用多智能体强化学习实现低空飞行天线的毫秒级动态优化。
Low-Altitude Fluid Antenna Network with Multi-Agent Reinforcement Learning
- 基于电磁数字孪生与多智能体强化学习，实现天线阵列快速重配置。
- 相比固定位置方案，系统总速率提升118.5%。
- 适合研究低空通信、智能无线网络的科研人员。
低空无线网络（LAWN）融合地面与空中平台，为无人机和电动垂直起降飞行器提供无处不在的通信、感知与定位服务。然而，动态的空-地与空-空信道、突发阻塞及异构干扰阻碍了该目标的实现。流体天线（FA）作为一种前沿的多输入多输出（MIMO）技术，通过重构天线位置以释放额外的空间自由度，克服上述挑战。本文面向实现低空FA网络，研究基于多智能体强化学习（MARL）的快速高效FA重配置。具体而言，提出一种电磁数字孪生（EM-DT）辅助的MARL框架，并引入两阶段迁移学习以弥合仿真到现实的差距。案例研究显示，联合优化天线位置与波束成形可使系统总速率提升118.5%，该增益源于天线阵列在毫秒级时间尺度上的动态重配置以及对移动空中用户的自适应波束引导。
原文摘要 · Abstract (English)
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.
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