Millimeter Wave Communications on Overhead Messenger Wire: Deep Reinforcement Learning-Based Predictive Beam Tracking

This paper discusses the feasibility of beam tracking against dynamics in millimeter wave (mmWave) nodes placed on overhead messenger wires. As specific disturbances on-wire deployments, we consider wind-forced perturbations and caused by impulsive forces to Our contribution is answer whether historical positions/velocities a mmWave node are useful track directional beams, given complicated dynamics. To this end, implement deep reinforcement learning (DRL) learn relationships between appropriate beam-steering angles. numerical evaluations yielded following key insights: First, wind perturbations, an beam-tracking policy can be learned from node. Second, wire, use position/velocity not necessarily sufficient, owing rapid displacement. resolve this, propose taking advantage positional interaction wire. done leveraging several points wire as state information DRL. The results confirmed avoidance misalignment due forces, which was possible using only