Robust Beamforming for Massive MIMO Aerial Communications
Research Paper#Wireless Communications, Massive MIMO, Beamforming, Deep Reinforcement Learning🔬 Research|Analyzed: Jan 3, 2026 18:25•
Published: Dec 29, 2025 23:50
•1 min read
•ArXivAnalysis
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 / Citation
View Original"The proposed method demonstrates superiority over baseline schemes in terms of average sum rate, robustness to CSI imperfection, user mobility, and scalability."