DACNN-aided nonlinear equalizer for a probabilistic shaping coherent optical communication system
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View abstract on PubMed
Summary
This summary is machine-generated.A new distribution alignment convolutional neural network (DACNN) equalizer improves performance for probabilistic shaping (PS) fiber optic systems. This method reduces training complexity and enhances receiver sensitivity for high-speed optical communication.
Area Of Science
- Optical Communications
- Signal Processing
- Machine Learning
Background
- Probabilistic shaping (PS) is crucial for fiber optic systems to approach the Shannon limit.
- Nonlinear equalizers struggle with the non-uniform distributions inherent in PS systems.
- Existing methods face challenges in effectively equalizing PS signals due to distribution mismatch.
Purpose Of The Study
- To propose a novel nonlinear equalizer for probabilistic shaping optical communication systems.
- To address the ineffectiveness of traditional nonlinear equalizers with non-uniform PS signal distributions.
- To reduce training complexity and enhance equalizer performance simultaneously.
Main Methods
- A distribution alignment convolutional neural network (DACNN) aided nonlinear equalizer was developed.
- The DACNN calibrates the equalizer using the prior distribution of probabilistic shaping.
- This approach aligns the equalizer's training with the signal's non-uniform distribution.
Main Results
- The DACNN equalizer demonstrated nonlinear equalization for 120 Gb/s PS 64QAM signals over 375 km.
- It improved receiver sensitivity by 2.6 dB compared to Volterra equalizers.
- It showed a 1.1 dB improvement over standard convolutional neural network (CNN) equalizers and faster convergence.
Conclusions
- The proposed DACNN equalizer effectively mitigates nonlinear distortion in probabilistic shaping fiber optic systems.
- DACNN offers superior performance and reduced training complexity compared to existing methods.
- This technique holds significant potential for advancing high-capacity optical communication systems.
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