Transmit Power Control for Indoor Small Cells: A Method Based on Federated Reinforcement Learning

TL;DR

This paper introduces a federated reinforcement learning approach for indoor small cell transmit power control, enhancing model generalisation across heterogeneous environments and improving convergence speed in new settings.

Contribution

It proposes a distributed FRL scheme for indoor power control that improves model generalisation and training efficiency across diverse environments.

Findings

FRL models perform comparably to single RL models.

FRL outperforms random and exhaustive search methods.

Using FRL as a base model accelerates adaptation in new environments.

Abstract

Setting the transmit power setting of 5G cells has been a long-term topic of discussion, as optimized power settings can help reduce interference and improve the quality of service to users. Recently, machine learning (ML)-based, especially reinforcement learning (RL)-based control methods have received much attention. However, there is little discussion about the generalisation ability of the trained RL models. This paper points out that an RL agent trained in a specific indoor environment is room-dependent, and cannot directly serve new heterogeneous environments. Therefore, in the context of Open Radio Access Network (O-RAN), this paper proposes a distributed cell power-control scheme based on Federated Reinforcement Learning (FRL). Models in different indoor environments are aggregated to the global model during the training process, and then the central server broadcasts the updated model back to each client. The model will also be used as the base model for adaptive training in the new environment. The simulation results show that the FRL model has similar performance to a single RL agent, and both are better than the random power allocation method and exhaustive search method. The results of the generalisation test show that using the FRL model as the base model improves the convergence speed of the model in the new environment.