Sensors, Vol. 19, Pages 4229: Deep Learning-Based LOS and NLOS Identification in Wireless Body Area Networks
Sensors, Vol. 19, Pages 4229: Deep Learning-Based LOS and NLOS Identification in Wireless Body Area Networks
Sensors doi: 10.3390/s19194229
Authors:
Krzysztof K. Cwalina
Piotr Rajchowski
Olga Blaszkiewicz
Alicja Olejniczak
Jaroslaw Sadowski
In this article, the usage of deep learning (DL) in ultra-wideband (UWB) Wireless Body Area Networks (WBANs) is presented. The developed approach, using channel impulse response, allows higher efficiency in identifying the direct visibility conditions between nodes in off-body communication with comparison to the methods described in the literature. The effectiveness of the proposed deep feedforward neural network was checked on the basis of the measurement data for dynamic scenarios in an indoor environment. The obtained results clearly prove the validity of the proposed DL approach in the UWB WBANs and high (over 98.6% for most cases) efficiency for LOS and NLOS conditions classification.
Source: Sensors - Category: Biotechnology Authors: Krzysztof K. Cwalina Piotr Rajchowski Olga Blaszkiewicz Alicja Olejniczak Jaroslaw Sadowski Tags: Article Source Type: research