发表机构
Pasargad Institute for Advanced Innovative Solutions (PIAIS); Department of Electrical Engineering, Sharif University of Technology; Department of Electrical Engineering, Amirkabir University of Technology; Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg; Department of Electrical and Computer Engineering, University of Cyprus（帕萨加德先进创新解决方案研究所; 谢里夫理工大学电气工程系; 阿米尔卡比尔理工大学电气工程系; 卢森堡大学安全、可靠性与信任跨学科中心; 塞浦路斯大学电气与计算机工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于GNN的极化成形框架，在不完美CSI下联合优化波束成形与极化向量，提升多用户MISO短包URLLC的有限块长度和速率并降低解码错误概率。
AI 中文摘要
本文研究了在不完美信道状态信息（CSI）下，多用户多输入单输出（MU-MISO）短包超可靠低延迟通信（URLLC）中的极化感知传输。我们考虑一个系统，其中基站（BS）和用户配备有极化可重构天线，通过可控极化系数实现自适应极化状态。我们制定了一个多目标优化问题，以在发射功率、延迟、可靠性和离散极化控制约束下，联合最大化有限块长度（FBL）可达和速率并最小化最大解码错误概率（DEP）。所得多目标问题通过归一化加权和效用进行标量化。为实现低复杂度在线决策，开发了一种异构图神经网络（GNN），学习从估计的极化CSI到数字波束成形和发射/接收极化成形向量（PFV）的联合映射，同时考虑系统约束。数值结果表明，与传统的固定极化方案相比，所提出的基于GNN的极化成形（PF）框架显著提高了FBL和速率，同时降低了最大DEP，特别是在信道去极化和不完美CSI情况下。这突显了自适应极化控制对可靠低延迟传输的潜力。
英文摘要
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.
Comments5 pages, 5 figures
