Dual-Attention Based 3D Channel Estimation
This addresses channel estimation for MIMO communications, likely incremental as it builds on existing attention mechanisms.
The paper tackles the high complexity and performance degradation of 3D channel estimation in MIMO systems by proposing a dual-attention mechanism based deep learning network (3DCENet) that achieves accurate estimates.
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.