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.
Highlights
WBANs (Wireless Body Area Networks) have gained immense popularity due to their use in modern 5G networks, where users expect the quality and speed of data streaming services to be increased while maintaining mobility
From the WBANs designing procedure point of view, the influence of the human body has a significant impact on the radio channel characteristics in the communication inside the human body, on the human body, between bodies, and between the human body and an external access point [1]
Deep Feedforward Neural Network (DFNN) method needs only two input parameters for sufficient classification efficiency and can learn the non-linear dependencies between these two parameters to maximize the classification effectiveness
Summary
WBANs (Wireless Body Area Networks) have gained immense popularity due to their use in modern 5G networks, where users expect the quality and speed of data streaming services to be increased while maintaining mobility. The research is focused around the off-body communication, which enables the information transmission outside the human body and is the most commonly used type of communication in the RTLS (Real Time Locating Systems) In such systems, operating in an indoor environment, due to, e.g., high time of flight measurement resolution, UWB (Ultra-Wideband) radio interfaces are widely used. They are characterized by a higher robustness to the multipath propagation effect than narrowband radio interfaces and allow obtaining even centimeter localization accuracy [2,3]. One such radio interface is the well-known DecaWave DWM1000 radio module, which was used in this study [4]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
