Polarforming-Enabled Power-Splitting SWIPT: A GNN-Based Optimization Approach

2026-09-12

AI Summary

本文针对物联网中无线信息与能量同时传输系统的稳定供电问题，提出一种基于图神经网络的动态极化控制和功率分配优化方法。

Abstract

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.

Problem

• SWIPT
• IoT
• power supply

Innovation

• dynamic polarization control
• graph neural network (GNN)
• simultaneous wireless information and power transfer (SWIPT)