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

TL;DR

This paper introduces a multi-scale spatial attention network that leverages inter-subchannel correlations to improve near-field MIMO channel estimation, outperforming existing sparsity-based methods.

Contribution

The paper proposes a novel multi-scale spatial attention network (MsSAN) that models inter-subchannel correlations for near-field MIMO channel estimation, addressing limitations of transform-based schemes.

Findings

MsSAN outperforms existing methods in channel reconstruction accuracy.

The proposed spatial attention mechanism effectively captures inter-subchannel correlations.

Multi-scale architecture refines subchannel features for better estimation.

Abstract

The deployment of extremely large-scale array (ELAA) brings higher spectral efficiency and spatial degree of freedom, but triggers issues on near-field channel estimation. Existing near-field channel estimation schemes primarily exploit sparsity in the transform domain. However, these schemes are sensitive to the transform matrix selection and the stopping criteria. Inspired by the success of deep learning (DL) in far-field channel estimation, this paper proposes a novel spatial-attention-based method for reconstructing extremely large-scale MIMO (XL-MIMO) channel. Initially, the spatial antenna correlations of near-field channels are analyzed as an expectation over the angle-distance space, which demonstrate correlation range of an antenna element varies with its position. Due to the strong correlation between adjacent antenna elements, interactions of inter-subchannel are applied to describe inherent correlation of near-field channels instead of inter-element. Subsequently, a multi-scale spatial attention network (MsSAN) with the inter-subchannel correlation learning capabilities is proposed tailed to near-field MIMO channel estimation. We employ the multi-scale architecture to refine the subchannel size in MsSAN. Specially, we inventively introduce the sum of dot products as spatial attention (SA) instead of cross-covariance to weight subchannel features at different scales in the SA module. Simulation results are presented to validate the proposed MsSAN achieves remarkable the inter-subchannel correlation learning capabilities and outperforms others in terms of near-field channel reconstruction.