Multi panel massive MIMO improves user location despite clock errors
Localization in Multi-Panel Massive MIMO With Clock Asynchronism: A Unified Approach
Information Theory
Summary
Locating devices accurately indoors or around buildings is hard because signals can be blocked or distorted, and different parts of the system may not keep perfect time. The authors created a method called UNILocMP that combines geometric models with a smart way to map signal channels, handling timing mismatches between parts of a big antenna setup. Their approach groups users based on signal type and improves position estimates for each group. Tests show their technique is better than other common methods and that using multiple antenna panels helps a lot, even when clocks aren't synchronized.
What this means in practice
- •For wireless network engineers: Improve accuracy and robustness of user localization in multi-panel massive MIMO base stations with unsynchronized clocks.
- •For indoor positioning system designers: Enhance location estimation methods for environments with limited line-of-sight and clock errors using combined geometric and data-driven models.
Authors
Yuhao Zhang, Guangjin Pan, Musa Furkan Keskin, Carmen D'Andrea, Stefano Buzzi, Henk Wymeersch
Abstract
In this work, we propose a unified localization framework, termed UNILocMP, that combines model-based geometry and channel charting (CC) for multi-panel massive multiple-input-multiple-output (MIMO) under clock asynchronism. Owing to the multi-panel architecture, users are classified into multi-line-of-sight (LoS) users, which maintain LoS links with at least two panels, and single/non-LoS users, which maintain a LoS link with only one panel or with none of the panels. For multi-LoS users, a joint position and clock bias estimation is developed; while for single/non-LoS users, an unsupervised CC model is trained with a two-stage data augmentation strategy, where a clock-bias aware dissimilarity metric is introduced. It is numerically validated that the proposed UNILocMP outperforms model-based and CC-based baselines and achieves acceptable performance compared with fully-supervised fingerprinting. Moreover, for a fixed total number of antennas, the multi-panel architecture significantly improves localization accuracy and robustness compared with a single-panel base station (BS) deployment, particularly in the presence of clock asynchronism.