@inproceedings{978b06ce47cf45fda31549bdd5f01e82,
title = "Pathloss-based non-Line-of-Sight Identification in an Indoor Environment: An Experimental Study",
abstract = "We report the findings of an experimental study on the problem of line-of-sight (LOS)/non-line-of-sight (NLOS) classification in an indoor environment. Specifically, we deploy a pair of NI 2901 USRP software-defined radios (SDR) in a large hall, which communicate on a center frequency of 2.4 GHz, using three different signal-to-noise ratios (SNR). The receive SDR constructs a dataset of pathloss measurements from the received signal as it moves across 15 equi-spaced positions on a 1D grid (for both LOS and NLOS scenarios). This allows us to estimate the pathloss parameters using the least-squares method to construct a parameterized pathloss model for a binary hypothesis test (BHT) for NLOS identification. Since the pathloss measurements slightly deviate from Gaussian distribution, we also feed our custom dataset to a range of machine learning (ML) algorithms. It turns out that the performance of the ML algorithms is only slightly superior to the Neyman-Pearson-based BHT.",
keywords = "binary hypothesis test, classification, least-squares, line-of-sight (LOS), machine learning, non-line-of-sight (NLOS), support vector machine",
author = "M. Asim and Iqbal, \{M. Ozair\} and Waqas Aman and \{Ur Rahman\}, \{M. Mahboob\} and Abbasi, \{Qammer H.\}",
note = "Publisher Copyright: {\textcopyright} 2024 18th European Conference on Antennas and Propagation, EuCAP 2024. All Rights Reserved.; 18th European Conference on Antennas and Propagation, EuCAP 2024 ; Conference date: 17-03-2024 Through 22-03-2024",
year = "2024",
doi = "10.23919/EuCAP60739.2024.10501521",
language = "English",
series = "18th European Conference on Antennas and Propagation, EuCAP 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "18th European Conference on Antennas and Propagation, EuCAP 2024",
address = "United States",
}