用双注意力机制提升大规模MIMO信道估计精度
Dual-Attention Based 3D Channel Estimation
- 设计双注意力结构，联合建模时频空域相关性
- 在相关信道下性能接近最优线性估计，显著优于传统方法
- 适合高维MIMO系统，尤其适用于深度学习信道估计场景
对于多输入多输出（MIMO）信道，基于线性最小均方误差（LMMSE）的最优信道估计（CE）需要三维（3D）滤波。然而，由于矩阵维度庞大，计算复杂度常难以承受。次优估计器通过将3D CE分解为时、频、空三个域进行近似，但在相关MIMO信道下会带来明显性能下降。近年来，深度学习（DL）可通过注意力机制在所有域中挖掘信道相关性。基于此能力，我们提出一种基于双注意力机制的3D信道估计网络（3DCENet），可实现高精度估计。
原文摘要 · Abstract (English)
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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