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arXiv 2605.08039eess.SP
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在考虑用户移动性的 pinch 天线系统中联合波束成形和天线位置优化：一种深度强化学习方法

Joint Beamforming and Antenna Placement Optimization in Pinching Antenna Systems with User Mobility: A Deep Reinforcement Learning Approach

Ali Amhaz, Mohamed Elhattab, Chadi Assi, Sanaa Sharafeddine

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AI总结：

本文提出利用深度强化学习优化移动用户与 pinch 天线系统之间的波束成形和天线位置，以提升平均速率并满足 QoS 要求，克服非凸性和环境随机性带来的挑战。

摘要：

近年来，由于其能够利用波导中动态可重构的 pinch 点进行灵活信号传输，pinching 天线系统（PASS）受到了广泛关注。然而，现有研究大多忽视了用户移动性，尽管最优的 pinch 配置高度依赖于用户的位置并必须持续调整。本文研究了一种由 PASS 支持的系统模型，其中基站（BS）服务于移动用户。我们提出了一个优化问题，旨在在预定义的时间范围内最大化用户的平均总速率，同时满足服务质量（QoS）约束。这一目标通过在 BS 处联合优化波束成形向量和波导中 pinch 位置来实现。然而，所得到的问题高度非凸，难以使用传统优化技术解决，因为变量之间存在复杂的耦合。困难进一步加剧了由于用户移动性和概率性阻挡模型引起的环境随机性。这揭示了一个关键的工程挑战：PASS 的性能增益严重依赖于实时跟踪或预测用户轨迹的能力。为了解决这些挑战，我们采用深度确定性策略梯度（DDPG）方法，在强化学习框架中进行，这非常适合连续状态和动作空间。最后，进行了广泛的模拟以验证所提出的方法，并展示了实时可配置性的重要性。

英文摘要：

Recently, the pinching antenna systems (PASS) have attracted significant attention due to their ability to exploit dynamically reconfigurable pinching points along waveguides for flexible signal transmission. However, existing work largely overlooks user mobility although the optimal pinching configuration is highly dependent on the user's location and must be continuously adjusted. In this work, we investigate a PASS-enabled system model in which a base station (BS) serves a mobile user. We formulate an optimization problem that aims to maximize the user's average sum rate over a predefined time horizon while satisfying quality-of-service (QoS) constraint. This objective is achieved by jointly optimizing the beamforming vector at the BS and the pinching locations along the waveguides. Nevertheless, the resulting problem is highly non-convex and challenging to solve using conventional optimization techniques due to the intricate coupling among variables. The difficulty is further exacerbated by environmental randomness arising from user mobility and a probabilistic blockage model. This reveals a key engineering challenge: the performance gains of PASS critically rely on the ability to track or predict user trajectories in real time. To address these challenges, we adopt a deep deterministic policy gradient (DDPG) approach within a reinforcement learning framework, which is well-suited for continuous state and action spaces. Finally, extensive simulations are conducted to validate the proposed approach and demonstrate the importance of real-time configurability.

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。

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