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.

中文翻译：

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超大规模可重构智能表面 (XL-RIS) 辅助宽带毫米波系统的近场信道估计


近场通信为近场信道带来了新的机遇，然而，球形波前传播使近场信号处理面临挑战。在此背景下，本文借助超大规模可重构智能表面（XL-RIS）提出了宽带 MIMO 毫米波系统的高效近场信道估计方法。对于模拟 RIS 反射的宽带信号，我们在角度和距离域中表征了它们的近场光束斜视效应。基于对所有频率上的近场波束方向图的数学分析，通过最小化两个任意波束的相干性来构建宽带球域字典。鉴于此，我们提出了一个二维压缩感知问题，以基于毫米波信道的球域稀疏性来恢复信道参数。为此，我们在提出的多频可并行子空间恢复框架中提出了一种基于相关系数的原子匹配方法，以实现有效的解决方案。此外，我们提出了一个二维预言估计器作为基准，并得出其跨所有子载波的下界。我们的研究结果强调了系统超参数和每个子载波的感知矩阵在确定估计准确性方面的重要性。最后，数值结果表明，与下界相比，我们提出的方法取得了相当可观的性能，并且时间复杂度与 RIS 元素的数量呈线性关系。