基于梯度元学习（GML）的可移动天线无线网络优化：挑战与机遇

中文摘要 AI 辅助

可移动天线（MA）作为未来无线网络的新兴技术被提出，通过利用额外的空间自由度，MA可主动重塑无线传播环境，从而提升网络性能。要充分释放MA网络的潜力，需联合优化MA天线定位与波束成形，而该非凸且高度耦合的问题，现有解决方案存在显著局限性。因此，本文提出一种梯度元学习（GML）优化框架。具体而言，本文首先阐述MA的硬件架构与信道特性，基于此分析优化MA无线网络的主要挑战；随后介绍GML框架的基本逻辑，并将其与现有方法进行对比；进一步讨论将该优化框架应用于MA网络的约束处理策略；通过一个具体案例，基于数值仿真展示所提框架的性能；最后，本文概述GML框架与MA无线网络的未来研究方向。

英文摘要

Movable antenna (MA) is proposed as an emerging technology for future wireless networks. By leveraging the additional spatial degrees of freedom, MA can proactively reshape the wireless propagation environment, thereby enhancing network performance.However, fully unlocking the potential of MA networks necessitates the joint optimization of MA antenna positioning and beamforming. For this non-convex and highly coupled problem, existing solutions exhibit significant limitations. Therefore, this paper proposes a gradient-based meta learning (GML) optimization framework. Specifically, we first elaborate on the hardware architecture and channel characteristics of MA, based on which we analyze the primary challenges in optimizing MA wireless networks. Subsequently, we introduce the fundamental logic of the GML framework and compare it with existing methods. Furthermore, we discuss the constraint handling strategies for applying the proposed optimization framework to MA networks. A specific case is studied to show the performance of proposed framework based on numerical simulation. Finally, this paper outlines future research directions for both the GML framework and MA wireless networks.

发表机构

• Xi’an Jiaotong University（西安交通大学）
• Beijing University of Posts and Telecommunications（北京邮电大学）
• Shanghai Jiao Tong University（上海交通大学）

机构由 AI 辅助整理，请以论文原文为准。