AI 中文总结
本文针对低空无线网络的信道与干扰挑战，提出电磁数字孪生辅助的多智能体强化学习框架，结合两阶段迁移学习，实现流体天线的快速高性能重构，使系统总速率较固定位置基线提升118.5%。
AI 中文摘要
低空无线网络（LAWNs）融合地面与空中平台，为无人机（UAV）及电动垂直起降（eVTOL）飞行器提供无处不在的通信、感知与定位服务。然而，动态空-地、空-空信道、突发遮挡及异构干扰阻碍了该目标的实现。流体天线（FA）作为一种前沿多输入多输出（MIMO）技术，可通过重构天线位置解锁额外空间自由度，克服上述挑战。本文为推动低空FA网络落地，研究采用多智能体强化学习（MARL）实现低空FA网络的快速高性能FA重构，提出电磁数字孪生（EM-DT）辅助的MARL框架；为弥合仿真-现实差距，引入两阶段迁移学习框架。案例研究表明，与固定位置基线相比，联合FA位置与波束成形优化可将系统总速率提升118.5%，该增益源于FA阵列的毫秒级动态重构及波束向移动空中用户的自适应转向。
英文摘要
Low-altitude wireless networks (LAWNs) integrate terrestrial and aerial platforms to provide ubiquitous communication, sensing, and localization services for unmanned aerial vehicles (UAVs) and electric vertical takeoff and landing (eVTOL) aircraft. However, dynamic air-ground and air-air channels, abrupt blockages, and heterogeneous interference hinder the realization of this goal. Nevertheless, fluid antenna (FA), a cutting-edge multiple-input multiple-output (MIMO) technique, overcomes these challenges by reconfiguring antenna positions to unlock additional spatial degrees-of-freedom. In this paper, towards bringing low-altitude FA networks into reality, we study the fast and high-performance FA reconfiguration for low-altitude FA networks with multi-agent reinforcement learning (MARL). Specifically, we present an electromagnetic digital twin (EM-DT)-assisted MARL framework. To fill the sim-to-real gap, we introduce a two-stage transfer learning framework. Our case study shows that joint FA positions and beamforming optimization can enhance the system sum-rate by 118.5%, compared to the fixed position baseline. This gain comes from the dynamic millisecond timescale reconfiguration of FA arrays and the adaptive steering of beams toward aerial users with mobility.
CommentsAccepted by IEEE Communications Magazine
