University of Oulu

A. M. Girgis, H. Seo, J. Park, M. Bennis and J. Choi, "Split Learning Meets Koopman Theory for Wireless Remote Monitoring and Prediction," 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Helsinki, Finland, 2021, pp. 1191-1196, doi: 10.1109/PIMRC50174.2021.9569357.

Split learning meets Koopman theory for wireless remote monitoring and prediction

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Author: Girgis, Abanoub M.1; Seo, Hyowoon1; Park, Jihong2;
Organizations: 1Centre for Wireless Communications University of Oulu, Oulu 90014, Finland
2School of Information Technology Deakin University, Geelong, VIC 3220, Australia
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.8 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2023-01-16


Remote state monitoring over wireless is envisaged to play a pivotal role in enabling beyond 5G applications ranging from remote drone control to remote surgery. One key challenge is to identify the system dynamics that is non-linear with a large dimensional state. To obviate this issue, in this article we propose to train an autoencoder whose encoder and decoder are split and stored at a state sensor and its remote observer, respectively. This autoencoder not only decreases the remote monitoring payload size by reducing the state representation dimension but also learns the system dynamics by lifting it via a Koopman operator, thereby allowing the observer to locally predict future states after training convergence. Numerical results under a non-linear cart-pole environment demonstrate that the proposed split learning of a Koopman autoencoder can locally predict future states, and the prediction accuracy increases with the representation dimension and transmission power.

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Series: IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications workshops
ISSN: 2166-9570
ISSN-E: 2166-9589
ISSN-L: 2166-9570
ISBN: 978-1-7281-7586-7
ISBN Print: 978-1-7281-7587-4
Pages: 1191 - 1196
DOI: 10.1109/PIMRC50174.2021.9569357
Host publication: 32nd IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2021
Conference: IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
Funding: This research was partly supported by Academy of Finland 6G Flagship (grant no.318927) and project SMARTER, projects EU-ICT IntellIoT and EUCHISTERA LearningEdge, Infotech-NOOR. Additionally, it was partly supported by the Australian Government through the Australian Research Council’s Discovery Projects funding scheme (DP200100391) and Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2014-3-00077, AI National Strategy Project).
EU Grant Number: (957218) IntellIoT - Intelligent, distributed, human-centered and trustworthy IoT environments
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
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