  This paper discusses the opportunity of bringing the concept of zero-shot
adaptation into learning-based millimeter-wave (mmWave) communication systems,
particularly in environments with unstable urban infrastructures. Here,
zero-shot adaptation implies that a learning agent adapts to unseen scenarios
during training without any adaptive fine-tuning. By considering learning-based
beam-tracking of a mmWave node placed on an overhead messenger wire, we first
discuss the importance of zero-shot adaptation. More specifically, we confirm
that the gap between the values of wire tension and total wire mass in training
and test scenarios deteriorates the beam-tracking performance in terms of the
received power. Motivated by this discussion, we propose a robust beam-tracking
method to adapt to a broad range of test scenarios in a zero-shot manner, i.e.,
without requiring any retraining to adapt the scenarios. The key idea is to
leverage a recent, robust adversarial reinforcement learning technique, where
such training and test gaps are regarded as disturbances from adversaries. In
our case, a beam-tracking agent performs training competitively bases on an
intelligent adversary who causes beam misalignments. Numerical evaluations
confirm the feasibility of zero-shot adaptation by showing that the on-wire
node achieves feasible beam-tracking performance without any adaptive
fine-tuning in unseen scenarios.
