Future sixth-generation (6G) systems are expected to leverage extremely large-scale multiple-input multiple-output (XL-MIMO) technology, which significantly expands the range of the near-field region. The spherical wavefront characteristics in the near field introduce additional degrees of freedom (DoFs), namely distance and angle, into the channel model, which leads to unique challenges in channel estimation (CE). In this paper, we propose a new sensing-enhanced uplink CE scheme for near-field XL-MIMO, which notably reduces the required quantity of baseband samples and the dictionary size. In particular, we first propose a sensing method that can be accomplished in a single time slot. It employs power sensors embedded within the antenna elements to measure the received power pattern rather than baseband samples. A time inversion algorithm is then proposed to precisely estimate the locations of users and scatterers, which offers a substantially lower computational complexity. Based on the estimated locations from sensing, a novel dictionary is then proposed by considering the eigen-problem based on the near-field transmission model, which facilitates efficient near-field CE with less baseband sampling and a more lightweight dictionary. Moreover, we derive the general form of the eigenvectors associated with the near-field channel matrix, revealing their noteworthy connection to the discrete prolate spheroidal sequence (DPSS). Simulation results unveil that the proposed time inversion algorithm achieves accurate localization with power measurements only, and remarkably outperforms various widely-adopted algorithms in terms of computational complexity. Furthermore, the proposed eigen-dictionary considerably improves the accuracy in CE with a compact dictionary size and a drastic reduction in baseband samples by up to 77%.

翻译：未来第六代（6G）系统预计将采用超大规模多输入多输出（XL-MIMO）技术，该技术显著扩展了近场区域的范围。近场中的球面波前特性为信道模型引入了额外的自由度（DoF），即距离和角度，这给信道估计（CE）带来了独特挑战。本文提出了一种面向近场XL-MIMO的新型感知增强上行CE方案，该方案显著减少了所需的基带样本数量和字典规模。具体而言，我们首先提出一种可在单个时隙内完成的感知方法。该方法利用嵌入天线单元中的功率传感器测量接收功率模式，而非基带样本。接着提出一种时间反演算法，以精确估计用户和散射体的位置，该算法具有显著更低的计算复杂度。基于感知得到的位置估计，通过考虑近场传输模型的本征问题，提出了一种新型字典，从而能够以更少的基带采样和更轻量化的字典实现高效近场CE。此外，我们推导了与近场信道矩阵相关的特征向量的一般形式，揭示了其与离散长椭球序列（DPSS）的重要联系。仿真结果表明，所提出的时间反演算法仅通过功率测量即可实现精确定位，且在计算复杂度上显著优于多种广泛采用的算法。此外，所提出的本征字典以紧凑的字典规模，将基带样本量大幅减少高达77%，同时显著提升了CE的准确性。