Throughput oriented lightweight near-optimal rendezvous algorithm for cognitive radio networks

Abstract

In cognitive radio networks, secondary users have to dynamically search and access spectrum unused by primary users. Due to this dynamic spectrum access nature, the rendezvous between secondary users is a great challenge for cognitive radio networks. In this paper, we propose a Throughput oriEnted lightweight Near-Optimal Rendezvous (TENOR) algorithm that does not need a common control channel. TENOR has very lightweight overhead and accomplishes near-optimal performance with regard to both throughput and rendezvous time. With TENOR, secondary users are grouped into node pairs that are spread onto different channels in a decentralized manner. The co-channel interference is minimized and the throughput is near optimal. We develop a mathematical model to analyze the performance of TENOR. Both analytical and simulation results indicate that TENOR achieves near-optimal throughput and rendezvous time, and significantly outperforms the state-of-the-art rendezvous algorithms in the literature.