Predictive Closed-Loop Remote Control over Wireless Two-Way Split Koopman Autoencoder

Real-time remote control over wireless is an important-yet-challenging application in 5G and beyond due to its mission-critical nature under limited communication resources. Current solutions hinge on not only utilizing ultra-reliable and low-latency communication (URLLC) links but also predicting f...

Full description

Saved in:
Bibliographic Details
Published in:IEEE internet of things journal 2022-12, Vol.9 (23), p.1-1
Main Authors: Girgis, Abanoub M., Seo, Hyowoon, Park, Jihong, Bennis, Mehdi, Choi, Jinho
Format: Article
Language:English
Subjects:
Actuators
autoencoder
beyond 5G
Coders
Communication
Control systems
Controllers
Dynamic scheduling
Koopman theory
Network latency
Nonlinear dynamics
Optimal control
Predictive control
Remote control
Remote sensors
Sensors
Signal to noise ratio
split learning
Ultra reliable low latency communication
Wireless communication
Wireless communications
Wireless sensor networks
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Real-time remote control over wireless is an important-yet-challenging application in 5G and beyond due to its mission-critical nature under limited communication resources. Current solutions hinge on not only utilizing ultra-reliable 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 non-linear dynamics onto a lifted subspace, which is reverted into the original non-linear 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 non-predictive baseline.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2022.3206415