Reliability analysis and optimization of a reconfigurable matching network for communication and sensing antennas in dynamic environments through Gaussian process regression
- Author
- Seppe Van Brandt (UGent) , Kamil Yavuz Kapusuz (UGent) , Joryan Sennesael (UGent) , Sam Lemey (UGent) , Patrick Van Torre (UGent) , Jo Verhaevert (UGent) , Tanja Van Hecke (UGent) and Hendrik Rogier (UGent)
- Organization
- Project
- Abstract
- During the implementation of the Internet of Things (IoT), the performance of communication and sensing antennas that are embedded in smart surfaces or smart devices can be affected by objects in their reactive near field due to detuning and antenna mismatch. Matching networks have been proposed to re-establish impedance matching when antennas become detuned due to environmental factors. In this work, the change in the reflection coefficient at the antenna, due to the presence of objects, is first characterized as a function of the frequency and object distance by applying Gaussian process regression on experimental data. Based on this characterization, for random object positions, it is shown through simulation that a dynamic environment can lower the reliability of a matching network by up to 90%, depending on the type of object, the probability distribution of the object distance, and the required bandwidth. As an alternative to complex and power-consuming real-time adaptive matching, a new, resilient network tuning strategy is proposed that takes into account these random variations. This new approach increases the reliability of the system by 10% to 40% in these dynamic environment scenarios.
- Keywords
- WIRELESS, DESIGN, Internet of Things (IoT), matching network, algorithm, reliability, Gaussian process, regression analysis, statistical analysis
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01HYMV86CZ36HETWPGK61YFJ8Y
- MLA
- Van Brandt, Seppe, et al. “Reliability Analysis and Optimization of a Reconfigurable Matching Network for Communication and Sensing Antennas in Dynamic Environments through Gaussian Process Regression.” SENSORS, vol. 24, no. 9, 2024, doi:10.3390/s24092689.
- APA
- Van Brandt, S., Kapusuz, K. Y., Sennesael, J., Lemey, S., Van Torre, P., Verhaevert, J., … Rogier, H. (2024). Reliability analysis and optimization of a reconfigurable matching network for communication and sensing antennas in dynamic environments through Gaussian process regression. SENSORS, 24(9). https://doi.org/10.3390/s24092689
- Chicago author-date
- Van Brandt, Seppe, Kamil Yavuz Kapusuz, Joryan Sennesael, Sam Lemey, Patrick Van Torre, Jo Verhaevert, Tanja Van Hecke, and Hendrik Rogier. 2024. “Reliability Analysis and Optimization of a Reconfigurable Matching Network for Communication and Sensing Antennas in Dynamic Environments through Gaussian Process Regression.” SENSORS 24 (9). https://doi.org/10.3390/s24092689.
- Chicago author-date (all authors)
- Van Brandt, Seppe, Kamil Yavuz Kapusuz, Joryan Sennesael, Sam Lemey, Patrick Van Torre, Jo Verhaevert, Tanja Van Hecke, and Hendrik Rogier. 2024. “Reliability Analysis and Optimization of a Reconfigurable Matching Network for Communication and Sensing Antennas in Dynamic Environments through Gaussian Process Regression.” SENSORS 24 (9). doi:10.3390/s24092689.
- Vancouver
- 1.Van Brandt S, Kapusuz KY, Sennesael J, Lemey S, Van Torre P, Verhaevert J, et al. Reliability analysis and optimization of a reconfigurable matching network for communication and sensing antennas in dynamic environments through Gaussian process regression. SENSORS. 2024;24(9).
- IEEE
- [1]S. Van Brandt et al., “Reliability analysis and optimization of a reconfigurable matching network for communication and sensing antennas in dynamic environments through Gaussian process regression,” SENSORS, vol. 24, no. 9, 2024.
@article{01HYMV86CZ36HETWPGK61YFJ8Y,
abstract = {{During the implementation of the Internet of Things (IoT), the performance of communication and sensing antennas that are embedded in smart surfaces or smart devices can be affected by objects in their reactive near field due to detuning and antenna mismatch. Matching networks have been proposed to re-establish impedance matching when antennas become detuned due to environmental factors. In this work, the change in the reflection coefficient at the antenna, due to the presence of objects, is first characterized as a function of the frequency and object distance by applying Gaussian process regression on experimental data. Based on this characterization, for random object positions, it is shown through simulation that a dynamic environment can lower the reliability of a matching network by up to 90%, depending on the type of object, the probability distribution of the object distance, and the required bandwidth. As an alternative to complex and power-consuming real-time adaptive matching, a new, resilient network tuning strategy is proposed that takes into account these random variations. This new approach increases the reliability of the system by 10% to 40% in these dynamic environment scenarios.}},
articleno = {{2689}},
author = {{Van Brandt, Seppe and Kapusuz, Kamil Yavuz and Sennesael, Joryan and Lemey, Sam and Van Torre, Patrick and Verhaevert, Jo and Van Hecke, Tanja and Rogier, Hendrik}},
issn = {{1424-8220}},
journal = {{SENSORS}},
keywords = {{WIRELESS,DESIGN,Internet of Things (IoT),matching network,algorithm,reliability,Gaussian process,regression analysis,statistical analysis}},
language = {{eng}},
number = {{9}},
pages = {{21}},
title = {{Reliability analysis and optimization of a reconfigurable matching network for communication and sensing antennas in dynamic environments through Gaussian process regression}},
url = {{http://doi.org/10.3390/s24092689}},
volume = {{24}},
year = {{2024}},
}
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