Practical Demonstration of 5G NR Transport Over-Fiber System with Convolutional Neural Network

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Abstract

This study describes an experimental realization using digital predistortion (DPD) for a fifth generation (5G) multiband new radio (NR) optical front haul (OFH) based Radio over Fiber (RoF) link. For the performance enhancement and complexity reduction of RoF links, a novel Convolutional Neural Network (CNN) based DPD technique is proposed, followed by comparisons with the generalised memory polynomial (GMP) based DPD method. To support enhanced mobile broad band scenario, the experimental testbed uses the 5G NR waveforms at 10 GHz with 20 MHz bandwidth and a flexible-waveform signal at 3 GHz with 20 MHz bandwidth. For 10 km of typical single mode fiber, a Mach Zehnder Modulator with two distinct radio frequency waveforms modulates a 1310 nm optical carrier utilizing distributed feedback laser. The error vector magnitude and number of estimated coefficients, and multiplications are all used to describe the experimental outcomes. The goal of the research is to see if CNN-based DPD improves performance while lowering complexity levels to meet 3GPP Release 17 criteria.
Original languageEnglish
Pages (from-to)103-117
Number of pages15
JournalTelecom
Volume3
Issue number1
Early online date2 Feb 2022
DOIs
Publication statusE-pub ahead of print - 2 Feb 2022

Keywords

  • digital predistortion
  • convolutional neural network
  • radio over fiber
  • error vector magnitude

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