LSTM networks enabled nonlinear equalization in 50-Gb/s PAM-4 transmission links.

Area of Science:

• Optical Communications
• Signal Processing
• Artificial Intelligence

Background:

• Digital signal processing in optical communication systems faces challenges with nonlinear impairments.
• Conventional linear equalizers struggle to effectively mitigate these nonlinear distortions.
• Long short-term memory (LSTM) networks offer advanced capabilities for complex sequence modeling.

Purpose of the Study:

• To propose and experimentally validate a nonlinear equalization technique for optical fiber communication systems using LSTM recurrent neural networks.
• To compare the performance of LSTM-based equalization against conventional linear equalizers and Volterra filtering.
• To assess the effectiveness of LSTM in handling signal impairments in a 50-Gb/s intensity modulation direct detection link.

Main Methods:

• Implementation of a nonlinear equalization technique at the end of offline digital signal processing.
• Two LSTM network approaches: direct signal categorization and signal noise estimation for compensation.
• Experimental testing over a 100-km standard single-mode fiber link using 50-Gb/s four-level pulse amplitude modulation.

Main Results:

• Remarkable performance improvement of the LSTM-based method over conventional linear equalizers.
• Significant enhancement compared to Volterra filtering, especially at high launch power.
• Demonstrated superior short-time universality of the proposed LSTM equalization technique.

Conclusions:

• LSTM recurrent neural networks provide a powerful nonlinear equalization solution for optical communication systems.
• The proposed LSTM technique effectively mitigates signal impairments, outperforming existing methods.
• This approach holds promise for enhancing the performance and reliability of high-speed optical links.