Neural Network DPD for Aggrandizing SM-VCSEL-SSMF-Based Radio over Fiber Link Performance

Muhammad Usman Hadi, Muhammad Awais, Mohsin Raza, Kiran Khurshid, Hyun Jung

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
4 Downloads (Pure)

Abstract

This paper demonstrates an unprecedented novel neural network (NN)-based digital predistortion (DPD) solution to overcome the signal impairments and nonlinearities in Analog Optical fronthauls using radio over fiber (RoF) systems. DPD is realized with Volterra-based procedures that utilize indirect learning architecture (ILA) and direct learning architecture (DLA) that becomes quite complex. The proposed method using NNs evades issues associated with ILA and utilizes an NN to first model the RoF link and then trains an NN-based predistorter by backpropagating through the RoF NN model. Furthermore, the experimental evaluation is carried out for Long Term Evolution 20 MHz 256 quadraturre amplitude modulation (QAM) modulation signal using an 850 nm Single Mode VCSEL and Standard Single Mode Fiber to establish a comparison between the NN-based RoF link and Volterra-based Memory Polynomial and Generalized Memory Polynomial using ILA. The efficacy of the DPD is examined by reporting the Adjacent Channel Power Ratio and Error Vector Magnitude. The experimental findings imply that NN-DPD convincingly learns the RoF nonlinearities which may not suit a Volterra-based model, and hence may offer a favorable trade-off in terms of computational overhead and DPD performance.

Original languageEnglish
Article number19
Pages (from-to)1-15
Number of pages15
JournalPhotonics
Volume8
Issue number1
DOIs
Publication statusPublished - 14 Jan 2021

Keywords

  • Adjacent channel power ratio
  • Digital predistortion
  • Error vector magnitude
  • Neural network
  • Radio over fiber

Fingerprint

Dive into the research topics of 'Neural Network DPD for Aggrandizing SM-VCSEL-SSMF-Based Radio over Fiber Link Performance'. Together they form a unique fingerprint.

Cite this