The knowledge of the Line of Sight (LOS) or Non Line of Sight (NLOS) propagation condition is an useful information in wireless communication system. Such knowledge can be inferred by the analysis of the signal, by using specific signal structures (e.g., preambles) or by the application of machine learning (ML) algorithms. In recent times, deep learning (DL) has been applied with success to the classification of LOS/NLOS conditions but with a significant computational time, which can be a practical issue in computing constrained devices. On the other hand, ML relies on the identification of key discriminating features, which can enhance the classification performance. This paper explores the application of entropy metrics to this classification problem. Beyond Shannon entropy, researchers have developed various entropy metrics in recent years in various domains (e.g., healthcare), but they have been scarcely applied to UWB LOS/NLOS classification to the knowledge of the author. This paper addresses this gap by applying entropy metrics in combination to machine learning classifiers to the public eWINE dataset, characterized by 7 different propagation environments where UWB signals were transmitted and recorded in LOS and NLOS conditions. The results presented in this paper show that entropy metrics can significantly enhance the LOS/NLOS classification accuracy and can produce an overall competitive performance. In addition, this paper presents a novel instance selection approach based on the use of entropy metrics, which is demonstrated to significantly outperform even the direct application of some DL algorithms on the basis of the results presented in literature on the same eWine data set.

BALDINI Gianmarco; 

2026-09-14

MDPI

JRC147756

1099-4300 (online),   

https://www.mdpi.com/1099-4300/28/8/904,    https://publications.jrc.ec.europa.eu/repository/handle/JRC147756,   

10.3390/e28080904 (online),