1 research outputs found
Zero-Shot Adaptation for mmWave Beam-Tracking on Overhead Messenger Wires through Robust Adversarial Reinforcement Learning
Millimeter wave (mmWave) beam-tracking based on machine learning enables the
development of accurate tracking policies while obviating the need to
periodically solve beam-optimization problems. However, its applicability is
still arguable when training-test gaps exist in terms of environmental
parameters that affect the node dynamics. From this skeptical point of view,
the contribution of this study is twofold. First, by considering an example
scenario, we confirm that the training-test gap adversely affects the
beam-tracking performance. More specifically, we consider nodes placed on
overhead messenger wires, where the node dynamics are affected by several
environmental parameters, e.g, the wire mass and tension. Although these are
particular scenarios, they yield insight into the validation of the
training-test gap problems. Second, we demonstrate the feasibility of
\textit{zero-shot adaptation} as a solution, where a learning agent adapts to
environmental parameters unseen during training. This is achieved by leveraging
a robust adversarial reinforcement learning (RARL) technique, where such
training-and-test gaps are regarded as disturbances by adversaries that are
jointly trained with a legitimate beam-tracking agent. Numerical evaluations
demonstrate that the beam-tracking policy learned via RARL can be applied to a
wide range of environmental parameters without severely degrading the received
power.Comment: 13 pages, 13 figures, 3 tables, under submission for possible
publication for IEE