Generalization in Reinforcement Learning for Radio Access Networks

TL;DR

This paper introduces a generalization-focused reinforcement learning framework for Radio Access Networks that enhances performance across diverse and dynamic 5G scenarios by robust state reconstruction, domain randomization, and distributed training.

Contribution

It presents a novel RL approach that improves generalization in RAN control through graph-based state encoding, domain randomization, and scalable distributed training architecture.

Findings

Achieves ~10% throughput improvement over baseline in 5G benchmarks.

Attains >20% spectral efficiency gains under high mobility conditions.

Models outperform MLP baselines with 30% higher throughput in multi-cell deployments.

Abstract

Modern RAN operate in highly dynamic and heterogeneous environments, where hand-tuned, rule-based RRM algorithms often underperform. While RL can surpass such heuristics in constrained settings, the diversity of deployments and unpredictable radio conditions introduce major generalization challenges. Data-driven policies frequently overfit to training conditions, degrading performance in unseen scenarios. To address this, we propose a generalization-centered RL framework for RAN control that: (i) robustly reconstructs dynamically varying states from partial and noisy observations, while encoding static and semi-static information, such as radio nodes, cell attributes, and their topology, through graph representations; (ii) applies domain randomization to broaden the training distribution; and (iii) distributes data generation across multiple actors while centralizing training in a cloud-compatible architecture aligned with O-RAN principles. Although generalization increases computational and data-management complexity, our distributed design mitigates this by scaling data collection and training across diverse network conditions. Applied to downlink link adaptation in five 5G benchmarks, our policy improves average throughput and spectral efficiency by ~10% over an OLLA baseline (10% BLER target) in full-buffer MIMO/mMIMO and by >20% under high mobility. It matches specialized RL in full-buffer traffic and achieves up to 4- and 2-fold gains in eMBB and mixed-traffic benchmarks, respectively. In nine-cell deployments, GAT models offer 30% higher throughput over MLP baselines. These results, combined with our scalable architecture, offer a path toward AI-native 6G RAN using a single, generalizable RL agent.