FORLORN: A Framework for Comparing Offline Methods and Reinforcement Learning for Optimization of RAN Parameters

TL;DR

This paper introduces FORLORN, a benchmarking framework for evaluating reinforcement learning methods in optimizing RAN parameters within simulated mobile network environments, demonstrating RL's adaptability and efficiency.

Contribution

The paper presents FORLORN, a novel framework for benchmarking RL in RAN optimization, enabling comparison with offline methods and supporting dynamic adaptation.

Findings

RL agents can match offline optimization in static scenarios

RL agents adapt effectively in dynamic network conditions

Framework facilitates development of RL-based RAN control algorithms

Abstract

The growing complexity and capacity demands for mobile networks necessitate innovative techniques for optimizing resource usage. Meanwhile, recent breakthroughs have brought Reinforcement Learning (RL) into the domain of continuous control of real-world systems. As a step towards RL-based network control, this paper introduces a new framework for benchmarking the performance of an RL agent in network environments simulated with ns-3. Within this framework, we demonstrate that an RL agent without domain-specific knowledge can learn how to efficiently adjust Radio Access Network (RAN) parameters to match offline optimization in static scenarios, while also adapting on the fly in dynamic scenarios, in order to improve the overall user experience. Our proposed framework may serve as a foundation for further work in developing workflows for designing RL-based RAN control algorithms.