Efficient Spectrum Utilization in Cognitive Radio Through Reinforcement Learning

Machine learning schemes can be employed in cognitive radio systems to intelligently locate the spectrum holes with some knowledge about the operating environment. In this paper, we formulate a variation of Actor Critic Learning algorithm known as Continuous Actor Critic Learning Automaton (CACLA) and compare this scheme with Actor Critic Learning scheme and existing Q-learning scheme. Simulation results show that our CACLA scheme has lesser execution time and achieves higher throughput compared to other two schemes.