Low-Altitude Fluid Antenna Network with Multi-Agent Reinforcement Learning

2026-08-28

AI Summary

本文通过多智能体强化学习优化低空流体天线网络的动态重构，以解决动态信道、遮挡和干扰问题，提高系统总速率。

Abstract

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.

Problem

• Low-altitude Wireless Networks
• Dynamic Channels
• Interference

Innovation

• Multi-Agent Reinforcement Learning
• Fluid Antenna
• Electromagnetic Digital Twin
• Transfer Learning