A Reinforcement Learning Approach for Interference Management in Heterogeneous Wireless Networks

Due to the increased demand for scarce wireless bandwidth, it has become insufficient to serve the network user equipment using macrocell base stations only. Network densification through the addition of low power nodes (picocell) to conventional high-power nodes addresses the bandwidth dearth issue, but unfortunately introduces unwanted interference into the network which causes a reduction in throughput. The purpose of this paper is to develop a model for controlling the interference between picocell and macrocell users of a cellular network so as to increase the overall network throughput. In order to achieve this, a reinforcement learning model was developed which was used in coordinating interference in a heterogeneous network comprising macrocell and picocell base stations. The learning mechanism was derived based on Q-learning, which consisted of agent, state, action, and reward. The base station was modeled as the agent, while the state represented the condition of the user equipment in terms of Signal to Interference Plus Noise Ratio. The action was represented by the transmission power level and the reward was given in terms of throughput. Simulation results showed that the trend of values of the learning rate (e.g., high to low, low to high, etc.) plays a major role in throughput performance. It was particularly shown that a multi-agent system with a normal learning rate could increase the throughput of associated user equipment by a whopping 212.5% compared to a macrocell-only scheme. © 2021. All Rights Reserved.