Distributed Nonparametric Sequential Spectrum Sensing under Electromagnetic Interference

Abstract

We propose a distributed sequential algorithm for quick detection of spectral holes in a Cognitive Radio set up. 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 change detection in the presence of heavy-tailed electromagnetic interference (EMI) and outliers. Different nonparametric sequential algorithms are compared to choose appropriate algorithms to be used at the local nodes and the FC. A recently developed entropy test is selected for the local nodes for energy detection, and a modified version of a well-known t test at the fusion centre for mean change detection. We show via simulations and analysis that the nonparametric distributed algorithms developed performs well in the presence of EMI and outliers. The algorithm is iterative in nature, unlike t test and rank test or their robust versions.