AI 中文总结
本文提出利用动态极化控制作为额外自由度，结合图神经网络优化SWIPT系统中的波束成形、极化和功率分割，显著提升不完美CSI下的性能。
AI 中文摘要
同时无线信息与功率传输（SWIPT）是未来物联网（IoT）的一项关键技术。然而，在此类网络中确保稳定的功率供应仍然是一个重大挑战。本工作将动态极化控制引入SWIPT系统，作为额外的自由度（DoF）。我们提出了一种系统，其中基站（BS）和用户均可调整其天线极化，这一技术被称为极化赋形（polarforming）。此外，每个用户设备能够分割入射信号，以同时进行信息解码（ID）和能量采集（EH）。由此产生的非凸优化问题具有许多耦合变量，我们使用图神经网络（GNN）来求解，该网络学习次优的波束成形、极化和功率分割变量。仿真结果表明，所提出的基于GNN的动态极化赋形优化显著优于固定极化方案，尤其是在信道状态信息（CSI）不完美的情况下。此外，联合极化赋形和基于GNN的优化在极化失配和不完美CSI下均能保持稳健的SWIPT性能。
英文摘要
Simultaneous wireless information and power transfer (SWIPT) is a critical technology for the future of the Internet of Things (IoT). However, ensuring a stable power supply in such networks remains a significant challenge. This work introduces dynamic polarization control as an additional degree of freedom (DoF) in SWIPT systems. We propose a system where both the base station (BS) and the users can adjust their antenna polarization, a technique known as polarforming. In addition, each user device is capable of splitting the incident signal to perform simultaneous information decoding (ID) and energy harvesting (EH). The resulting non-convex optimization, with many coupled variables, is solved using a graph neural network (GNN) that learns the sub-optimal beamforming, polarization, and power-splitting variables. Simulation results demonstrate that the proposed GNN-based dynamic polarforming optimization significantly outperforms fixed-polarization schemes, particularly under imperfect channel state information (CSI). Moreover, joint polarforming and GNN-based optimization maintain robust SWIPT performance under both polarization mismatch and imperfect CSI.
Comments5 pages, 5 figures
