Multi-Agent Deep Reinforcement Learning for Uplink Power Control in Multi-Cell Systems

Abstract

The power control is a significant technique for suppressing co-channel interference that severely limits the capacity and connectivity of multi-cell communication systems. In this paper, we propose a novel and efficient multi-agent deep reinforcement learning (MADRL)-based uplink power control method for multi-cell multi-user communication systems. We first formulate the multi-user uplink transmission power optimization problem to maximize the sum throughput of multiple cells. Then, the optimization problem is transformed into a Markov decision process. Since the multi-user power control needs to consider the cooperation of strategies between users, the MADRL technique can be adopted. In our MADRL model, each agent outputs the uplink transmission power of the corresponding user by leveraging the value decomposition network. We also design a pruning algorithm to accelerate the training process of the MADRL model. The experimental results indicate that the proposed MADRL-based uplink power control method is superior to the baseline methods in terms of system throughput and quality of service. The designed pruning algorithm can effectively accelerate model training and also further improve the throughput performance of the proposed method.