Adaptive Error Correction and Machine Learning for Robust Optical Fibre Communication

Optical fibre communication systems are key for high-speed data transmission but they face challenges such as transmission errors and signal degradation. Hence this study proposes an innovative method that combines adaptive error correction technique with machine learning algorithm to enhance the robustness and reliability of optic fibre communication. Adaptive error correction methods are utilized, specifically low-density parity check codes and Turbo codes which are known for their efficiency in handling high error rates in data transmission. These techniques dynamically adjust their error correction parameters based on the different conditions of optical fibre communication channel. Then, the integration of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) are utilized to detect and correct errors in real time. For training and evaluation, the International Telecommunication Union (ITU-T) are employed. This dataset includes different signal parameters and error scenarios. Experimental evaluation demonstrate that this method eliminates the bit error rate and enhance the signal to noise ratio compared to traditional error correction methods. It also ensures minimal latency, making the method suitable for high-speed optical fibre communication system. This study contributes to the field of optical fibre communication by providing a robust solution that utilizes the strengths of both adaptive error correction and machine learning.