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Related Experiment Video

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An Improved End-to-End Autoencoder Based on Reinforcement Learning by Using Decision Tree for Optical Transceivers.

Qianwu Zhang1, Zicong Wang1, Shuaihang Duan1

  • 1Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai University, Shanghai 200444, China.

Micromachines
|January 21, 2022
PubMed
Summary

This study introduces a novel autoencoder using Decision Trees and reinforcement learning for optical transceivers, achieving 48 Gb/s over 65 km standard single mode fiber. Optimal parameters for bit error rate performance were identified, reducing computational complexity.

Keywords:
Adaboost algorithmdeep learningmachine learningneural networksoptical fiber communication

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Area of Science:

  • Optical Communications
  • Machine Learning in Telecommunications
  • Signal Processing

Background:

  • Autoencoders are increasingly used in optical communication systems for signal processing.
  • Reinforcement learning offers potential for optimizing complex system parameters.
  • Decision Trees, particularly with algorithms like Adaboost, can enhance neural network performance.

Purpose of the Study:

  • To propose and experimentally validate an improved end-to-end autoencoder for optical transceivers.
  • To leverage reinforcement learning and Decision Trees for enhanced performance.
  • To analyze the impact of Decision Tree parameters on bit error rate (BER).

Main Methods:

  • Developed an asymmetrical autoencoder combining a deep neural network with the Adaboost algorithm.
  • Employed reinforcement learning for optimizing the autoencoder's parameters.
  • Conducted experiments to evaluate performance over standard single mode fiber (SSMF) and analyzed key parameters like Tree depth and number of Decision Trees.

Main Results:

  • Achieved a data rate of 48 Gb/s with a 7% hard-decision forward error correction (HD-FEC) threshold over 65 km SSMF.
  • Identified optimal parameters: 30 Decision Trees for stable BER performance between 25-75 km SSMF, and a Tree depth of 5 for optimal BER.
  • Reduced computational complexity during training compared to Fully-Connected Neural Network autoencoders.

Conclusions:

  • The proposed autoencoder, enhanced by reinforcement learning and Decision Trees, significantly improves optical transceiver performance.
  • The study provides optimal parameter guidelines for maximizing BER performance in specific fiber optic link configurations.
  • This approach offers a computationally efficient alternative to traditional autoencoder architectures in optical communications.