Learning-driven MIMO channel estimation using a residual U-Net-BiLSTM-attention hybrid model.
This work introduces a hybrid residual U-Net-bidirectional long short-term memory with attention (ResUNet-BiLSTM-Attention) channel estimator that learns a nonlinear mapping from noisy pilot observations to MIMO channel coefficients and consistently outperforms both conventional and learning-based baselines.