Deep‐Learned Channel Estimation for MIMO‐OFDM System by Exploiting Frequency‐Space Correlation

ABSTRACT In massive MIMO–OFDM systems, the accurate acquisition of channel state information is crucial for ensuring reliable data transmission. As the antenna array size and signal bandwidth increase, wireless channel often exhibits sparsity in the angular and delay domains. However, the random distribution of spatial angles poses a persistent challenge for convolutional neural networks to extract spatial features effectively for channel estimation. To address this issue, we proposes a novel channel estimation network termed the attentive residual autoencoder network. Leveraging an autoencoder architecture enhanced with attention mechanisms and residual connections, the proposed method effectively captures frequency‐space correlation. Numerical results show that the proposed algorithm significantly outperforms existing channel estimation methods.