A NLLS Based Sub-Nyquist Rate Spectrum Sensing for Wideband Cognitive Radio

TL;DR

This paper introduces a sub-Nyquist rate spectrum sensing method for wideband cognitive radio using NLLS estimation, significantly reducing sampling rates while maintaining detection performance at moderate SNR levels.

Contribution

It presents a novel wideband spectrum sensing approach that employs sub-Nyquist sampling and NLLS estimation, with a new detection threshold expression and a low-complexity channel selection algorithm.

Findings

Achieves similar detection performance to Nyquist sampling at SNR above 4 dB.

Reduces sampling rate by a factor of 3 compared to conventional energy detection.

Applicable to both correlated and uncorrelated wideband signals.

Abstract

For systems and devices, such as cognitive radio and networks, that need to be aware of available frequency bands, spectrum sensing has an important role. 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 method 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 non-linear least square (NLLS) estimator to detect the occupied and vacant channels of the spectrum. We provide an expression for the detection threshold as a function of sampling parameters and noise power. Also, a sequential forward selection algorithm is presented to find the occupied channels with low complexity. The method can be applied to both correlated and uncorrelated wideband multichannel signals. A comparison with conventional energy detection using Nyquist-rate sampling shows that the proposed scheme can yield similar performance for SNR above 4 dB with a factor of 3 smaller sampling rate.