L-Moment-Based LOS and NLOS Channel Characterization via Four-parameter Kappa Distribution for AoA BLE CTE Measurements
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866908802797797376 |
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| author | Talebian, Hamed Mahmood, Aamir Gidlund, Mikael |
| author_facet | Talebian, Hamed Mahmood, Aamir Gidlund, Mikael |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_01229 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | L-Moment-Based LOS and NLOS Channel Characterization via Four-parameter Kappa Distribution for AoA BLE CTE Measurements Talebian, Hamed Mahmood, Aamir Gidlund, Mikael Information Theory Probability 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. |
| title | L-Moment-Based LOS and NLOS Channel Characterization via Four-parameter Kappa Distribution for AoA BLE CTE Measurements |
| topic | Information Theory Probability |
| url | https://arxiv.org/abs/2602.01229 |