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Deep Reinforcement Learning for 5G Dense Urban Wireless Routing
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Barak Gahtan, M.Sc. Thesis Seminar
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Monday, 27.6.2022, 14:00
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Zoom Lecture: 94134094787
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Advisor:  Prof. Reuven Cohen, Prof. Alex Bronstein
We study the problem of routing real-time flows over a multi-hop mmWave mesh. We develop a model-free Deep Reinforcement Learning algorithm that determines which subset of the mmWave links should be activated during each time slot and using what power level. The proposed algorithm, called AARL (Adaptive Activator RL), can handle a variety of network topologies, packet loads and interference models. It does not require prior knowledge of the interdependence of different mmWave links or the topology, and it is capable of adapting to different topologies. We demonstrate AARL on three different topologies: a small topology with 10 links, a moderate topology with 48 links, and a large topology with 96 links. We compare the results of AARL to those of a greedy algorithm. AARL is shown to outperform the greedy algorithm in two aspects. First, its schedule is better since it obtains higher goodput. Second, and even more importantly, the running time of the greedy algorithm renders it impractical for real-time scheduling, whereas the running time of AARL, is suitable for meeting the time constraints of typical 5g networks.
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