Demodulating Optical Wireless Communication of FBG Sensing with Turbulence-Caused Noise by Stacked Denoising Autoencoders and the Deep Belief Network

Free-space optics communication (FSO) can be used as a transmission medium for fiber optic sensing signals to make fiber optic sensing easier to implement; however, interference with the sensing signals caused by the optical turbulence and scattering of airborne particles in the FSO path is a potent...

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Vydané v:Electronics (Basel) Ročník 13; číslo 20; s. 4127
Hlavní autori: Bogale, Shegaw Demessie, Yao, Cheng-Kai, Manie, Yibeltal Chanie, Dehnaw, Amare Mulatie, Tefera, Minyechil Alehegn, Li, Wei-Long, Zhong, Zi-Gui, Peng, Peng-Chun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.10.2024
Predmet:
Airborne sensing
Atmospheric turbulence
Belief networks
Bragg gratings
Computational linguistics
Deep learning
Equipment and supplies
Fiber optics
Free space optics
Language processing
Machine learning
Natural language interfaces
Neural networks
Noise prediction
Noise reduction
Outdoor air quality
Sensors
Wavelengths
Wavelet transforms
Weather
Wireless communications
ISSN:2079-9292, 2079-9292
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Popis
Shrnutí:Free-space optics communication (FSO) can be used as a transmission medium for fiber optic sensing signals to make fiber optic sensing easier to implement; however, interference with the sensing signals caused by the optical turbulence and scattering of airborne particles in the FSO path is a potential problem. This work aims to deep denoise sensed signals from fiber Bragg grating (FBG) sensors based on FSO link transmission using advanced denoising deep learning techniques, such as stacked denoising autoencoders (SDAE). Furthermore, it will demodulate the sensed wavelength of FBGs by applying the deep belief network (DBN) technique. This is the first time the real FBG sensing experiment has utilized the actual noise interference caused by the environmental turbulence from an FSO link rather than adding noise through numerical processing. Consequently, the spectrum of the FBG sensors is clearly modulated by the noise and the issue with peak power variation. This complicates the determination of the center wavelengths of multiple stacked FBG spectra, requiring the use of machine learning techniques to predict these wavelengths. The results indicate that SDAE is efficient in denoising from the FBG spectrum, and DBN is effective in demodulating the central wavelength of the overlapped FBG spectrum. Thus, it is beneficial to implement an FSO link-based FBG sensing system in adverse weather conditions or atmospheric turbulence.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13204127