Reinforcement Learning Framework Boosts 5G RAN Throughput and Spectral Efficiency

Global: Generalizable Reinforcement Learning Framework Enhances 5G RAN Performance

A team of researchers has introduced a reinforcement learning (RL) framework designed to improve radio access network (RAN) control across diverse 5G deployments. The approach targets downlink link adaptation and aims to deliver higher throughput and spectral efficiency while handling partial, noisy observations and varying network topologies. Tested on five benchmark scenarios, the framework demonstrates measurable gains over traditional outer-loop link adaptation (OLLA) methods.

Background and Motivation

Current rule‑based radio resource management (RRM) algorithms often struggle in dynamic, heterogeneous environments, and conventional RL solutions can overfit to specific training conditions. These limitations motivate a shift toward generalization‑focused methods that remain effective when faced with unseen radio conditions.

Framework Overview

The proposed system reconstructs dynamically changing states from incomplete data and incorporates static and semi‑static information—such as node identities, cell attributes, and network topology—through graph‑based representations. Domain randomization expands the training distribution, reducing the risk of overfitting.

Distributed Training Architecture

Data generation is spread across multiple actors operating in varied network settings, while model training is centralized in a cloud‑compatible environment that aligns with Open RAN (O‑RAN) principles. This design balances the increased computational and data‑management demands of generalization with scalable data collection.

Performance Evaluation

When applied to downlink link adaptation, the RL policy improves average throughput and spectral efficiency by approximately 10% compared with an OLLA baseline that targets a 10% block error rate (BLER). Under high‑mobility conditions, the improvement exceeds 20%. In full‑buffer traffic, the new policy matches specialized RL solutions, and in enhanced mobile broadband (eMBB) and mixed‑traffic benchmarks it delivers up to four‑fold and two‑fold gains, respectively.

Model Comparisons

Across nine‑cell deployments, graph attention network (GAT) models achieve 30% higher throughput than multilayer perceptron (MLP) baselines, highlighting the advantage of topology‑aware representations for RAN control.

Future Outlook

The authors suggest that the scalable, cloud‑native architecture and the demonstrated generalization capabilities provide a viable pathway toward AI‑native 6G RAN implementations that rely on a single, adaptable RL agent.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.