Reservoir computing-based digital signal equalizer for equivalent-time sampling.

Area of Science:

• Electrical Engineering
• Optical Communications
• Signal Processing

Background:

• Sampling oscilloscopes are crucial for optical signal quality assessment.
• Equivalent-time sampling in oscilloscopes yields non-continuous data for random digital signals.
• Traditional equalization methods like filtering and averaging are ineffective on such data.

Purpose of the Study:

• To propose a novel signal equalization method for sampling oscilloscopes using reservoir computing.
• To address the inability of sampling oscilloscopes to equalize random digital signals.
• To enhance the performance of optical communication signal analysis.

Main Methods:

• Development of a signal equalization technique based on reservoir computing.
• Training a reservoir model to create an equivalent-time equalizer.
• Comparison of the proposed method with continuous-time equalizers.

Main Results:

• The reservoir computing-based equalizer achieves over 95% coincidence compared to continuous-time equalizers.
• Significant improvements in signal quality metrics: eye height increased by 7, eye width by 1.6.
• A substantial reduction in eye diagram jitter by 2.3 times.

Conclusions:

• Reservoir computing effectively equalizes random digital signals from sampling oscilloscopes.
• The proposed method overcomes the inherent limitations of equivalent-time sampling for signal equalization.
• This advancement enhances the capability of sampling oscilloscopes in optical communication analysis.