University of Oulu

A. M. Girgis, J. Park, M. Bennis and M. Debbah, "Predictive Control and Communication Co-Design via Two-Way Gaussian Process Regression and AoI-Aware Scheduling," in IEEE Transactions on Communications, vol. 69, no. 10, pp. 7077-7093, Oct. 2021, doi: 10.1109/TCOMM.2021.3099156

Predictive control and communication co-design via two-way Gaussian process regression and AoI-aware scheduling

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Author: Girgis, Abanoub M.1; Park, Jihong2; Bennis, Mehdi1;
Organizations: 1Centre for Wireless Communications, University of Oulu, Oulu, Finland
2School of Information Technology, Deakin University, Geelong, VIC, Australia
3CentraleSupélec, Université Paris-Saclay, Gif-sur-Yvette, France
4Technology Innovation Institute, Abu Dhabi, United Arab Emirates
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.4 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2021122162715
Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2021-12-21
Description:

Abstract

This article studies the joint problem of uplink-downlink scheduling and power allocation for controlling a large number of control systems that upload their states to remote controllers and download control actions over wireless links. To overcome the lack of wireless resources, we propose a machine learning-based solution, where only one control system is controlled, while the rest of the control systems are actuated by locally predicting the missing state and/or action information using the previous uplink and/or downlink receptions via a Gaussian process regression (GPR). This GPR prediction credibility is determined using the age-of-information (AoI) of the latest reception. Moreover, the successful reception is affected by the transmission power, mandating a co-design of the communication and control operations. To this end, we formulate a network-wide minimization problem of the average AoI and transmission power under communication reliability and control stability constraints. To solve the problem, we propose a dynamic control algorithm using the Lyapunov drift-plus-penalty optimization framework. Numerical results corroborate that the proposed algorithm can stably control 2× more number of actuators compared to an event-triggered scheduling baseline with Kalman filtering and frequency division multiple access, which is 18× larger than a round-robin scheduling baseline.

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Series: IEEE transactions on communications
ISSN: 0090-6778
ISSN-E: 1558-0857
ISSN-L: 0090-6778
Volume: 69
Issue: 10
Pages: 7077 - 7093
DOI: 10.1109/TCOMM.2021.3099156
OADOI: https://oadoi.org/10.1109/TCOMM.2021.3099156
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
6G
Funding: This work is supported by Academy of Finland 6G Flagship (grant no. 318927) and project SMARTER, projects EU-ICT IntellIoT and EUCHISTERA LearningEdge, and CONNECT, Infotech-NOOR, and NEGEIN.
Copyright information: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
  https://creativecommons.org/licenses/by/4.0/