Using Deep Reinforcement Learning for mmWave Real-Time Scheduling
Published in 2023 14th International Conference on Network of the Future (NoF), 2023
We study the problem of real-time scheduling in a multi-hop millimeter-wave (mmWave) mesh. We develop a model-free deep reinforcement learning algorithm called Adaptive Activator RL (AARL), which determines the subset of mmWave links that should be activated during each time slot and the power level for each link. The most important property of AARL is its ability to make scheduling decisions within the strict time frame constraints of typical 5G mmWave networks.
Recommended citation: @inproceedings{gahtan2023using, title={Using Deep Reinforcement Learning for mmWave Real-Time Scheduling}, author={Gahtan, Barak and Cohen, Reuven and Bronstein, Alex M and Kedar, Gil}, booktitle={2023 14th International Conference on Network of the Future (NoF)}, pages={71--79}, year={2023}, organization={IEEE} }
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