用深度学习优化天线开关状态，提升通信速率。
Deep Learning Optimization of Two-State Pinching Antennas Systems
- 用神经网络直接学习天线激活策略，利用空间特征和信号结构
- 在波导相位与功率分配复杂耦合下，实现通信速率最大化
- 考虑用户位置不确定性，适合真实部署场景的智能天线系统
无线通信系统的发展需要灵活、节能且低成本的天线技术。近年来，通过二进制激活状态动态调控电磁波传播的夹紧天线（Pinching Antennas, PAs）成为有前景的候选方案。本文研究在波导中选择固定位置的若干PAs进行激活，以最大化用户终端的通信速率。由于天线激活、波导引起的相位偏移和功率分配之间的复杂相互作用，该问题被建模为组合分数0-1二次规划问题。为高效求解此难题，我们采用不同复杂度的神经网络架构，直接从数据中学习激活策略，利用空间特征和信号结构。此外，我们在训练和评估流程中引入用户位置不确定性，以模拟真实部署条件。仿真结果证明了所提模型的有效性和鲁棒性。
原文摘要 · Abstract (English)
The evolution of wireless communication systems requires flexible, energy-efficient, and cost-effective antenna technologies. Pinching antennas (PAs), which can dynamically control electromagnetic wave propagation through binary activation states, have recently emerged as a promising candidate. In this work, we investigate the problem of optimally selecting a subset of fixed-position PAs to activate in a waveguide, when the aim is to maximize the communication rate at a user terminal. Due to the complex interplay between antenna activation, waveguide-induced phase shifts, and power division, this problem is formulated as a combinatorial fractional 0-1 quadratic program. To efficiently solve this challenging problem, we use neural network architectures of varying complexity to learn activation policies directly from data, leveraging spatial features and signal structure. Furthermore, we incorporate user location uncertainty into our training and evaluation pipeline to simulate realistic deployment conditions. Simulation results demonstrate the effectiveness and robustness of the proposed models.
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