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Low Complexity Convolutional Neural Networks for Equalization in Optical Fiber Transmission

Signal Processing 2022-10-12 v1 Artificial Intelligence
Authors: Mohannad Abu-romoh , Nelson Costa , Antonio Napoli , João Pedro , Yves Jaouën , Mansoor Yousefi
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Abstract

A convolutional neural network is proposed to mitigate fiber transmission effects, achieving a five-fold reduction in trainable parameters compared to alternative equalizers, and 3.5 dB improvement in MSE compared to DBP with comparable complexity.

Keywords

neural networks for signal processing deep learning for wireless communications wireless communication

Cite

@article{arxiv.2210.05454,
  title  = {Low Complexity Convolutional Neural Networks for Equalization in Optical Fiber Transmission},
  author = {Mohannad Abu-romoh and Nelson Costa and Antonio Napoli and João Pedro and Yves Jaouën and Mansoor Yousefi},
  journal= {arXiv preprint arXiv:2210.05454},
  year   = {2022}
}

Comments

2 pages, 3 figures. Submitted to the OSA Advanced Photonics Congress 2021. Presented in Signal Processing in Photonic Communications (SPPCom) 2021. From the session: Neural Networks Applications for Photonic Systems (SpM5C)

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