Transformer-Based Channel Estimation for Massive MIMO Systems
Dr. Wei Zhang, Prof. Yong Li, Dr. Hao Chen
Tsinghua University
Abstract
This paper proposes a novel transformer-based architecture for channel estimation in massive MIMO systems. By leveraging self-attention mechanisms, our model captures spatial-temporal correlations in channel state information more effectively than existing CNN or RNN-based approaches. Experiments on both synthetic and real-world datasets demonstrate a 15% improvement in estimation accuracy with 30% less computational overhead compared to state-of-the-art deep learning methods.
AI Summary
- Introduces a transformer architecture specifically designed for wireless channel estimation in massive MIMO.
- Achieves 15% better accuracy than existing CNN/RNN methods with 30% less compute.
- Validated on both synthetic and real-world 5G NR datasets from three urban environments.
- Proposes a novel positional encoding scheme tailored for antenna array geometry.
Key Findings
- 1Self-attention captures long-range spatial dependencies between antenna elements more effectively than convolution.
- 2The proposed positional encoding for antenna arrays reduces estimation error by 8% alone.
- 3Model generalizes well across different channel conditions without retraining.
Industry Implications
Could significantly reduce pilot overhead in 5G Advanced and 6G systems.
Enables more efficient use of massive MIMO in urban deployments.
Provides a foundation for AI-native air interface design in future 6G standards.
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