Latency prediction in 5G for control with deadtime compensation
(2019) p.51-55- Abstract
- With the promise of increased responsiveness and robustness of the emerging 5G technology, it is suddenly becoming feasible to deploy latency-sensitive control systems over the cloud via a mobile network. Even though 5G is herald to give lower latency and jitter than current mobile networks, the effect of the delay would still be non-negligible for certain applications.
In this paper we explore and demonstrate the possibility of compensating for the unknown and time-varying latency introduced by a 5G mobile network for control of a latency-sensitive plant. We show that the latency from a prototype 5G test bed lacks significant short-term correlation, making accurate latency prediction a difficult task. Further, because of the... (More) - With the promise of increased responsiveness and robustness of the emerging 5G technology, it is suddenly becoming feasible to deploy latency-sensitive control systems over the cloud via a mobile network. Even though 5G is herald to give lower latency and jitter than current mobile networks, the effect of the delay would still be non-negligible for certain applications.
In this paper we explore and demonstrate the possibility of compensating for the unknown and time-varying latency introduced by a 5G mobile network for control of a latency-sensitive plant. We show that the latency from a prototype 5G test bed lacks significant short-term correlation, making accurate latency prediction a difficult task. Further, because of the unknown and time-varying latency our used simple interpolation-based model experiences some troubling theoretical properties, limiting its usability in real world environments. Despite this, we give a demonstration of the strategy which seems to increase robustness in a simulated plant.
(Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/a08df584-b5c6-4ec2-8852-7b7459a60928
- author
- Ruuskanen, Johan LU ; Peng, Haorui LU and Martins, Alexandre LU
- organization
- publishing date
- 2019-04-15
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Latency prediction, 5G, Deadtime compensation, Time varying delay
- host publication
- IoT-Fog '19 Proceedings of the Workshop on Fog Computing and the IoT
- pages
- 4 pages
- publisher
- Association for Computing Machinery (ACM)
- external identifiers
-
- scopus:85066038059
- ISBN
- 978-1-4503-6698-4
- project
- Autonomous camera systems in resource constrained environments
- Event-Based Information Fusion for the Self-Adaptive Cloud
- language
- English
- LU publication?
- yes
- id
- a08df584-b5c6-4ec2-8852-7b7459a60928
- date added to LUP
- 2019-06-11 10:47:44
- date last changed
- 2022-05-03 22:07:40
@inproceedings{a08df584-b5c6-4ec2-8852-7b7459a60928, abstract = {{With the promise of increased responsiveness and robustness of the emerging 5G technology, it is suddenly becoming feasible to deploy latency-sensitive control systems over the cloud via a mobile network. Even though 5G is herald to give lower latency and jitter than current mobile networks, the effect of the delay would still be non-negligible for certain applications.<br/><br/>In this paper we explore and demonstrate the possibility of compensating for the unknown and time-varying latency introduced by a 5G mobile network for control of a latency-sensitive plant. We show that the latency from a prototype 5G test bed lacks significant short-term correlation, making accurate latency prediction a difficult task. Further, because of the unknown and time-varying latency our used simple interpolation-based model experiences some troubling theoretical properties, limiting its usability in real world environments. Despite this, we give a demonstration of the strategy which seems to increase robustness in a simulated plant.<br/>}}, author = {{Ruuskanen, Johan and Peng, Haorui and Martins, Alexandre}}, booktitle = {{IoT-Fog '19 Proceedings of the Workshop on Fog Computing and the IoT}}, isbn = {{978-1-4503-6698-4}}, keywords = {{Latency prediction; 5G; Deadtime compensation; Time varying delay}}, language = {{eng}}, month = {{04}}, pages = {{51--55}}, publisher = {{Association for Computing Machinery (ACM)}}, title = {{Latency prediction in 5G for control with deadtime compensation}}, url = {{https://lup.lub.lu.se/search/files/65822795/fogIoT_ruuskanen.pdf}}, year = {{2019}}, }