Deep Learning-aided TR-UWB MIMO System

Zia, Muhammad Umer, Xiang, Wei, Huang, Tao, and Ijaz Haider, Naqvi (2022) Deep Learning-aided TR-UWB MIMO System. IEEE Transactions on Communications, 70 (10). pp. 6579-6588.

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Abstract

This paper presents a novel deep learning-aided scheme dubbed PRρ-net for improving the bit error rate (BER) of the Time Reversal (TR) Ultra-Wideband (UWB) Multiple Input Multiple Output (MIMO) system with imperfect Channel State Information (CSI). The designed system employs Frequency Division Duplexing (FDD) with explicit feedback in a scenario where the CSI is subject to estimation and quantization errors. Imperfect CSI causes a drastic increase in BER of the FDD-based TR-UWB MIMO system, and we tackle this problem by proposing a novel neural network-aided design for the conventional precoder at the transmitter and equalizer at the receiver. A closed-form expression for the initial estimation of the channel correlation is derived by utilizing transmitted data in time-varying channel conditions modeled as a Markov process. Subsequently, a neural network-aided design is proposed to improve the initial estimate of channel correlation. An adaptive pilot transmission strategy for a more efficient data transmission is proposed that uses channel correlation information. The theoretical analysis of the model under the Gaussian assumptions is presented, and the results agree with the Monte-Carlo simulations. The simulation results indicate high performance gains when the suggested neural networks are used to combat the effect of channel imperfections.

Item ID: 75813
Item Type: Article (Research - C1)
ISSN: 1558-0857
Copyright Information: Published Version: © 2022 IEEE. Accepted Version may be made open access in an INstitutional Repository wihout embargo.
Date Deposited: 12 Sep 2022 00:53
FoR Codes: 40 ENGINEERING > 4006 Communications engineering > 400608 Wireless communication systems and technologies (incl. microwave and millimetrewave) @ 60%
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning @ 40%
SEO Codes: 22 INFORMATION AND COMMUNICATION SERVICES > 2201 Communication technologies, systems and services > 220107 Wireless technologies, networks and services @ 80%
22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 20%
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