Cognitive Radio with Reinforcement Learning Applied to Multicast Downlink Transmission with Power Adjustment

Abstract

This paper shows how channel assignment in multicast terrestrial communication systems with distributed channel occupancy detection can be improved using intelligence based on reinforcement learning and transmitter power adjustment. It is shown how such schemes greatly reduce the number of reassignments and improve the dropping probability, at the expense of increased blocking. It is found that using different minimum quality of service threshold percentages can partly control and improve the performance, in place of the more traditional SINR threshold levels. The paper also shows how a power adjustment technique is developed which significantly reduces the level of overlap between adjacent base stations, and further reduces interference and transmitter power.