Online Frequency Scheduling by Learning Parallel Actions

• URL: http://arxiv.org/abs/2406.05041v1
• Date: Fri, 7 Jun 2024 16:14:51 GMT
• Title: Online Frequency Scheduling by Learning Parallel Actions
• Authors: Anastasios Giovanidis, Mathieu Leconte, Sabrine Aroua, Tor Kvernvik, David Sandberg,
• Abstract summary: Frequency resources need to be assigned to a set of users while allowing for concurrent transmissions in the same sub-band. Traditional methods are insufficient to cope with all the involved constraints and uncertainties. We propose a scheduler based on action-branching over sub-bands, which is a deep Q-learning architecture with parallel decision capabilities.
• Score: 5.9838600557884805
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Radio Resource Management is a challenging topic in future 6G networks where novel applications create strong competition among the users for the available resources. In this work we consider the frequency scheduling problem in a multi-user MIMO system. Frequency resources need to be assigned to a set of users while allowing for concurrent transmissions in the same sub-band. Traditional methods are insufficient to cope with all the involved constraints and uncertainties, whereas reinforcement learning can directly learn near-optimal solutions for such complex environments. However, the scheduling problem has an enormous action space accounting for all the combinations of users and sub-bands, so out-of-the-box algorithms cannot be used directly. In this work, we propose a scheduler based on action-branching over sub-bands, which is a deep Q-learning architecture with parallel decision capabilities. The sub-bands learn correlated but local decision policies and altogether they optimize a global reward. To improve the scaling of the architecture with the number of sub-bands, we propose variations (Unibranch, Graph Neural Network-based) that reduce the number of parameters to learn. The parallel decision making of the proposed architecture allows to meet short inference time requirements in real systems. Furthermore, the deep Q-learning approach permits online fine-tuning after deployment to bridge the sim-to-real gap. The proposed architectures are evaluated against relevant baselines from the literature showing competitive performance and possibilities of online adaptation to evolving environments.

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