR and small number of samples.