Hard/Soft NLoS Detection via Combinatorial Data Augmentation for 6G Positioning

2026-03-09

AI Summary

This work addresses the challenge of achieving high-precision 6G localization in dynamic environments where real-time acquisition of line-of-sight (LoS) and non-line-of-sight (NLoS) channel states remains difficult, thereby limiting positioning accuracy. To overcome this limitation, the authors propose a Combinatorial Data Augmentation-guided NLoS Detection method (CDA-ND), which introduces, for the first time, a combinatorial data augmentation mechanism to generate NLoS evidence vectors. By integrating multi-base-station coarse localization, clustering analysis, and probabilistic confidence modeling, CDA-ND enables effective fusion of hard and soft decisions without requiring complete environmental priors. Evaluated in indoor factory scenarios, the method achieves NLoS detection accuracies of 96.6% under 18% NLoS conditions and 91.1% under 56% NLoS conditions, reducing the mean absolute positioning error by 20.04% and 65.99%, respectively.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Local Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

A key enabler for meeting the stringent requirements of 6G positioning is the ability to exploit site-dependent information governing line-of-sight (LoS) and non-line-of-sight (NLoS) propagation. However, acquiring such environmental information in real time is challenging in practice. To address this issue, we propose a novel NLoS detection algorithm termed combinatorial data augmentation-guided NLoS detection (CDA-ND), which builds upon our prior work. CDA-ND generates numerous preliminary estimated locations (PELs) by applying multilateration over many gNodeB (gNB) combinations using a single snapshot of range measurements. When a target gNB is in NLoS, the resulting PELs split into two clusters: one derived using the target gNB's range measurement and the other derived without it. Their displacement is summarized by a single vector, called the NLoS evidence vector (NEV), which is used to compute an NLoS likelihood score. Based on this score, two modes of NLoS detection are developed. First, each gNB is classified as LoS or NLoS, termed hard decision (HD), using a simple threshold test. Second, each gNB's NLoS confidence is probabilistically quantified, termed soft decision (SD), which extends HD with weak site-survey priors, namely empirical NLoS-score samples and the average NLoS probability. We then design positioning algorithms tailored to these two modes by excluding gNBs deemed NLoS and re-weighting the remaining gNBs for SD. The proposed CDA-ND achieves high reliability in indoor factory environments under frequency range 1, attaining NLoS detection accuracies of 96.6% and 91.1% when the proportion of NLoS gNBs is approximately 18% and 56%, respectively. As a result, integrating CDA-ND into positioning significantly reduces mean absolute error by 20.04% and 65.99% in LoS- and NLoS-dominant environments, respectively.

Problem

• 6G positioning
• NLoS detection
• line-of-sight
• non-line-of-sight
• environmental information

Innovation

• Combinatorial Data Augmentation
• NLoS Detection
• 6G Positioning
• Soft Decision
• NLoS Evidence Vector