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

arXiv: https://arxiv.org/abs/1611.06497 · HTML: https://mlsanity.com/paper/1611.06497/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2016.

## Built on by (most cited first, 3 of 3)

- [Multi-agent Reinforcement Learning with Graph Q-Networks for Antenna Tuning](https://mlsanity.com/paper/2302.01199/) (2023)
- [Generalization in Reinforcement Learning for Radio Access Networks](https://mlsanity.com/paper/2507.06602/) (2025)
- [A Q-Learning-based Approach for Distributed Beam Scheduling in mmWave Networks](https://mlsanity.com/paper/2110.08704/) (2021)

## Abstract (arXiv)

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.