University of Oulu

A. M. Girgis, H. Seo, J. Park, M. Bennis and J. Choi, "Predictive Closed-Loop Remote Control Over Wireless Two-Way Split Koopman Autoencoder," in IEEE Internet of Things Journal, vol. 9, no. 23, pp. 23285-23301, 1 Dec.1, 2022, doi: 10.1109/JIOT.2022.3206415

Predictive closed-loop remote control over wireless two-way split Koopman autoencoder

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Author: Girgis, Abanoub M.1; Seo, Hyowoon2; Park, Jihong3;
Organizations: 1Centre for Wireless Communications, University of Oulu, Oulu, Finland
2Department of Electronics and Communications Engineering, Kwangwoon University, Seoul, South Korea
3School of Information Technology, Deakin University, Geelong, VIC, Australia
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.7 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe202301051555
Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2023-01-05
Description:

Abstract

Real-time remote control over wireless is an important yet challenging application in fifth-generation and beyond due to its mission-critical nature under limited communication resources. Current solutions hinge on not only utilizing ultrareliable and low-latency communication (URLLC) links but also predicting future states, which may consume enormous communication resources and struggle with a short prediction time horizon. To fill this void, in this article we propose a novel two-way Koopman autoencoder (AE) approach wherein: 1) a sensing Koopman AE learns to understand the temporal state dynamics and predicts missing packets from a sensor to its remote controller and 2) a controlling Koopman AE learns to understand the temporal action dynamics and predicts missing packets from the controller to an actuator co-located with the sensor. Specifically, each Koopman AE aims to learn the Koopman operator in the hidden layers while the encoder of the AE aims to project the nonlinear dynamics onto a lifted subspace, which is reverted into the original nonlinear dynamics by the decoder of the AE. The Koopman operator describes the linearized temporal dynamics, enabling long-term future prediction and coping with missing packets and closed-form optimal control in the lifted subspace. Simulation results corroborate that the proposed approach achieves a 38X lower mean squared control error at 0-dBm signal-to-noise ratio (SNR) than the nonpredictive baseline.

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Series: IEEE internet of things journal
ISSN: 2372-2541
ISSN-E: 2327-4662
ISSN-L: 2327-4662
Volume: 9
Issue: 23
Pages: 23285 - 23301
DOI: 10.1109/jiot.2022.3206415
OADOI: https://oadoi.org/10.1109/jiot.2022.3206415
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
6G
Funding: This work was supported in part by the Academy of Finland 6G Flagship under Grant 318927 and Project SMARTER; in part by Project EU-ICT IntellIoT and EU-CHISTERA LeadingEdge; in part by Project CONNECT, Infotech-NOOR, and NEGEIN; and in part by the National Research Foundation of Korea (NRF) Grant funded by the Korea Government (MSIT) under Grant 2022R1F1A1075078.
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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