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.

翻译：极大规模多输入多输出（XL-MIMO）是一种能够实现未来无线通信多种应用前景的技术。要充分发挥其巨大的潜在性能增益，精确的信道状态信息是基础性的技术前提。在传统大规模MIMO中，信道通常采用远场平面波前模型，并在角度域具有丰富的稀疏性，这便于设计低复杂度的信道估计方案。然而，在XL-MIMO系统中，由于近场球面波前不可忽略，这种稀疏性并不显著。为应对角度域信道估计方案固有的性能损失，我们首先基于近场信道的极域稀疏性，通过改进现有MRDN方案，提出面向XL-MIMO系统的极域多重残差密集网络（P-MRDN）。此外，进一步设计了极域多尺度残差密集网络（P-MSRDN）以提升信道估计精度。最后，仿真结果表明，所提方案相较于现有基准方案具有优越性能，且信道稀疏性对所提方案的影响极小。