mlsanityData to 14 Oct 2026

Few-bit Quantization of Neural Networks for Nonlinearity Mitigation in a Fiber Transmission Experiment

arXiv 2205.11284 ↗First posted 23 May 2022eess.SPcited by 1

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Abstract (arXiv)

A neural network is quantized for the mitigation of nonlinear and components distortions in a 16-QAM 9x50km dual-polarization fiber transmission experiment. Post-training additive power-of-two quantization at 6 bits incurs a negligible Q-factor penalty. At 5 bits, the model size is reduced by 85%, with 0.8 dB penalty.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 7 references that are in the map. First posted on arXiv 23 May 2022.

Builds on

6 of 7 references.

References · 6

  1. 2017Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inferencecited by 1,593
  2. 2019HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precisioncited by 402
  3. 2019Low-bit Quantization of Neural Networks for Efficient Inferencecited by 193
  4. 2019Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networkscited by 148
  5. 2021Performance versus Complexity Study of Neural Network Equalizers in Coherent Optical Systemscited by 19
  6. 2020Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensationcited by 7

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Cited by year

Citing papers per year: 2023: 1.

1 paper.

  1. 2023Quantization of Neural Network Equalizers in Optical Fiber Transmission Experimentscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2022QFT: Post-training quantization via fast joint finetuning of all degrees of freedom
  2. 2025Fibbinary-Based Compression and Quantization for Efficient Neural Radio Receivers
  3. 2025GDNSQ: Gradual Differentiable Noise Scale Quantization for Low-bit Neural Networks
  4. 2022Reducing Computational Complexity of Neural Networks in Optical Channel Equalization: From Concepts to Implementation
  5. 2025Quantization Effects of Artificial Neural Networks for Embedded Edge-Computing Applications
  6. 2021A White Paper on Neural Network Quantization
  7. 2022AMED: Automatic Mixed-Precision Quantization for Edge Devices
  8. 2026Nonlinear Bipolar Compensation: Handling Outliers in Post-Training Quantization