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.

中文翻译：

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通过强大的对抗性增强学习，对架空信使线上的毫米波波束跟踪进行零射适应

本文讨论了将零击适应概念引入基于学习的毫米波（mmWave）通信系统的机会，特别是在城市基础设施不稳定的环境中。在这里，零击适应意味着学习者在训练过程中适应了看不见的场景，而没有任何自适应的微调。通过考虑放置在头顶挂绳上的mmWave节点的基于学习的波束跟踪，我们首先讨论零击适应的重要性。更具体地说，我们确认，在训练和测试情况下，线张力值与总线质量之间的差距会降低接收功率方面的光束跟踪性能。出于这一讨论的考虑，我们提出了一种健壮的波束跟踪方法，以零散的方式适应广泛的测试场景，即 无需任何重新培训即可适应各种情况。关键思想是利用一种最新的，强大的对抗增强学习技术，其中这种训练和测试差距被视为来自对手的干扰。在我们的案例中，光束跟踪代理基于会导致光束未对准的聪明对手进行有竞争力的训练。数值评估通过显示在线节点在看不见的情况下无需任何自适应微调就可以实现可行的波束跟踪性能，从而证实了零脉冲自适应的可行性。光束跟踪代理基于导致光束未对准的聪明对手进行竞争性训练。数值评估通过显示在线节点在看不见的情况下无需任何自适应微调就可以实现可行的波束跟踪性能，从而证实了零脉冲自适应的可行性。光束跟踪代理基于导致光束未对准的聪明对手进行竞争性训练。数值评估通过显示在线节点在看不见的情况下无需任何自适应微调就可以实现可行的波束跟踪性能，从而证实了零脉冲自适应的可行性。