Research Paper#Wireless Communications, Massive MIMO, Beamforming, Deep Reinforcement Learning🔬 ResearchAnalyzed: Jan 3, 2026 18:25
Robust Beamforming for Massive MIMO Aerial Communications
Published:Dec 29, 2025 23:50
•1 min read
•ArXiv
Analysis
This paper addresses the critical challenge of beamforming in massive MIMO aerial networks, a key technology for future communication systems. The use of a distributed deep reinforcement learning (DRL) approach, particularly with a Fourier Neural Operator (FNO), is novel and promising for handling the complexities of imperfect channel state information (CSI), user mobility, and scalability. The integration of transfer learning and low-rank decomposition further enhances the practicality of the proposed method. The paper's focus on robustness and computational efficiency, demonstrated through comparisons with established baselines, is particularly important for real-world deployment.
Key Takeaways
- •Proposes a distributed beamforming framework for massive MIMO aerial networks.
- •Employs a novel entropy-based multi-agent DRL approach with FNO.
- •Integrates transfer learning and low-rank decomposition for scalability and robustness.
- •Demonstrates superior performance compared to various baseline methods.
- •Achieves significant computational efficiency and reduced communication overhead.
Reference
“The proposed method demonstrates superiority over baseline schemes in terms of average sum rate, robustness to CSI imperfection, user mobility, and scalability.”