Clustering-Based Spectrum Sharing Strategy for Cognitive Radio Networks

In this paper, we propose a clustering-based resource allocation (RA) scheme for the multiuser orthogonal frequency division multiplexing (OFDM)-based cognitive radio network, where we aim to maximize the sum capacity of the secondary users (SUs) subject to practical constraints in wireless environment. Our general RA optimization task leads to a challenging mixed integer programming problem that is computationally intractable. We first introduce a simple and efficient clustering method to divide all the SUs into multiple groups based on their mutual interference degrees, where the SUs in different groups can share the same OFDM subchannels to improve spectrum utilization efficiency, while the SUs with heavy mutual interference cluster together in the same group and employ different subchannels to alleviate their mutual interference. Then we develop efficient radio RA algorithms to maximize the sum rate of the SUs in each cluster. A user-oriented subchannel assignment method is presented to remove the awkward integer constraints of the formulated RA problem, followed by a fast power distribution algorithm that can work out optimal solutions with an approximate linear complexity. Simulation results indicate that our proposed RA scheme can improve the throughput of the SUs significantly as compared with other methods. Moreover, our proposed RA algorithms converge stably and quickly.