Decentralized Adaptive Eigenvalue-Based Spectrum Sensing for Multiantenna Cognitive Radio Systems

Abstract

Eigenvalue-based spectrum sensing (EBSS) techniques may operate in a totally blind manner while they offer remarkably improved performance for specific types of signals compared with energy-based methods. In the literature so far, only batch and centralized cooperative EBSS techniques have been considered, which, however, suffer from limitations that render them impractical in several cases. Thus, the aim of this paper is to develop practical cooperative adaptive versions of typical EBSS techniques that could be applied in a completely decentralized manner. To this end, at first, novel adaptive EBSS techniques are developed for the maximum eigenvalue detector, the maximum-minimum eigenvalue detector, and the generalized likelihood ratio test scheme, respectively, for a single-user (no cooperation) case. Then, a novel distributed subspace tracking method is proposed, which enables the cooperating nodes to track the joint subspace of their received signals. Based on this method, cooperative decentralized versions of the adaptive EBSS techniques are subsequently developed that overcome the limitations of the existing batch centralized approaches. The performance of the proposed methods is verified via indicative simulations.