A New Approach for Eye Diagram Analysis Using Deep Transfer Learning for Identification and Intensity Classification of Rainfall Effect on Signals Transmitted via Free-Space Optical Communication

Free Space Optics (FSO) has emerged as a crucial communication modality in recent years, especially with the implementation of techniques associated with the 5G network and its subsequent advancements. This study proposes the use of Machine Learning, including Convolutional Neural Networks (CNNs) an...

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Bibliographic Details
Published in:	2024 International Joint Conference on Neural Networks (IJCNN) pp. 1 - 8
Main Authors:	Reis, Raiane Rocha, Da Silva, Suane Pires P., Ohata, Elene Firmeza, Portela, Thiago F., de Freitas Guimaraes, Glendo, de Araujo, Aderaldo Irineu Levartoski, Filho, Pedro Pedrosa Reboucas, Rego, Paulo A. L.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 30-06-2024
Subjects:	Computer Vision Deep Transfer Learning Eye Diagram Feature extraction Free Space Optics Free-space optical communication Neural networks Rain Rainfall Support vector machines Transfer learning
Online Access:	Get full text
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Summary:	Free Space Optics (FSO) has emerged as a crucial communication modality in recent years, especially with the implementation of techniques associated with the 5G network and its subsequent advancements. This study proposes the use of Machine Learning, including Convolutional Neural Networks (CNNs) and classifiers such as Random Forest, Naïve Bayes, Multilayer Perceptron, and Support Vector Machine, to analyze and classify the effects of rain in FSO systems. The methodology involves generating a database through simulations in OptiSystem, followed by preprocessing the images to retain only relevant eye diagrams. A CNN is then applied as a feature extractor, with its attributes used as input for the classifiers. After classification, it is possible to discern the type of rain associated with each entry in the database. The results highlight effective combinations, such as VGG16 and VGG19 with the Bayes classifier for distances of 500m and 1 km, and Random Forest with InceptionV3 for 1 km, achieving accuracies above 90% and 99%, respectively. This study offers a practical and effective approach to signal quality analysis in FSO systems, emphasizing the importance of Machine Learning, especially CNNs, in this context. These techniques allow for precise analysis adaptable to weather conditions, providing valuable insights for future enhancements and real-world implementations.
ISSN:	2161-4407
DOI:	10.1109/IJCNN60899.2024.10650240