Distributed Nonparametric Sequential Spectrum Sensing under Electromagnetic Interference

TL;DR

This paper introduces a robust, nonparametric distributed sequential spectrum sensing algorithm for cognitive radio networks that effectively detects spectral holes amidst electromagnetic interference, fading, and outliers.

Contribution

It develops a novel nonparametric sequential detection method suitable for challenging electromagnetic environments, improving robustness and computational efficiency.

Findings

Performs well under fading and EMI conditions

Robust to outliers and heavy-tailed interference

Requires minimal computation and storage

Abstract

A nonparametric distributed sequential algorithm for quick detection of spectral holes in a Cognitive Radio set up is proposed. Two or more local nodes make decisions and inform the fusion centre (FC) over a reporting Multiple Access Channel (MAC), which then makes the final decision. The local nodes use energy detection and the FC uses mean detection in the presence of fading, heavy-tailed electromagnetic interference (EMI) and outliers. The statistics of the primary signal, channel gain or the EMI is not known. Different nonparametric sequential algorithms are compared to choose appropriate algorithms to be used at the local nodes and the FC. Modification of a recently developed random walk test is selected for the local nodes for energy detection as well as at the fusion centre for mean detection. It is shown via simulations and analysis that the nonparametric distributed algorithm developed performs well in the presence of fading, EMI and is robust to outliers. The algorithm is iterative in nature making the computation and storage requirements minimal.