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Dual-Attention Based 3D Channel Estimation

arXiv cs.LG / 4/3/2026

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Key Points

  • The paper addresses multi-input multi-output (MIMO) 3D channel estimation, noting that optimal LMMSE-based channel estimation effectively requires three-dimensional (time–frequency–space) filtering that is computationally expensive in practice.
  • Existing suboptimal approaches reduce complexity by decomposing 3D channel estimation into separate domains, but this can significantly degrade performance for correlated MIMO channels.
  • The authors propose 3DCENet, a dual-attention deep learning network designed to capture channel correlations across all domains more effectively than domain-decomposition methods.
  • The proposed dual-attention mechanism is claimed to enable accurate channel estimates while mitigating the prohibitive complexity of full 3D filtering.

Abstract

For multi-input and multi-output (MIMO) channels, the optimal channel estimation (CE) based on linear minimum mean square error (LMMSE) requires three-dimensional (3D) filtering. However, the complexity is often prohibitive due to large matrix dimensions. Suboptimal estimators approximate 3DCE by decomposing it into time, frequency, and spatial domains, while yields noticeable performance degradation under correlated MIMO channels. On the other hand, recent advances in deep learning (DL) can explore channel correlations in all domains via attention mechanisms. Building on this capability, we propose a dual attention mechanism based 3DCE network (3DCENet) that can achieve accurate estimates.

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