Tools
Tools
Hadi, Muhammad Usman, Awais, Muhammad 
ORCID: https://orcid.org/0000-0001-6421-9245, Raza, Mohsin, Khurshid, Kiran and Jung, Hyun
(2021)
Neural network DPD for aggrandizing SM-VCSEL-SSMF-based radio over fiber link performance.
Photonics, 8 (1).
ISSN 2304-6732
Preview |
PDF (photonics-08-00019)
- Published Version
Available under License Creative Commons Attribution. Download (3MB) | Preview |
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | adjacent channel power ratio,digital predistortion,error vector magnitude,neural network,radio over fiber,atomic and molecular physics, and optics,instrumentation,radiology nuclear medicine and imaging ,/dk/atira/pure/subjectarea/asjc/3100/3107 |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 17 Oct 2023 00:45 |
| Last Modified: | 24 Oct 2023 01:43 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/93317 |
| DOI: | 10.3390/photonics8010019 |
Downloads
Downloads per month over past year
Actions (login required)
 |
View Item |