Learning and Uncertainty-Exploited Directional Antenna Control for Robust Aerial Networking

Aerial communication using directional antennas (ACDA) is a promising solution to enable long-distance and broad-band unmanned aerial vehicle (UAV)-to-UAV communication. The automatic alignment of directional antennas allows transmission energy to focus in certain direction and hence significantly extends communication range and rejects interference. In this paper, we develop reinforcement learning (RL)-based on-line directional antennas control solutions for the ACDA system. The novel stochastic optimal control algorithm integrates RL, an effective uncertainty evaluation method called multivariate probabilistic collocation method (MPCM), and unscented Kalman Filter (UKF) for the nonlinear random switching dynamics. Simulation studies are conducted to illustrate and validate the proposed solutions.