GNN-Based Polarforming for Multi-User MISO Short-Packet URLLC under Imperfect CSI
Abstract
This paper investigates polarization-aware transmission for multi-user multiple-input single-output (MU-MISO) short-packet ultra-reliable low-latency communications (URLLC) under imperfect channel state information (CSI). We consider a system in which the base station (BS) and users are equipped with polarization-reconfigurable antennas that enable adaptive polarization states through controllable polarization coefficients. A multi-objective optimization problem is formulated to jointly maximize the finite-blocklength (FBL) achievable sum rate and minimize the maximum decoding error probability (DEP), subject to transmit-power, latency, reliability, and discrete polarization-control constraints. The resulting multi-objective problem is scalarized using a normalized weighted-sum utility. To enable low-complexity online decision-making, a heterogeneous graph neural network (GNN) is developed to learn the joint mapping from estimated polarized CSI to digital beamforming and transmit/receive polarforming vectors (PFVs) while accounting for the system constraints. Numerical results demonstrate that the proposed GNN-based polarforming (PF) framework substantially improves the FBL sum rate while reducing the maximum DEP compared with conventional fixed-polarization schemes, particularly under channel depolarization and imperfect CSI. This highlights the potential of adaptive polarization control for reliable low-latency transmission.
Related
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BibTeX
@misc{mehrzad2026gnnbased,
title = {{GNN-Based Polarforming for Multi-User MISO Short-Packet URLLC under Imperfect CSI}},
author = {Zahra Mehrzad and Hamed Aghaei-Karkaj and Rahman Saadat Yeganeh and Kamran Ebrahimi and Mohammad Robat Mili and Symeon Chatzinotas and Ioannis Krikidis},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.14006},
eprint = {2609.14006},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.14006},
}
RIS
TY - UNPB
TI - GNN-Based Polarforming for Multi-User MISO Short-Packet URLLC under Imperfect CSI
AU - Zahra Mehrzad
AU - Hamed Aghaei-Karkaj
AU - Rahman Saadat Yeganeh
AU - Kamran Ebrahimi
AU - Mohammad Robat Mili
AU - Symeon Chatzinotas
AU - Ioannis Krikidis
PY - 2026
DA - 2026/09/12
PB - arXiv
DO - 10.48550/arxiv.2609.14006
UR - https://arxiv.org/abs/2609.14006
AB - This paper investigates polarization-aware transmission for multi-user multiple-input single-output (MU-MISO) short-packet ultra-reliable low-latency communications (URLLC) under imperfect channel state information (CSI). We consider a system in which the base station (BS) and users are equipped with polarization-reconfigurable antennas that enable adaptive polarization states through controllable polarization coefficients. A multi-objective optimization problem is formulated to jointly maximize the finite-blocklength (FBL) achievable sum rate and minimize the maximum decoding error probability (DEP), subject to transmit-power, latency, reliability, and discrete polarization-control constraints. The resulting multi-objective problem is scalarized using a normalized weighted-sum utility. To enable low-complexity online decision-making, a heterogeneous graph neural network (GNN) is developed to learn the joint mapping from estimated polarized CSI to digital beamforming and transmit/receive polarforming vectors (PFVs) while accounting for the system constraints. Numerical results demonstrate that the proposed GNN-based polarforming (PF) framework substantially improves the FBL sum rate while reducing the maximum DEP compared with conventional fixed-polarization schemes, particularly under channel depolarization and imperfect CSI. This highlights the potential of adaptive polarization control for reliable low-latency transmission.
ER -