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Andrey Pryamikov¹

¹Prokhorov General Physics Institute of the Russian Academy of Sciences, Moscow, Russia. pryamikov@mail.ru.

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|September 10, 2024

Summary

The backpropagation algorithm, widely used in artificial neural networks, offers efficient digital signal processing for optical systems. This deep learning approach enables cost-effective, low-complexity signal processing designs.

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Area of Science:

Artificial Intelligence
Optical Communications
Signal Processing

Background:

The backpropagation algorithm is a cornerstone of artificial neural network training.
Optical fiber transmission systems require efficient digital signal processing (DSP) schemes.
Current DSP designs may face challenges in complexity and cost-effectiveness.

Purpose of the Study:

To explore the application of the backpropagation algorithm in optical fiber transmission systems.
To investigate the potential of deep learning frameworks for DSP design.
To demonstrate a new paradigm for cost-effective and low-complexity DSP.

Main Methods:

Applying the backpropagation algorithm to develop DSP schemes.
Utilizing deep learning principles within a DSP framework.
Evaluating the efficiency and complexity of the proposed DSP designs.

Main Results:

The backpropagation algorithm proves effective for DSP in optical fiber systems.
Deep learning frameworks offer a novel approach to DSP design.
The developed paradigm achieves high efficiency with low complexity and cost.

Conclusions:

Backpropagation-based deep learning presents a powerful tool for optical DSP.
This approach facilitates the creation of advanced, efficient, and economical optical transmission systems.
The integration of AI into DSP marks a significant advancement in optical communications technology.