基于鲁棒对抗性强化学习的mmWave波束跟踪Zero-Shot自适应算法
Zero-Shot Adaptation for mmWave Beam-Tracking on Overhead Messenger Wires through Robust Adversarial Reinforcement Learning
摘要：
基于机器学习的毫米波（mmWave）波束跟踪使得能够开发准确的跟踪策略，同时避免了周期性地解决波束优化问题的需要。然而，它的适用性仍然是可验证的，当训练测试差距存在的环境参数，影响节点的动态。从这个怀疑的角度来看，这项研究的贡献是双重的。首先，通过考虑一个例子的情况下，我们确认，训练测试的差距产生不利影响的波束跟踪性能。更具体地说，我们认为节点放置在架空吊线缆，节点动力学的影响，由几个环境参数，例如，线质量和张力。虽然这些都是特定的场景，但它们可以深入了解培训-测试差距问题的验证。其次，我们证明了\textit{zero-shot adaptation}作为一种解决方案的可行性，其中学习代理适应训练过程中看不到的环境参数。这是通过利用鲁棒的对抗性强化学习（RARL）技术来实现的，其中这种训练和测试间隙被视为与合法的波束跟踪代理联合训练的对手的干扰。数值计算表明，通过RARL学习的波束跟踪策略可以应用于广泛的环境参数，而不会严重降低接收功率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
