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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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Related Experiment Video

Updated: Jul 23, 2025

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
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Enhanced Performance of Artificial-Neural-Network-Based Equalization for Short-Haul Fiber-Optic Communications.

Mahmoud M T Maghrabi1,2, Hariharan Swaminathan1, Shiva Kumar1

  • 1Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON L8S 4K1, Canada.

Sensors (Basel, Switzerland)
|July 14, 2023
PubMed
Summary

This study introduces an artificial neural network (ANN) equalizer to reduce distortions in short-haul fiber-optic systems. The novel ANN equalizer offers superior performance and lower computational cost compared to traditional methods.

Keywords:
artificial neural network (ANN)digital signal processing (DSP)fiber-optic communicationsintensity modulation and direct detection (IMDD)short reach

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Area of Science:

  • Optical Communications
  • Artificial Intelligence
  • Signal Processing

Background:

  • Short-haul fiber-optic communication systems face signal distortions.
  • Intensity Modulation and Direct Detection (IMDD) systems are widely used but susceptible to impairments.
  • Existing equalization methods like MLSE can be computationally intensive.

Purpose of the Study:

  • To propose an efficient and easy-to-implement artificial neural network (ANN)-based equalizer.
  • To mitigate distortions in short-haul IMDD fiber-optic systems.
  • To improve the compensation performance and reduce computational complexity of equalizers.

Main Methods:

  • Development of a single-layer artificial neural network (ANN) equalizer.
  • Implementation of an advanced training scheme to enhance compensation performance.
  • Testing and validation on 10- and 28-Gbaud short-reach optical-fiber communication systems.

Main Results:

  • The proposed ANN equalizer significantly improved compensation performance.
  • The ANN equalizer demonstrated superior bit error rate (BER) performance.
  • Reduced computational cost and storage memory requirements compared to MLSE.

Conclusions:

  • The ANN-based equalizer is an efficient and robust solution for short-haul fiber-optic systems.
  • The advanced training scheme enhances the ANN equalizer's effectiveness.
  • The proposed equalizer offers a better trade-off between performance and complexity than MLSE.