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Open AccessArticle
Real-Time Signal Quality Assessment and Power Adaptation of FSO Links Operating Under All-Weather Conditions Using Deep Learning Exploiting Eye Diagrams
by
Somia A. Abd El-Mottaleb
Somia A. Abd El-Mottaleb 1
and
Ahmad Atieh
Ahmad Atieh 2,*
1
Department of Mechatronics Engineering, Alexandria Higher Institute of Engineering and Technology, Alexandria 21311, Egypt
2
Optiwave Systems Inc., Ottawa, ON K2E 8A7, Canada
*
Author to whom correspondence should be addressed.
Photonics 2025, 12(8), 789; https://doi.org/10.3390/photonics12080789 (registering DOI)
Submission received: 4 July 2025
/
Revised: 29 July 2025
/
Accepted: 1 August 2025
/
Published: 4 August 2025
Abstract
This paper proposes an intelligent power adaptation framework for Free-Space Optics (FSO) communication systems operating under different weather conditions exploiting a deep learning (DL) analysis of received eye diagram images. The system incorporates two Convolutional Neural Network (CNN) architectures, LeNet and Wide Residual Network (Wide ResNet) algorithms to perform regression tasks that predict received signal quality metrics such as the Quality Factor (Q-factor) and Bit Error Rate (BER) from the received eye diagram. These models are evaluated using Mean Squared Error (MSE) and the coefficient of determination (R2 score) to assess prediction accuracy. Additionally, a custom CNN‑based classifier is trained to determine whether the BER reading from the eye diagram exceeds a critical threshold of 10−4; this classifier achieves an overall accuracy of 99%, correctly detecting 194/195 “acceptable” and 4/5 “unacceptable” instances. Based on the predicted signal quality, the framework activates a dual-amplifier configuration comprising a pre-channel amplifier with a maximum gain of 25 dB and a post-channel amplifier with a maximum gain of 10 dB. The total gain of the amplifiers is adjusted to support the operation of the FSO system under all-weather conditions. The FSO system uses a 15 dBm laser source at 1550 nm. The DL models are tested on both internal and external datasets to validate their generalization capability. The results show that the regression models achieve strong predictive performance, and the classifier reliably detects degraded signal conditions, enabling the real-time gain control of the amplifiers to achieve the quality of transmission. The proposed solution supports robust FSO communication under challenging atmospheric conditions including dry snow, making it suitable for deployment in regions like Northern Europe, Canada, and Northern Japan.
Share and Cite
MDPI and ACS Style
Abd El-Mottaleb, S.A.; Atieh, A.
Real-Time Signal Quality Assessment and Power Adaptation of FSO Links Operating Under All-Weather Conditions Using Deep Learning Exploiting Eye Diagrams. Photonics 2025, 12, 789.
https://doi.org/10.3390/photonics12080789
AMA Style
Abd El-Mottaleb SA, Atieh A.
Real-Time Signal Quality Assessment and Power Adaptation of FSO Links Operating Under All-Weather Conditions Using Deep Learning Exploiting Eye Diagrams. Photonics. 2025; 12(8):789.
https://doi.org/10.3390/photonics12080789
Chicago/Turabian Style
Abd El-Mottaleb, Somia A., and Ahmad Atieh.
2025. "Real-Time Signal Quality Assessment and Power Adaptation of FSO Links Operating Under All-Weather Conditions Using Deep Learning Exploiting Eye Diagrams" Photonics 12, no. 8: 789.
https://doi.org/10.3390/photonics12080789
APA Style
Abd El-Mottaleb, S. A., & Atieh, A.
(2025). Real-Time Signal Quality Assessment and Power Adaptation of FSO Links Operating Under All-Weather Conditions Using Deep Learning Exploiting Eye Diagrams. Photonics, 12(8), 789.
https://doi.org/10.3390/photonics12080789
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