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June 3, 2026Journal of Electromagnetic Engineering and ScienceOpen Access

Location Variability Confidence Interval Model Using Statistical Inference for Over-rooftop Paths and Signal Data Classification Method

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Authors

SYSang-Hoo YoonSPSang-Wook ParkYYYoung‐Keun Yoon

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Overview

This randomized trial predicts signal loss rates in suburban settings, highlighting improved measurement accuracy.

Key Points

  • The study aims to develop a model for predicting signal loss rates over distance in suburban environments under varying conditions.
  • Utilized statistical inference to derive a linear equation for signal loss rates in over-rooftop paths.
  • Introduced a Gaussian mixture model clustering algorithm to classify real-time measurements as either LoS or NLoS.
  • Established an 80% confidence interval for prediction accuracy.
  • The model achieved an 80% confidence interval with equivalent slope to existing regression equations.
  • The clustering algorithm classified signals with high accuracy under both LoS and NLoS conditions.

Cite This Study

Yoon et al. (2026) studied this question.

synapsesocial.com/papers/6a1fc4bbdee9eb8c0dce64abhttps://doi.org/10.26866/jees.2026.3.r.362
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