Polarforming-Enabled Power-Splitting SWIPT: A GNN-Based Optimization Approach
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.
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BibTeX
@misc{aghaeikarkaj2026polarformingenabled,
title = {{Polarforming-Enabled Power-Splitting SWIPT: A GNN-Based Optimization Approach}},
author = {Hamed Aghaei-Karkaj and Kamran Ebrahimi and Zahra Mehrzad and Mohammad Robat Mili and Symeon Chatzinotas and Ioannis Krikidis},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.14014},
eprint = {2609.14014},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.14014},
}
RIS
TY - UNPB
TI - Polarforming-Enabled Power-Splitting SWIPT: A GNN-Based Optimization Approach
AU - Hamed Aghaei-Karkaj
AU - Kamran Ebrahimi
AU - Zahra Mehrzad
AU - Mohammad Robat Mili
AU - Symeon Chatzinotas
AU - Ioannis Krikidis
PY - 2026
DA - 2026/09/12
PB - arXiv
DO - 10.48550/arxiv.2609.14014
UR - https://arxiv.org/abs/2609.14014
AB - 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.
ER -