Distributed Beamforming for Airborne Massive MIMO
Research Paper#Wireless Communication, Massive MIMO, Deep Reinforcement Learning🔬 Research|Analyzed: Jan 3, 2026 16:55•
Published: Dec 29, 2025 23:25
•1 min read
•ArXivAnalysis
This paper addresses the challenge of providing wireless coverage in remote or dense areas using aerial platforms. It proposes a novel distributed beamforming framework for massive MIMO networks, leveraging a deep reinforcement learning approach. The key innovation is the use of an entropy-based multi-agent DRL model that doesn't require CSI sharing, reducing overhead and improving scalability. The paper's significance lies in its potential to enable robust and scalable wireless solutions for next-generation networks, particularly in dynamic and interference-rich environments.
Key Takeaways
- •Proposes a distributed beamforming framework for massive MIMO networks using aerial platforms (HAPSs/HABs).
- •Employs an entropy-based multi-agent deep reinforcement learning (DRL) model.
- •The model doesn't require CSI sharing, reducing overhead.
- •Outperforms ZF and MRT, especially in high-interference scenarios.
- •Demonstrates scalability with increasing users and cluster configurations.
Reference / Citation
View Original"The proposed method outperforms zero forcing (ZF) and maximum ratio transmission (MRT) techniques, particularly in high-interference scenarios, while remaining robust to CSI imperfections."