Fault-Tolerant Algorithm for Distributed Primary Detection in Cognitive Radio Networks

Abstract

This paper attempts to identify the reliability of detection of licensed primary transmission based on cooperative sensing in cognitive radio networks. With a parallel fusion network model, the correlation issue of the received signals between the nodes in the worst case is derived. Leveraging the property of false sensing data due to malfunctioning or malicious software, the optimizing strategy, namely Fault-Tolerant algorithm for Distributed Detection (FTDD) is proposed, and quantitative analysis of false alarm reliability and detection probability under the scheme is presented. In particular, the tradeoff between licensed transmissions and user cooperation among nodes is discussed. Simulation experiments are also used to evaluate the fusion performance under practical settings. The model and analytic results provide useful tools for reliability analysis for other wireless decentralization-based applications (e.g., those involving robust spectrum sensing).