Scholarlink

We tackle radio resource allocation in 5G and B5G networks, focusing on applications with stringent delay requirements. We formulate this problem as a discounted Markov Decision Process (MDP), considering each user’s Channel Quality Indicator and queue status. We introduce a reducible MDP using state abstraction. By mapping transition dynamics and rewards to an abstract state space, we simplify solving MDPs with smaller state spaces, avoiding the complexity of the original high-dimensional state space. We explore different methods for weighted state aggregation and verify through simulations that our dimension reduction strategy yields results close to the optimal policy.

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Radio Resource Allocation in 5G/B5G Networks: A Dimension Reduction Approach Using Markov Decision Processes

  • 会议论文

  • 发表日期：

    2025年

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  • Lucas Inglés,
  • Olivier Tsemogne,
  • Claudina Rattaro

摘要

We tackle radio resource allocation in 5G and B5G networks, focusing on applications with stringent delay requirements. We formulate this problem as a discounted Markov Decision Process (MDP), considering each user’s Channel Quality Indicator and queue status. We introduce a reducible MDP using state abstraction. By mapping transition dynamics and rewards to an abstract state space, we simplify solving MDPs with smaller state spaces, avoiding the complexity of the original high-dimensional state space. We explore different methods for weighted state aggregation and verify through simulations that our dimension reduction strategy yields results close to the optimal policy.