Abstract
In a cognitive radio network (CRN), a secondary user (SU) can utilize a channel that is unoccupied by any primary users (PU) in the licensed spectrum to increase spectrum usage efficiency. To provide rendezvous between any pair of SUs and avoid jamming attacks, many solutions applied a channel hopping mechanism. The channel occupancy probability is a factor that heavily affects the performance of a channel hopping protocol. However, most existing channel hopping solutions do not address this issue well. In this paper, we propose the Occupancy, Load and channel usage Probability Awareness Anti-jamming channel hopping protocol (OLAA P). Applying deep learning technology to predict the future PU occupancy probability for each channel, OLAA_P is capable of properly adjusting the usage ratio of each channel. Simulation results verify that OLAA_P outperforms the latest anti-jamming hopping protocol, Load Awareness Anti-jamming channel hopping protocol (LAA), in terms of average time to rendezvous.
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