University of Oulu

C. -F. Liu and M. Bennis, "Data-Driven Predictive Scheduling in Ultra-Reliable Low-Latency Industrial IoT: A Generative Adversarial Network Approach," 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Atlanta, GA, USA, 2020, pp. 1-5, doi: 10.1109/SPAWC48557.2020.9154307

Data-driven predictive scheduling in ultra-reliable low-latency industrial IoT : a generative adversarial network approach

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Author: Liu, Chen-Feng1; Bennis, Mehdi1
Organizations: 1Centre for Wireless Communications, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.6 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2021-02-18


To date, model-based reliable communication with low latency is of paramount importance for time-critical wireless control systems. In this work, we study the downlink (DL) controller-to-actuator scheduling problem in a wireless industrial network such that the outage probability is minimized. In contrast to the existing literature based on well-known stationary fading channel models, we assume an arbitrary and unknown channel fading model, which is available only via samples. To overcome the issue of limited data samples, we invoke the generative adversarial network framework and propose an online data-driven approach to jointly schedule the DL transmissions and learn the channel distributions in an online manner. Numerical results show that the proposed approach can effectively learn any arbitrary channel distribution and further achieve the optimal performance by using the predicted outage probability.

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Series: IEEE International Workshop on Signal Processing Advances in Wireless Communications
ISSN: 2325-3789
ISSN-L: 2325-3789
ISBN: 978-1-7281-5478-7
ISBN Print: 978-1-7281-5479-4
Article number: 9154307
DOI: 10.1109/SPAWC48557.2020.9154307
Host publication: 21st IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2020
Conference: IEEE International Workshop on Signal Processing Advances in Wireless Communications
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This research was supported by the Academy of Finland project CARMA, the Academy of Finland project MISSION, the Academy of Finland project SMARTER, and the Nokia Bell-Labs project ELLIS.
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