AI-driven Dynamic mmWave Networking

Dynamic mmWave Mesh Network

Millimeter-wavelength (mmWave) mesh network can provide multi-Gbps transmission but with large path loss and heterogeneous objectives which is hard to solve by heuristic models. Machine learning (ML) techniques, especially reinforcement learning (RL), have great potential in solving multi-objective, non-linear, and non-convex problems that often happen in mmWave mesh network configuration. On the other hand, network configuration policies learned from simulations cannot always help physical networks meet performance requirements due to sim2real gap. In this work, we develop a reinforcement learning (RL) model to train a policy for dynamic topology management and a self-supervised policy adaptation algorithm to bridge the domain gap. The experimental results shows that our RL agent can learn a policy to avoid blockage links and self-supervised learning model can help to eliminate domain gaps. The testbed we built can establish multiple routes and can be controlled effectively by a central controller. We successfully ran the simulation-trained RL policy and self-supervision agent on the real testbed.