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Scheduling for Industrial Control Traffic Using Massive MIMO and Large Intelligent Surfaces

Fitzgerald, Emma LU orcid and Pióro, Michal LU (2023) 13th International Workshop on Resilient Networks Design and Modeling, RNDM 2023
Abstract

Industry 4.0, with its focus on flexibility and customizability, is pushing in the direction of wireless communication in future smart factories, in particular massive multiple-input multiple-output (MIMO), and its future evolution Large Intelligent Surfaces (LIS), which provide more reliable channel quality than previous technologies. As such, there arises the need to perform efficient scheduling of industrial control traffic in massive MIMO systems, in a way that meets its highly stringent latency and reliability requirements. In this paper, we provide mixed-integer programming optimization formulations to perform this scheduling, while minimizing the use of radio resources. We give formulations for both fixed and variable schedule... (More)

Industry 4.0, with its focus on flexibility and customizability, is pushing in the direction of wireless communication in future smart factories, in particular massive multiple-input multiple-output (MIMO), and its future evolution Large Intelligent Surfaces (LIS), which provide more reliable channel quality than previous technologies. As such, there arises the need to perform efficient scheduling of industrial control traffic in massive MIMO systems, in a way that meets its highly stringent latency and reliability requirements. In this paper, we provide mixed-integer programming optimization formulations to perform this scheduling, while minimizing the use of radio resources. We give formulations for both fixed and variable schedule frame lengths. We tested our formulations in numerical experiments with varying traffic profiles and numbers of nodes, up to a maximum of 32 nodes. For all problem instances tested, we were able to calculate an optimal schedule within less than 1 s, making our approach feasible for use in real deployment scenarios.

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Please use this url to cite or link to this publication:
author
and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
industrial communications, large intelligent surfaces, LIS, massive MIMO, optimization, scheduling
host publication
Proceedings of 2023 13th International Workshop on Resilient Networks Design and Modeling, RNDM 2023
editor
Fischer, Mathias ; Rak, Jacek and Kassler, Andreas
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
13th International Workshop on Resilient Networks Design and Modeling, RNDM 2023
conference location
Hamburg, Germany
conference dates
2023-09-20 - 2023-09-22
external identifiers
  • scopus:85178293461
ISBN
9798350327359
DOI
10.1109/RNDM59149.2023.10293135
language
English
LU publication?
yes
id
6efa9fcb-b56c-4e9f-aecd-379b01a1c1a8
date added to LUP
2024-01-02 15:40:24
date last changed
2024-01-03 09:39:45
@inproceedings{6efa9fcb-b56c-4e9f-aecd-379b01a1c1a8,
  abstract     = {{<p>Industry 4.0, with its focus on flexibility and customizability, is pushing in the direction of wireless communication in future smart factories, in particular massive multiple-input multiple-output (MIMO), and its future evolution Large Intelligent Surfaces (LIS), which provide more reliable channel quality than previous technologies. As such, there arises the need to perform efficient scheduling of industrial control traffic in massive MIMO systems, in a way that meets its highly stringent latency and reliability requirements. In this paper, we provide mixed-integer programming optimization formulations to perform this scheduling, while minimizing the use of radio resources. We give formulations for both fixed and variable schedule frame lengths. We tested our formulations in numerical experiments with varying traffic profiles and numbers of nodes, up to a maximum of 32 nodes. For all problem instances tested, we were able to calculate an optimal schedule within less than 1 s, making our approach feasible for use in real deployment scenarios.</p>}},
  author       = {{Fitzgerald, Emma and Pióro, Michal}},
  booktitle    = {{Proceedings of 2023 13th International Workshop on Resilient Networks Design and Modeling, RNDM 2023}},
  editor       = {{Fischer, Mathias and Rak, Jacek and Kassler, Andreas}},
  isbn         = {{9798350327359}},
  keywords     = {{industrial communications; large intelligent surfaces; LIS; massive MIMO; optimization; scheduling}},
  language     = {{eng}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Scheduling for Industrial Control Traffic Using Massive MIMO and Large Intelligent Surfaces}},
  url          = {{http://dx.doi.org/10.1109/RNDM59149.2023.10293135}},
  doi          = {{10.1109/RNDM59149.2023.10293135}},
  year         = {{2023}},
}