Channel Estimation for XL-MIMO Systems with Polar-Domain Multi-Scale Residual Dense Network

TL;DR

This paper introduces a novel polar-domain multi-scale residual dense network for accurate channel estimation in XL-MIMO systems, effectively addressing near-field effects and outperforming existing methods.

Contribution

It proposes the P-MRDN and P-MSRDN models that leverage polar-domain sparsity and multi-scale features for improved near-field channel estimation in XL-MIMO systems.

Findings

P-MRDN outperforms benchmark schemes in simulation.

P-MSRDN achieves higher estimation accuracy.

Proposed methods are robust to channel sparsity variations.

Abstract

Extremely large-scale multiple-input multiple-output (XL-MIMO) is a promising technique to enable versatile applications for future wireless communications.To realize the huge potential performance gain, accurate channel state information is a fundamental technical prerequisite. In conventional massive MIMO, the channel is often modeled by the far-field planar-wavefront with rich sparsity in the angular domain that facilitates the design of low-complexity channel estimation. However, this sparsity is not conspicuous in XL-MIMO systems due to the non-negligible near-field spherical-wavefront. To address the inherent performance loss of the angular-domain channel estimation schemes, we first propose the polar-domain multiple residual dense network (P-MRDN) for XL-MIMO systems based on the polar-domain sparsity of the near-field channel by improving the existing MRDN scheme. Furthermore, a polar-domain multi-scale residual dense network (P-MSRDN) is designed to improve the channel estimation accuracy. Finally, simulation results reveal the superior performance of the proposed schemes compared with existing benchmark schemes and the minimal influence of the channel sparsity on the proposed schemes.