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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Bibliografische gegevens
Hoofdauteurs:	Burak Demirel, Yu Wang, Cristian Tatino, Pablo Soldati
Formaat:	Artigo
Taal:	Inglês
Gepubliceerd in:	IEEE 2026-01-01
Reeks:	IEEE Transactions on Machine Learning in Communications and Networking
Onderwerpen:	Reinforcement learning model generalization link adaptation radio access networks
Online toegang:	https://ieeexplore.ieee.org/document/11358408/
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Generalization in Reinforcement Learning for Radio Access Networks

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