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Arxiv
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Generalization in Reinforcement Learning for Radio Access Networks
- Reaserchers propose a generalization-centered RL framework for RAN control due to challenges posed by dynamic and heterogeneous environments in radio access networks.
- The framework encodes cell topology and node attributes, applies domain randomization, and uses distributed data generation to improve generalization.
- Applied to downlink link adaptation in 5G benchmarks, the proposed policy enhances throughput and spectral efficiency by over 10% in various scenarios.
- The results indicate promising performance gains, offering a scalable architecture for potential future adoption in AI-driven 6G RAN development.
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