Search
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 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.
Citations
-
2 6
CrossRef
-
0
Web of Science
-
2 8
Scopus
Authors (5)
Cite as
Full text
download paper
downloaded 74 times
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.3390/s19194229
- License
-
open in new tab
Keywords
Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
SENSORS
no. 19,
ISSN: 1424-8220 - Language:
- English
- Publication year:
- 2019
- Bibliographic description:
- Cwalina K., Rajchowski P., Błaszkiewicz O., Olejniczak A., Sadowski J.: Deep Learning-Based LOS and NLOS Identification in Wireless Body Area Networks// SENSORS -Vol. 19,iss. 19 (2019), s.4229-
- DOI:
- Digital Object Identifier (open in new tab) 10.3390/s19194229
- Sources of funding:
-
- Free publication
- Verified by:
- Gdańsk University of Technology
seen 195 times
Recommended for you
Deep Learning: A Case Study for Image Recognition Using Transfer Learning
- S. Erpolat Tasabat,
- O. Aydin
2021