Moment-based Spectrum Sensing Under Generalized Noise Channels

TL;DR

This paper introduces a moment-based spectrum sensing detector that operates under generalized noise modeled by the McLeish distribution, providing accurate performance metrics and outperforming traditional energy detectors in noise uncertainty scenarios.

Contribution

It proposes a novel blind spectrum sensing method using the McLeish distribution, with closed-form performance metrics and improved robustness over conventional detectors.

Findings

Outperforms energy detector under noise uncertainty

Provides closed-form expressions for detection metrics

Validates accuracy through simulations

Abstract

A new spectrum sensing detector is proposed and analytically studied, when it operates under generalized noise channels. Particularly, the McLeish distribution is used to model the underlying noise, which is suitable for both non-Gaussian (impulsive) as well as classical Gaussian noise modeling. The introduced detector adopts a moment-based approach, whereas it is not required to know the transmit signal and channel fading statistics (i.e., blind detection). Important performance metrics are presented in closed forms, such as the false-alarm probability, detection probability and decision threshold. Analytical and simulation results are cross-compared validating the accuracy of the proposed approach. Finally, it is demonstrated that the proposed approach outperforms the conventional energy detector in the practical case of noise uncertainty, yet introducing a comparable computational complexity.