A Multi-Scale Spatial Attention Network for Near-Field MIMO Channel Estimation

The deployment of extremely large-scale antenna array (ELAA) brings higher spectral efficiency and spatial degree of freedom, but triggers issues on near-field channel estimation. Inspired by the success of deep learning (DL) in far-field channel estimation, this paper proposes a novel spatial-attention-based method to reconstruct extremely large-scale MIMO (XL-MIMO) channel. Initially, the spatial antenna correlation in near-field channels is drawn as the expectation over spatial region, different from only over spatial angle in far-field channels. The spatial antenna correlation implies that the near-field channel exhibits spatial nonstationarity, that the inter-antenna correlation vary with the antenna index and spatial regions and reveals the weakness of the widely applied convolutional neural network (CNN) with fixed receptive field. Subsequently, we develop a multi-scale spatial attention network (MsSAN) with low computational cost to enhance near-field MIMO channel estimation. In MsSAN, the channel is refined to subchannels of different scales layer by layer and each subchannel is treated as a whole and the spatial attention (SA) map is calculated by the sum of dot products of inter-subchannel so that the complexity grows linearly with channel size. Simulation results are presented to validate the proposed MsSAN with low computational cost outperforms others in terms of near-field channel reconstruction.