A Reinforcement Learning Approach for Scheduling in mmWave Networks
This addresses the problem of reliable communication in hostile or dynamic environments like military networks, but it is incremental as it applies an existing reinforcement learning method to a specific domain.
The paper tackles the problem of achieving resilient communication at a desired rate in mmWave networks prone to link blockages and node failures, using a Soft Actor-Critic reinforcement learning algorithm that adapts information flow without prior knowledge of link capacities or topology, with numerical evaluations showing it can maintain the desired rate in dynamic environments and is robust against blockage.
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.