GNN-Based Polarforming for Multi-User MISO Short-Packet URLLC under Imperfect CSI

2026-09-12

AI Summary

本文针对不完美信道状态信息下的多用户MISO短包URLLC，通过基于GNN的极化控制方法优化了有限码长可达和速率并降低了最大解码错误概率。

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.

Problem

• MU-MISO
• URLLC
• imperfect CSI
• polarization-aware transmission
• finite-blocklength

Innovation

• GNN-based polarforming
• imperfect CSI
• multi-user MISO
• short-packet URLLC
• adaptive polarization