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

2024-03-18

IEEE Journal on Selected Areas in Communications

AI Summary

To address the high-dimensional channel estimation challenge in 6G near-field extra-large-scale MIMO (XL-MIMO) systems induced by spherical-wave propagation, this paper proposes a sensing-enhanced uplink channel estimation paradigm. We introduce the first embedded power-sensor-driven joint near-field sensing and channel estimation framework operating within a single time slot. Time-reversal-based localization achieves millimeter-level accuracy for both users and scatterers. Furthermore, we uncover an intrinsic connection between near-field channel eigenmodes and discrete prolate spheroidal sequences (DPSS), enabling the construction of the first lightweight eigenmode dictionary specifically tailored for near-field XL-MIMO. Compared to conventional approaches, the proposed method reduces computational complexity by over 90%, decreases channel estimation error by 42%, compresses dictionary size by 75%, and cuts baseband sampling requirements by up to 66%.

Technology Category

• Machine Learning: Multimodal Learning
• Intelligent Robots: State Estimation
• Search and Optimization: Learning to Search

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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%.

Problem

• Estimating near-field XL-MIMO channels with reduced baseband samples
• Localizing users and scatterers using low-complexity power sensing
• Designing efficient dictionaries for near-field channel estimation

Innovation

• Power sensors measure received power pattern
• Time inversion algorithm estimates user locations
• Eigen-dictionary improves channel estimation efficiency