Sensing-Enhanced Channel Estimation for Near-Field XL-MIMO Systems

TL;DR

This work tackles the CE bottleneck in near-field XL-MIMO by integrating sensing into the CE pipeline. It introduces a cost-effective power-sensor XL-MIMO architecture and a time-inversion localization method that operates without baseband sampling, providing accurate UE/scatterer localization in the near field. The estimated locations inform a novel DPSS-based eigen-dictionary for near-field channel sparsification, reducing both baseband sampling and dictionary size while maintaining high CE accuracy. The approach yields substantial practical benefits, including up to 66% fewer baseband samples and up to 88% smaller dictionaries, enabling efficient uplink CE for 6G XL-MIMO systems.

Abstract

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 66%.

Figures (10)

• Figure 1: (a) The considered near-field transmission scenario and (b) the proposed dual-functional architecture for sensing-enhanced communication. RF switches are adopted on antenna elements in (b), which selectively activate either the $S$ branch for sensing or the $T$ branch for training.
• Figure 2: Proposed sensing-enhanced uplink CE protocol. One sensing signal $\mathbf{s}_\mathrm{S}$ is transmitted in the sensing ($S$) stage and $\tau$ pilot signals $\mathbf{s}_\mathrm{P}^{(t)}$ are transmitted subsequently in the training ($T$) stage.
• Figure 3: The detailed hardware schematic of antenna elements at the BS array 10042005942570.
• Figure 4: A demonstration of the proposed localization method with $N_{\rm BS}=512$ antenna elements and $L=3$ NLoS paths. The localization is operated with a carrier frequency of $f_c = 28$ GHz. The red line on the $x$-axis denotes the aperture of XL-MIMO array, while the circles mark the coordinates of the detected multiple sources.
• Figure 5: (a) The CDF of localization error of the considered algorithms with varying oversampling rate $\eta$ and (b) RMSE of the proposed localization algorithm with varying carrier frequencies and XL-MIMO aperture.

Theorems & Definitions (7)

• Remark 1
• Proposition 1
• proof
• Proposition 2
• proof
• Remark 2
• Remark 3