Near-field communications present new opportunities over near-field channels, however, the spherical wavefront propagation makes near-field signal processing challenging. In this context, this paper proposes efficient near-field channel estimation methods for wideband MIMO mmWave systems with the aid of extremely large-scale reconfigurable intelligent surfaces (XL-RIS). For the wideband signals reflected by the analog RIS, we characterize their near-field beam squint effect in both angle and distance domains. Based on the mathematical analysis of the near-field beam patterns over all frequencies, a wideband spherical-domain dictionary is constructed by minimizing the coherence of two arbitrary beams. In light of this, we formulate a two-dimensional compressive sensing problem to recover the channel parameter based on the spherical-domain sparsity of mmWave channels. To this end, we present a correlation coefficient-based atom matching method within our proposed multi-frequency parallelizable subspace recovery framework for efficient solutions. Additionally, we propose a two-dimensional oracle estimator as a benchmark and derive its lower bound across all subcarriers. Our findings emphasize the significance of system hyperparameters and the sensing matrix of each subcarrier in determining the accuracy of the estimation. Finally, numerical results show that our proposed method achieves considerable performance compared with the lower bound and has a time complexity linear to the number of RIS elements.

翻译：近场通信在近场信道中提供了新机遇，然而球面波前传播使得近场信号处理面临挑战。在此背景下，本文针对超大规模可重构智能表面辅助的宽带MIMO毫米波系统，提出了高效的近场信道估计方法。对于模拟RIS反射的宽带信号，我们从角度域和距离域两方面表征了其近场波束斜变效应。基于所有频率上的近场波束图样的数学分析，通过最小化任意两个波束的相干性，构建了一个宽带球面域字典。据此，我们基于毫米波信道的球面域稀疏性，将信道参数恢复问题建模为二维压缩感知问题。为此，我们在所提出的多频可并行子空间恢复框架内，提出了一种基于相关系数的原子匹配方法以实现高效求解。此外，我们提出了一种二维理想估计器作为基准，并推导了其在所有子载波上的下界。研究结果强调了系统超参数和各子载波感知矩阵在估计精度中的关键作用。最后，数值结果表明，本文方法相较于下界可实现显著性能，且时间复杂度与RIS单元数量呈线性关系。