Scheduling for Industrial Control Traffic Using Massive MIMO and Large Intelligent Surfaces
(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.
(Less)
- author
- Fitzgerald, Emma LU and Pióro, Michal LU
- organization
- publishing date
- 2023
- 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}}, }