Deep Learning-Based LOS and NLOS Identification in Wireless Body Area Networks.

Clicks: 454
ID: 60446
2019
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Abstract
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 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.
Reference Key
cwalina2019deepsensors Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Cwalina, Krzysztof K;Rajchowski, Piotr;Blaszkiewicz, Olga;Olejniczak, Alicja;Sadowski, Jaroslaw;
Journal sensors
Year 2019
DOI
E4229
URL
Keywords
Deep learning Machine learning los wban ban dwm1000 nlos uwb channel impulse response

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