A Reinforcement Learning Approach for Scheduling in mmWave Networks

• URL: http://arxiv.org/abs/2108.00548v1
• Date: Sun, 1 Aug 2021 21:47:47 GMT
• Title: A Reinforcement Learning Approach for Scheduling in mmWave Networks
• Authors: Mine Gokce Dogan, Yahya H. Ezzeldin, Christina Fragouli, Addison W. Bohannon
• Abstract summary: We consider a source that wishes to communicate with a destination at a desired rate, over a mmWave network. To achieve resilience to link and node failures, we here explore a state-of-the-art Soft Actor-Critic (SAC) deep reinforcement learning algorithm.
• Score: 24.46948464551684
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We consider a source that wishes to communicate with a destination at a desired rate, over a mmWave network where links are subject to blockage and nodes to failure (e.g., in a hostile military environment). To achieve resilience to link and node failures, we here explore a state-of-the-art Soft Actor-Critic (SAC) deep reinforcement learning algorithm, that adapts the information flow through the network, without using knowledge of the link capacities or network topology. Numerical evaluations show that our algorithm can achieve the desired rate even in dynamic environments and it is robust against blockage.

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