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

arXiv: https://arxiv.org/abs/2608.27909 · HTML: https://mlsanity.com/paper/2608.27909/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 15 references that are in the map.
- First posted on arXiv 28 Aug 2026.

## Builds on (most cited first, 9 of 9)

- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [MLPerf Inference Benchmark](https://mlsanity.com/paper/1911.02549/) (2019)
- [Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics](https://mlsanity.com/paper/1910.01465/) (2019)
- [Generative AI-enabled Wireless Communications for Robust Low-Altitude Economy Networking](https://mlsanity.com/paper/2502.18118/) (2025)
- [Low-Altitude Wireless Networks: A Comprehensive Survey](https://mlsanity.com/paper/2509.11607/) (2025)
- [Fluid Antenna Systems Enabling 6G:Principles, Applications, and Research Directions](https://mlsanity.com/paper/2412.03839/) (2024)
- [Fluid Antenna Systems Meet Low-Altitude Wireless Networks: Fundamentals, Opportunities, and Future Directions](https://mlsanity.com/paper/2506.13250/) (2025)
- [Scalable Fluid Antenna Systems: A New Paradigm for Array Signal Processing](https://mlsanity.com/paper/2508.10831/) (2025)
- [UAV-Enabled Fluid Antenna Systems for Multi-Target Wireless Sensing over LAWCNs](https://mlsanity.com/paper/2509.22497/) (2025)

## Abstract (arXiv)

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.