A Reinforcement Learning Approach to Power Control and Rate Adaptation in Cellular Networks

TL;DR

This paper introduces a reinforcement learning framework for power control and rate adaptation in cellular networks, enabling efficient, near-optimal management of transmission power and data rates with limited system information.

Contribution

It presents a novel RL-based approach that effectively learns power and rate control policies in wireless systems, addressing practical limitations of existing methods.

Findings

Achieves significant energy savings.

Ensures fairness among users.

Learns effective control policies quickly.

Abstract

Optimizing radio transmission power and user data rates in wireless systems via power control requires an accurate and instantaneous knowledge of the system model. While this problem has been extensively studied in the literature, an efficient solution approaching optimality with the limited information available in practical systems is still lacking. This paper presents a reinforcement learning framework for power control and rate adaptation in the downlink of a radio access network that closes this gap. We present a comprehensive design of the learning framework that includes the characterization of the system state, the design of a general reward function, and the method to learn the control policy. System level simulations show that our design can quickly learn a power control policy that brings significant energy savings and fairness across users in the system.