Deep Learning Based Cooperative MIMO Systems for Wireless Body Area Networks

DOI: https://doi.org/10.21553/rev-jec.352

Abstract

Wireless Body Area Network (WBAN) is widely applied in various fields, including healthcare, sports, wellness, and assistive technologies, by offering the benefits of convenience, reliability, low latency, privacy, and customization. However, the propagation characteristics of the WBAN channel can impact the reliability of transmission, which is particularly crucial in healthcare systems. To address this issue, this article presents a novel approach using deep learning-based cooperative Multiple-Input Multiple-Output (MIMO) systems that leverage the autoencoder (AE) technique. In our proposed approach, we utilize the AE-based cooperative MIMO systems with two different techniques: Amplify-and-Forward (AE-AF) and Decode-and-Forward (AE-DF). The AE-AF scheme operates without needing training parameters at the relay node, whereas the AE-DF scheme necessitates training parameters at the relay node. Both schemes aim to overcome challenges such as multipath propagation phenomena, thereby enhancing the performance of on-body communication systems. Additionally, we introduce two combinators, Minimum Mean Square Error (MMSE) scheme and Radio Transformation Network (RTN), to effectively mitigate co-channel interference (CCI) in the received signal streams and improve the bit error rate performance of the AE-AF and AE-DF systems. We assess the performance of these systems in scenarios with and without direct links. Simulation results demonstrate significant performance improvements compared to baseline cooperative MIMO systems using MMSE combining, namely AF-MMSE and DF-MMSE systems. Notably, the proposed systems employing RTN combination, including both direct and relay paths, achieve a 7.5 dB gain over the baseline when all nodes are equipped with two transceiver antennas.

Author Biographies

Tam Thi Thanh Bui, Academy of Military Science and Technology
I am currently working toward the PhD. degree at the Electronic Institute at Academy of Military Science and Technology. I obtained my bachelor degree in radio-electronics from Le Quy Don Technical University, Vietnam in 2009. I received my master of engineering (ME) in electronic engineering from Le Quy Don Technical University in 2016. My research interests are in the area of signal processing, biomedical engineering MIMO systems and deep learning.
Nam Xuan Tran, Le Quy Don Technical University
is currently a professor at the Department of Communications Engineering at Le Quy Don Technical  University Vietnam. He received his master of engineering (ME) in telecommunications engineering from the University of Technology Sydney, Australia in 1998, and doctor of engineering in electronic engineering from The University ofElectro-Communications, Japan in 2003. From November 2003 to March 2006 he was a research associate at the Information and Communication Systems Group, Department of Information and Communication Engineering, The University of Electro-ommunications, Tokyo, Japan. Dr. Tran’s research interests are in the areas of adaptive antennas, spacetime processing, space-time coding and MIMO systems. Dr. Tran is a recipient of the 2003 IEEE AP-S Japan Chapter Young Engineer Award. He is a member of IEEE, IEICE, and the Radio-Electronics Association of Vietnam.
Anh Huy Phan, Academy of Military Science and Technology
Huy Anh Phan received the Bachelor’s degree in physics from Hanoi University of Science, Hanoi, Vietnam, in 2003, received the M.Eng. degree in telecommunications from the University of Melbourne, Melbourne, Australia, in 2007, and received the Ph.D. degree in Electrical from the School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, Australia, in 2014. Now, he is a reseacher at Academy of Military Science and Technology, Ha Noi, Viet Nam. His research interests are in signal processing for communications, currently on optimization problems in cognitive radio, wireless relay networks, MIMO detection and UAV.
Published
2024-03-30
Issue
Vol. 14, No. 1 (Jan-Mar, 2024)
Section
Regular articles
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