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

TL;DR

This paper introduces CDA-ND, a novel NLoS detection algorithm for 6G positioning that uses combinatorial data augmentation to improve accuracy by effectively identifying and excluding NLoS signals.

Contribution

The paper presents a new NLoS detection method that generates multiple location estimates and uses a likelihood score for reliable classification, enhancing 6G positioning accuracy.

Findings

Achieves 96.6% NLoS detection accuracy in indoor factory environments.

Reduces mean absolute positioning error by up to 66%.

Effectively distinguishes LoS and NLoS signals in complex environments.

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.