A Reinforcement learning-based cognitive MAC protocol
Ιωάννα Κακάλου, Georgios I. Papadimitriou, Petros Nicopolitidis, Panagiotis G. Sarigiannidis, Mohammad S. Obaidat · 2015
A Multi-Channel Cognitive MAC Protocol for adhoc cognitive networks that uses a distributed learning reinforcement scheme is proposed in this paper. The proposed protocol learns the Primary User (PU) traffic characteristics and then selects the best channel to transmit. The scheme, which addresses overlay cognitive networks,avoids collision with the PU nodes and manages to exceed the performance of the less adaptive statistical channel selection schemes in normal and especially bursty traffic environments. The simulation analysis results have shown that the performance of our proposed scheme outperforms that of the CREAM-MAC scheme.