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Machine learning-enabled channel estimation for massive MIMO in high-speed railway communications
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DOI：10.1016/j.phycom.2026.103091.png)
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Abstract
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With the rapid development of 5 G mobile communications, achieving seamless wireless connectivity in highspeed rail scenarios remains challenging. Massive MIMO technology shows great promise, but channel estimation faces severe difficulties due to extreme Doppler effects and non-stationary propagation characteristics. Conventional LS and LMMSE algorithms suffer significant accuracy degradation under high mobility conditions and fail to exploit the temporal correlation of massive MIMO channels.This paper proposes a novel CNN-BiLSTM network architecture for channel estimation. The model employs a spherical coordinate system to represent multipath propagation, including line-of-sight, sea surface reflection, and single/double-bounced components. A DFT-based beam space representation reduces dimensionality and achieves channel sparsity. The architecture utilizes CNN layers for spatial feature extraction and BiLSTM layers for bidirectional temporal modeling. Additionally, a hybrid learning approach combining theoretical Rician and ray-traced channel models enhances generalization across various propagation conditions.Simulation results demonstrate that the proposed scheme achieves a 10.4 dB NMSE improvement over conventional LMMSE at 400 km/h, reduces pilot overhead by approximately 67 %, while maintaining sum-rate performance within 5 % of ideal CSI conditions.
Keywords:
Massive MIMO
High-speed rail communications
Channel estimation
BiLSTM
Doppler Effect
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