Cluster-Based Cooperative Spectrum Sensing Assignment Strategy for Heterogeneous Cognitive Radio Network

Abstract

Cognitive radio (CR) is proposed as an efficient way to address the issue of spectrum shortage and underutilization, in which cooperative spectrum sensing (CSS) is used to enhance sensing performance. One of the most fundamental problems of CSS is how to appropriately assign the secondary users (SUs) to sense the primary user (PU) channels. In this paper, we study the CSS problem under a more practical scenario where we take the heterogeneous characteristics of both SUs and PU channels into consideration. With the objective to maximize the achievable throughput for SUs, we propose a cluster-based CSS to obtain a proper assignment policy, in which all the SUs in the same cluster cooperate in sensing the same set of PU channels. Moreover, the cluster-based CSS problem is formulated as a Maximum-Weight one-sided Biclique (MWB) problem. We prove that the MWB problem is NP-complete, and a greedy heuristic algorithm with polynomial computational complexity is developed to find the suboptimal assignment policy. Evaluation results show that our proposed greedy algorithm can achieve a close-to-optimal solution with far less complexity.