Efficient Exploration for Reinforcement Learning Based Distributed Spectrum Sharing in Cognitive Radio System

In this paper, we investigate how distributed reinforcement learning-based resource assignment algorithms can be used to improve the performance of a cognitive radio system. Today’s decision making in most wireless systems include cognitive radio systems in development, depends purely on instantaneous measurement. Two system architectures have been investigated in this paper. A point-to-point architecture is examined first in an open spectrum scenario. Then, the distributed reinforcement learning-based algorithms are developed by modifying the traditional reinforcement learning model in order to be applied to a fully distributed cognitive radio system.