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arXiv · 2604.15956

FP-ANeT: A Fixed-Point Attention Network for Hybrid-Field THz Ultra-massive MIMO Channel Estimation

Kangchun Zhao·Haitian Yang·Yijie Mao
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Abstract

Ultra-massive multiple-input multiple-output (UM-MIMO) is a key technology for enabling terahertz (THz) communications in 6G networks, offering high beamforming gain to combat severe path loss. However, the large antenna array expands the near-field region, resulting in a hybrid near- and far-field communication environment. This makes channel estimation significantly more challenging than in conventional networks. To address this issue, we propose a novel attention augmented channel estimator named the fixed-point attention network (FP-ANet), which integrates fixed-point theory with a dual-attention mechanism. By combining a linear and dual-attention residual blocks based non-linear estimator in each iteration, this model-driven approach effectively exploits the sparsity of THz channels in the angular-distance domain, enabling a more precise and physically-grounded channel estimation. Simulation results show that FP-ANet achieves superior channel estimation accuracy compared to state-of-the-art methods while maintaining comparable computational complexity.

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Kangchun Zhao, Haitian Yang, Yijie Mao. 2026-04-17. FP-ANeT: A Fixed-Point Attention Network for Hybrid-Field THz Ultra-massive MIMO Channel Estimation. https://arxiv.org/abs/2604.15956

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