Research Paper#Wireless Communication, Machine Learning, Power Allocation🔬 ResearchAnalyzed: Jan 3, 2026 16:23
Hybrid Tree-Transformer for Scalable Power Allocation
Published:Dec 27, 2025 16:23
•1 min read
•ArXiv
Analysis
This paper addresses the computational bottleneck of Transformer models in large-scale wireless communication, specifically power allocation. The proposed hybrid architecture offers a promising solution by combining a binary tree for feature compression and a Transformer for global representation, leading to improved scalability and efficiency. The focus on cell-free massive MIMO systems and the demonstration of near-optimal performance with reduced inference time are significant contributions.
Key Takeaways
- •Proposes a hybrid Tree-Transformer architecture for scalable power allocation.
- •Addresses the computational limitations of Transformer models in large-scale wireless networks.
- •Achieves near-optimal performance with reduced inference time in cell-free massive MIMO systems.
- •Offers efficient inference across large and variable user sets without retraining.
Reference
“The model achieves logarithmic depth and linear total complexity, enabling efficient inference across large and variable user sets without retraining or architectural changes.”