Collaborative Sensing Management for Cognitive Radio Networks with Reporting Overhead

Abstract

Many cognitive radio (CR) networks adopt the collaborative sensing strategy that combines the sensing results of multiple CR stations, for getting the better primary user (PU) detection performance. In the practical CR networks with collaborative sensing, since a station reports the sensing result to a fusion center through a message, the reporting overhead is not negligible. In this paper, we design a sensing management scheme for CR networks, while taking this reporting overhead into account. First, we formulate an optimization problem that aims at finding the optimal number of sensing stations which collaborate to maximize the PU detection probability for given sensing/reporting time. Then, we propose a heuristic scheme to efficiently select the collaborative sensing stations, without a priori information for the locations of stations. Furthermore, we determine the optimal sensing interval for minimizing the PU detection delay while maintaining the CR channel utilization to a target value, by using a Markov analysis. The simulation results show that the proposed sensing management scheme significantly improves the PU detection performance.