An Analysis of Deep-Neural-Network Model for the Determination of the Bit-Rate of Optical Fiber Signals

An ever-increasing need for additional network capacity is expected to last far into the next century. The bandwidth is essential because of the ever-increasing number of users and the length of time spent online by everyone. Internet use has increased exponentially during the last several years. Be...

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Bibliographic Details
Main Authors: Ghosh, Manthan, Raut, Manisha, Parteki, Rina, Das, Deepa, Thakare, Laxam P, Jichkar, Rucha, Rathore, Sagar Sing, Bawankar, Shyam
Format: Conference Proceeding
Language:English
Subjects:
Bit rate
bit-rate analysis
Computational modeling
Deep learning
fiber-optic cable
Neural networks
Optical computing
Optical fiber cables
Optical fibers
variational mode decomposition (VMD)
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Summary:An ever-increasing need for additional network capacity is expected to last far into the next century. The bandwidth is essential because of the ever-increasing number of users and the length of time spent online by everyone. Internet use has increased exponentially during the last several years. Because of these features, high-capacity optical grid systems have been developed and, unexpectedly, widely used by commercial locations. Here, we used a signal decomposition method and the wavelet-transform to de-noise the signal and calculate the bit rate of the signal using deep neural network architecture. Bitrate in Optical Fiber Communication is the focus of this article. The proposed method provides superior performance and might be a serious candidate for automated bitrate detection over a fiber optic communication connection.
ISSN:2157-0485
DOI:10.1109/ICETET-SIP58143.2023.10151480