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Experimental Evaluation of Computational Complexity for Different Neural Network Equalizers in Optical Communications

arXiv 2109.08711 ↗First posted 17 Sep 2021eess.SP cs.LGcited by 1

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Abstract (arXiv)

Addressing the neural network-based optical channel equalizers, we quantify the trade-off between their performance and complexity by carrying out the comparative analysis of several neural network architectures, presenting the results for TWC and SSMF set-ups.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 2 of its 10 references that are in the map. First posted on arXiv 17 Sep 2021.

Builds on

2 of 10 references.

References · 2

  1. 2019Lower Bounds for Smooth Nonconvex Finite-Sum Optimizationcited by 19
  2. 2021Performance versus Complexity Study of Neural Network Equalizers in Coherent Optical Systemscited by 17

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