A Channel Usage Probability Awareness Anti-Jamming Channel Hopping Protocol for Cognitive Radio Networks

Abstract

To solve the rendezvous problem between any pair of Secondary Users (SUs) and potential jamming attacks in cognitive radio networks, many solutions have employed a channel hopping mechanism. The Primary User (PU) occupancy probability significantly affects the performance of these channel hopping protocols. Certain existing solutions adapt the frequency of channel usage based on predicted PU occupancy probability derived from historical averages. In this paper, we demonstrate the inadequacy of such predictions and propose a rendezvous-guaranteed anti-jamming channel hopping protocol with channel usage adjustment capability, termed OLAA_P. By using deep learning technology to predict future PU occupancy probability for each channel, OLAA_P can properly adjust the usage ratio of each channel. Simulation results validate that OLAA_P outperforms recently-proposed practical anti-jamming hopping protocols in terms of throughput.