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Real-Time Machine Learning Methods for Two-Way End-to-End Wireless Communication Systems

Authors
Baek, SeunghwanMoon, JihwanPark, JunheeSong, ChangickLee, Inkyu
Issue Date
15-11월-2022
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Training; Receivers; Transmitters; Wireless communication; Generators; Internet of Things; Real-time systems; Autoencoder (AE); end-to-end design; machine learning (ML)
Citation
IEEE INTERNET OF THINGS JOURNAL, v.9, no.22, pp.22983 - 22992
Indexed
SCIE
SCOPUS
Journal Title
IEEE INTERNET OF THINGS JOURNAL
Volume
9
Number
22
Start Page
22983
End Page
22992
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/146495
DOI
10.1109/JIOT.2022.3186811
ISSN
2327-4662
Abstract
In this article, we study a data-driven real-time machine learning method for end-to-end wireless systems for the Internet of Things (IoT). For a two-way communication link between two IoT devices, we propose an efficient learning algorithm that can train the autoencoder-based transmitter and receiver in each device without needing to know the channel between two devices. To this end, we adopt the conditional generative adversarial network (cGAN) that can learn an output distribution of the channel for a given conditioning signal. Our proposed training method consists of the link update stage and the self-update stage. In the link update stage, two devices transmit the training data and update their own receiver and the cGAN simultaneously. Subsequently, in the self-update stage, the two devices train their transmitters at the same time for the given receivers and cGANs. Our proposed real-time training method is updated without the knowledge of the channel models nor information feedback for training. Finally, we demonstrate that the proposed training method achieves significant performance gains over conventional schemes in various practical communication scenarios.
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공과대학 (전기전자공학부)
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