arXiv subjects

Hamed Talebian

Publications and source records attributed to Hamed Talebian.

3 recordsLinked to original sources

arXiv2026

Lightweight Non-Line-of-Sight Channel Detection for ML-assisted Bluetooth Direction Finding

Bluetooth Low Energy (BLE) direction-finding is promising for indoor industrial localization, but its accuracy degrades in multipath environments where reflections and scattering bias angle estimates. Although line-of-sight (LOS) and non-line-of-sight (NLOS) detection is well studied for wide-band radios, BLE direction-finding still lacks narrow-band channel-feature representations, scalable kernel-based feature transformations, and dedicated datasets for data-driven, lightweight channel classification. To address this gap, the work introduces a controlled BLE measurement setup that generates labeled LOS/NLOS data in two distinct propagation environments. A quality-driven machine learning (ML)-based pipeline is then developed for BLE Constant Tone Extension (CTE) In-phase-Quadrature (IQ) features. First, robust quantile-based standardization is applied to reduce the influence of outliers and heavy-tailed effects. The standardized features are then analyzed using Principal Component Analysis (PCA) and Adaptive Kernel Density Estimation (AKDE) to verify scenario-dependent statistics and reveal LOS/NLOS separability. Next, Nystr\"om Kernel Approximation (NKA) constructs low-rank nonlinear feature maps followed by a lightweight Support Vector Classifier (SVC) head for LOS/NLOS detection. This classifier is compared with Random Forest (RF) and Multilayer Perceptron (MLP) models. Results show that NKA improves accuracy by about 7-14% relative to the raw baseline. Although the MLP achieves higher absolute accuracy, the Nystr\"om--SVC approach offers a more favorable trade-off between training complexity, inference cost, and memory footprint. Finally, several pipeline-calibrated posterior probabilities are utilized for cost-aware threshold selection and efficient real-time LOS/NLOS detection in resource-constrained localization systems.

cs.IT↗

arXiv2026

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

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.

cs.IT↗

arXiv2025

A Robust 5G Terrestrial Positioning System with Sensor Fusion in GNSS-denied Scenarios

This paper presents a terrestrial localization system based on 5G infrastructure as a viable alternative to GNSS, particularly in scenarios where GNSS signals are obstructed or unavailable. It discusses network planning aimed at enabling positioning as a primary service, in contrast to the traditional focus on communication services in terrestrial networks. Building on a network infrastructure optimized for positioning, the paper proposes a system that leverages carrier phase (CP) ranging in combination with trilateration to localize the user within the network when at least three base stations (BSs) provide line-of-sight (LOS) conditions. Achieving accurate CP-based positioning requires addressing three key challenges: integer ambiguity resolution, LOS/NLOS link identification, and localization under obstructed LOS conditions. To this end, the system employs a multi-carrier CP approach, which eliminates the need for explicit integer ambiguity estimation. Additionally, a deep learning model is developed to identify NLOS links and exclude them from the trilateration process. In cases where LOS is obstructed and CP ranging becomes unreliable, the system incorporates an error-state extended Kalman filter to fuse complementary data from other sensors, such as inertial measurement units (IMUs) and cameras. This hybrid approach enables robust tracking of moving users across diverse channel conditions. The performance of the proposed terrestrial positioning system is evaluated using the real-world KITTI dataset, featuring a moving vehicle in an urban environment. Simulation results show that the system can achieve a positioning error of less than 5 meters in the KITTI urban scenario--comparable to that of public commercial GNSS services--highlighting its potential as a resilient and accurate solution for GNSS-denied environments.

cs.IT↗