Machine learning-assisted design and control for period-one microwave photonic sensing signal
Microwave photonic (MWP) sensing and measurement are envisioned to be a promising alternative to the conventional pure electronic or optical solutions. A semiconductor laser (SL) with external optical feedback (EOF) operating in a period-one (P1) dynamic state contributes a new implementation archit...
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| Vydáno v: | Optics and laser technology Ročník 180; s. 111449 |
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| Jazyk: | angličtina |
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01.01.2025
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| ISSN: | 0030-3992 |
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| Abstract | Microwave photonic (MWP) sensing and measurement are envisioned to be a promising alternative to the conventional pure electronic or optical solutions. A semiconductor laser (SL) with external optical feedback (EOF) operating in a period-one (P1) dynamic state contributes a new implementation architecture for MWP systems. However, designing such a SL system to generate frequency-modulated MWP sensing signals through traditional Lang–Kobayashi (L–K) equations requires extensive computational effort to derive the system control parameters (SCP), making real-time adjustment of the SCP impossible in cases where it is needed. This paper proposes an effective design approach based on machine learning. A feedforward neural network (FNN), in conjunction with a gradient descent algorithm, is employed to fast and accurately ascertain the SCP, offering a solution readily applicable in the system design. Both simulation and experiment are conducted to validate the proposed approach.
•Proposes a machine learning approach to optimize control for period-one state.•Solves computational bottlenecks of Lang–Kobayashi (L–K) equation method.•Uses a neural network with gradient descent to determine control parameters quickly.•Experimental validation confirmed the performance of the proposed method. |
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| AbstractList | Microwave photonic (MWP) sensing and measurement are envisioned to be a promising alternative to the conventional pure electronic or optical solutions. A semiconductor laser (SL) with external optical feedback (EOF) operating in a period-one (P1) dynamic state contributes a new implementation architecture for MWP systems. However, designing such a SL system to generate frequency-modulated MWP sensing signals through traditional Lang–Kobayashi (L–K) equations requires extensive computational effort to derive the system control parameters (SCP), making real-time adjustment of the SCP impossible in cases where it is needed. This paper proposes an effective design approach based on machine learning. A feedforward neural network (FNN), in conjunction with a gradient descent algorithm, is employed to fast and accurately ascertain the SCP, offering a solution readily applicable in the system design. Both simulation and experiment are conducted to validate the proposed approach.
•Proposes a machine learning approach to optimize control for period-one state.•Solves computational bottlenecks of Lang–Kobayashi (L–K) equation method.•Uses a neural network with gradient descent to determine control parameters quickly.•Experimental validation confirmed the performance of the proposed method. |
| ArticleNumber | 111449 |
| Author | Guo, Qinghua Fang, Can Yu, Yanguang Ruan, Yuxi |
| Author_xml | – sequence: 1 givenname: Can orcidid: 0000-0001-7621-3940 surname: Fang fullname: Fang, Can email: cf861@uowmail.edu.au – sequence: 2 givenname: Yuxi orcidid: 0000-0003-3905-7162 surname: Ruan fullname: Ruan, Yuxi email: yruan@uow.edu.au – sequence: 3 givenname: Qinghua orcidid: 0000-0002-5180-7854 surname: Guo fullname: Guo, Qinghua email: qguo@uow.edu.au – sequence: 4 givenname: Yanguang orcidid: 0000-0002-1541-9061 surname: Yu fullname: Yu, Yanguang email: yanguang@uow.edu.au |
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| Cites_doi | 10.1109/JSTQE.2011.2121055 10.1109/JLT.2022.3201776 10.1364/OE.23.002777 10.1364/OE.24.004239 10.1002/lpor.202100549 10.1364/OE.25.025523 10.1364/AO.54.007820 10.1109/JSEN.2019.2935087 10.1364/OL.44.004869 10.1364/OE.396180 10.1364/OL.41.005764 10.1364/OL.44.001662 10.1109/JQE.1980.1070479 10.1364/OE.481505 10.1364/OE.23.001470 10.1109/JQE.2012.2185487 10.1364/JOSAB.389890 10.1038/nature04275 10.1109/3.119502 10.1016/j.optlastec.2024.110963 10.1109/JQE.2008.2001929 10.1109/JLT.2022.3176318 10.1364/OE.22.018648 10.1364/AOP.7.000570 10.1109/JLT.2020.2993320 10.1364/OE.24.002655 10.1038/nphoton.2014.326 10.3390/app12178635 |
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| Keywords | Self-mixing effect Optical feedback effect Semiconductor laser with optical feedback Microwave photonics Machine learning Laser dynamics |
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| SubjectTerms | Laser dynamics Machine learning Microwave photonics Optical feedback effect Self-mixing effect Semiconductor laser with optical feedback |
| Title | Machine learning-assisted design and control for period-one microwave photonic sensing signal |
| URI | https://dx.doi.org/10.1016/j.optlastec.2024.111449 |
| Volume | 180 |
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