L-Moment-Based LOS and NLOS Channel Characterization via Four-parameter Kappa Distribution for AoA BLE CTE Measurements

2026-02-01

AI Summary

This study addresses the limitations of existing BLE Angle-of-Arrival (AoA) research, which suffers from a lack of rigorously paired line-of-sight (LOS) and non-line-of-sight (NLOS) in-phase/quadrature (IQ) measurement data and the inadequacy of conventional flat-fading models in capturing the heavy-tailed characteristics of indoor channels, leading to degraded localization accuracy under NLOS conditions. By collecting a large-scale dataset of 132,000 geometrically paired, labeled Constant Tone Extension (CTE) packets, this work pioneers the integration of L-moment ratios (LMRs) with the four-parameter Kappa distribution to accurately model and discriminate between LOS and NLOS channels. An L-moment-based self-supervised clustering and multivariate anomaly detection approach achieves clear separation of the two channel types in L-moment ratio diagrams. Experimental results demonstrate that 92% of power feature variance differences are statistically significant, NLOS exhibits heavier tails and stronger skewness, and the Kappa distribution provides optimal fit under NLOS, yielding near-zero normalized L-kurtosis bias.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

Bluetooth Low Energy (BLE) CTE transmissions provide in-phase and quadrature (IQ) samples whose empirical statistics are strongly governed by the propagation regime. in particular, the distributions differ markedly between line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. In NLOS, multipath-induced distortions typically degrade Angle-of-Arrivial (AoA) estimation accuracy. Existing BLE direction finding datasets rarely provide tightly controlled, IQ-level paired LOS and NLOS measurements with rigorous statistical validation, and commonly used flat-fading models can be inadequate for cluttered indoor environments exhibiting heavy-tailed power distributions. To address these limitations, we conduct a paired-geometry BLE AoA measurement campaign using an off-the-shelf module, collecting 132000 labeled CTE packets under matched anchor-tag conditions. A robust preprocessing stage removes anomalous CTEs using combined univariate and multivariate criteria. Feature-wise hypothesis tests on IQ-derived power features confirm strong LOS and NLOS separability. All mean differences are statistically significant; additionally, 92 percent of feature-wise variance differences are significant. We further compute L-moment ratios (LMRs) and analyze them in the L-moment Ratio Diagram (LMRD), showing that NLOS subsets exhibit markedly heavier tails and stronger asymmetry than LOS. Kappa-family distributions fitted from LMRs provide substantially improved dual scored L--moment goodness-of-fit (GoF), Specifically, for NLOS, which is the smallest discrepancy in the LMRD and a near-zero standardized L-kurtosis deviation. As a practice, we apply a self-supervised clustering to L-moment statistics, achieving a more separable representation, compared to product moments.

Problem

• BLE AoA
• LOS/NLOS channel characterization
• heavy-tailed distributions
• IQ-level measurements
• multipath distortion

Innovation

• L-moments
• Kappa distribution
• BLE AoA
• LOS/NLOS classification
• heavy-tailed distributions