Data transmission enhancement using optimal coding technique over in vivo channel for interbody communication

Mezher, M. A., Din, S., Ilyas, M., Bayat, O., Abbasi, Q. H. and Ashraf, I. (2022) Data transmission enhancement using optimal coding technique over in vivo channel for interbody communication. Big Data, (doi: 10.1089/big.2021.0224) (Early Online Publication)

Abstract

Wireless in vivo actuators and sensors are examples of sophisticated technologies. Another breakthrough is the use of in vivo wireless medical devices, which provide scalable and cost-effective solutions for wearable device integration. In vivo wireless body area networks devices reduce surgery invasiveness and provide continuous health monitoring. Also, patient data may be collected over a long period of time. Given the large fading in in vivo channels due to the signal path going through flesh, bones, skins, and blood, channel coding is considered a solution for increasing the efficiency and overcoming inter-symbol interference in wireless communications. Simulations are performed by using 50 MHz bandwidth at Ultra-Wideband frequencies (3.10–10.60 GHz). Optimal channel coding (Turbo codes, Convolutional codes, with the help of polar codes) improves data transmission performance over the in vivo channel in this research. Moreover, the results reveal that turbo codes outperform polar and convolutional codes in terms of bit error rate. Other approaches perform similarly when the information block length is increased. The simulation in this work indicates that the in vivo channel shows less performance than the Rayleigh channel due to the dense structure of the human body (flesh, skins, blood, bones, muscles, and fat).

• Item Type: Articles
• Status: Early Online Publication
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Abbasi, Professor Qammer
• Authors: Mezher, M. A., Din, S., Ilyas, M., Bayat, O., Abbasi, Q. H., and Ashraf, I.
• College/School: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Journal Name: Big Data
• Publisher: Mary Ann Liebert Inc.
• ISSN: 2167-6461
• ISSN (Online): 2167-647X
• Published Online: 04 April 2022
• Copyright Holders: Copyright © 2022 Mary Ann Liebert, Inc.
• First Published: First published in Big Data 2022
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher