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Compressed Nonlinear Equalizers for 112-Gbps Optical Interconnects: Efficiency and Stability.

Wenjia Zhang1, Ling Ge1, Yanci Zhang1

  • 1State Key Laboratory of Advanced Optical Communication Systems and Networks, Shanghai Jiao Tong University, Shanghai 200240, China.

Sensors (Basel, Switzerland)
|August 23, 2020
PubMed
Summary
This summary is machine-generated.

Neural network-based equalization (NNE) significantly outperforms Volterra series-based equalization (VE) for high-speed optical interconnects, offering superior bit error rate and lower complexity. Pruned VE shows more consistent performance across bias variations.

Keywords:
VCSELVolterra series-based equalizationneural network-based equalization

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Area of Science:

  • Optical communications
  • Signal processing
  • Machine learning for engineering

Background:

  • High-speed short-reach optical interconnects demand low-complexity nonlinear equalization.
  • Vertical cavity surface emitting laser (VCSEL) technology is crucial for these interconnects.
  • Comparing Volterra series-based equalization (VE) and neural network-based equalization (NNE) is essential for optimizing performance.

Discussion:

  • The study investigates the design space and pruning algorithms of nonlinear equalizers.
  • Fundamental reasons for nonlinear compensation capabilities, efficiency, and stability limitations are explored.
  • Performance is evaluated based on complexity, efficiency, and stability.

Key Insights:

  • NNE demonstrates over an order of magnitude better bit error rate (BER) than VE at equivalent computational complexity.
  • Pruned NNE achieves approximately 50% lower computational complexity than VE for the same BER.
  • VE exhibits significant performance instability under challenging channel conditions.

Outlook:

  • Further research into pruned NNE could lead to more robust and efficient optical interconnects.
  • Understanding equalizer limitations is key to advancing high-speed communication systems.
  • Optimizing equalizer design balances performance, complexity, and stability for future optical networks.