Dynamic Resource Allocation through Reinforcement Learning Approach in Multi-cell OFDMA Networks

In this paper, we present a distributed resource allocation algorithm for cellular OFDMA networks by adopting a Reinforcement Learning (RL) approach. We use an RL method which employ Growing Self Organizing Maps to deal with the huge and continuous problem space. The goal of the algorithm is to maximize the network throughput in a fair manner. Indeed, the algorithm maximizes the throughput until fairness violation does not exceed an adjustable threshold. Simulation results illustrates that the fairness definition leads to enormous extra throughput achievement relative to the fair algorithm.