Distributed Spectrum Sensing for Cognitive Radio Sensor Networks Using Diffusion Adaptation

Abstract

Cognitive radio is a practical solution for spectrum scarcity. In cognitive networks, unlicensed (secondary) users should sense the spectrum before any usage to make sure that the licensed (primary) users do not use the spectrum at that time. Due to the importance of spectrum sensing in cognitive networks, this should be fast and reliable, particularly in networks with communication link failure, which leads to the network topology change. Decentralized decision making algorithms are known as a promising technique to provide reliability, scalability, and adaptation, especially in sensor networks. In this article, we propose a distributed diffusion based method in which secondary users (sensors) cooperate to improve the performance of spectrum sensing. The proposed method provides a significant improvement in convergence rate and reliability. Simulation results indicate that the proposed algorithm shows an acceptable performance and converges twice as fast as recently proposed consensus based spectrum sensing algorithms in the literature and is almost insensitive to communication link failure.