Distributed Beamforming in Massive MIMO Communication for a Constellation of Airborne Platform Stations
Hesam Khoshkbari Georges Kaddoum Bassant Selim Omid Abbasi Halim Yanikomeroglu
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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.引用本文复制引用
Hesam Khoshkbari,Georges Kaddoum,Bassant Selim,Omid Abbasi,Halim Yanikomeroglu.Distributed Beamforming in Massive MIMO Communication for a Constellation of Airborne Platform Stations[EB/OL].(2025-12-29)[2026-01-16].https://arxiv.org/abs/2512.23900.学科分类
通信/无线通信/计算技术、计算机技术
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