Generalization in Reinforcement Learning for Radio Access Networks
Modern radio access networks (RANs) operate in highly dynamic and heterogeneous environments, where hand-tuned, rule-based radio resource management (RRM) algorithms frequently underperform. While reinforcement learning (RL) can surpass these heuristics in constrained scenarios, the unpredictable na...
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| Principais autores: | , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
IEEE
2026-01-01
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| coleção: | IEEE Transactions on Machine Learning in Communications and Networking |
| Assuntos: | |
| Acesso em linha: | https://ieeexplore.ieee.org/document/11358408/ |
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