Distributed Beamforming in Massive MIMO Communication for a Constellation of Airborne Platform Stations

TL;DR

This paper introduces a distributed beamforming approach for massive MIMO airborne platforms using multi-agent deep reinforcement learning, which reduces overhead and improves performance in interference-heavy environments.

Contribution

It presents a novel entropy-based multi-agent DRL framework for distributed beamforming that does not require CSI sharing among aerial platform stations.

Findings

Outperforms ZF and MRT in high-interference scenarios

Robust to imperfect CSI

Scalable with increasing users and clusters

Abstract

Non-terrestrial base stations (NTBSs), including high-altitude platform stations (HAPSs) and hot-air balloons (HABs), are integral to next-generation wireless networks, offering coverage in remote areas and enhancing capacity in dense regions. In this paper, we propose a distributed beamforming framework for a massive MIMO network with a constellation of aerial platform stations (APSs). Our approach leverages an entropy-based multi-agent deep reinforcement learning (DRL) model, where each APS operates as an independent agent using imperfect channel state information (CSI) in both training and testing phases. Unlike conventional methods, our model does not require CSI sharing among APSs, significantly reducing overhead. Simulations results demonstrate that our method outperforms zero forcing (ZF) and maximum ratio transmission (MRT) techniques, particularly in high-interference scenarios, while remaining robust to CSI imperfections. Additionally, our framework exhibits scalability, maintaining stable performance over an increasing number of users and various cluster configurations. Therefore, the proposed method holds promise for dynamic and interference-rich NTBS networks, advancing scalable and robust wireless solutions.