• Login
    View Item 
    •   Home
    • Research
    • Articles
    • View Item
    •   Home
    • Research
    • Articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguideTheses and Dissertations LibguideSubmit an Item

    Statistics

    Display statistics

    Demonstration of a low-complexity memory-polynomial-aided neural network equalizer for CAP visible-light communication with superluminescent diode

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    OEA_3_8_200009_2020_.pdf
    Size:
    18.57Mb
    Format:
    PDF
    Description:
    Accepted Manuscript
    Download
    Type
    Article
    Authors
    Hu, Fangchen cc
    Holguin Lerma, Jorge Alberto cc
    Mao, Yuan cc
    Zou, Peng
    Shen, Chao cc
    Ng, Tien Khee cc
    Ooi, Boon S. cc
    Chi, Nan cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Photonics Laboratory
    KAUST Grant Number
    BAS/1/1614-01-01
    GEN/1/6607-01-01
    KCR/1/2081-01-01
    OSR-CRG2017-3417
    REP/1/2878-01-01
    Date
    2020-08-24
    Online Publication Date
    2020-08-24
    Print Publication Date
    2020
    Submitted Date
    2020-04-06
    Permanent link to this record
    http://hdl.handle.net/10754/664812
    
    Metadata
    Show full item record
    Abstract
    Visible-light communication (VLC) stands as a promising component of the future communication network by providing high-capacity, low-latency, and high-security wireless communication. Superluminescent diode (SLD) is proposed as a new light emitter in the VLC system due to its properties of droop-free emission, high optical power density, and low speckle-noise. In this paper, we analyze a VLC system based on SLD, demonstrating effective implementation of carrierless amplitude and phase modulation (CAP). We create a low-complexity memory-polynomial-aided neural network (MPANN) to replace the traditional finite impulse response (FIR) post-equalization filters of CAP, leading to significant mitigation of the linear and nonlinear distortion of the VLC channel. The MPANN shows a gain in Q factor of up to 2.7 dB higher than other equalizers, and more than four times lower complexity than a standard deep neural network (DNN), hence, the proposed MPANN opens a pathway for the next generation of robust and efficient neural network equalizers in VLC. We experimentally demonstrate a proof-of-concept 2.95-Gbit/s transmission using MPANN-aided CAP with 16-quadrature amplitude modulation (16-QAM) through a 30-cm channel based on the 442-nm blue SLD emitter.
    Citation
    Hu, F., A. Holguin-Lerma, J., Mao, Y., Zou, P., … Shen, C. (2020). Demonstration of a low-complexity memory-polynomial-aided neural network equalizer for CAP visible-light communication with superluminescent diode. Opto-Electronic Advances, 3(8), 200009–200009. doi:10.29026/oea.2020.200009
    Sponsors
    This work was supported in part by the National Key Research, Development Program of China (2017YFB0403603), and the NSFC project (No. 61925104). JAHL, YM, TKN and BSO gratefully acknowledge the financial support from King Abdullah University of Science and Technology (KAUST) through BAS/1/1614-01-01, REP/1/2878-01-01, GEN/1/6607-01-01, and KCR/1/2081-01-01. This publication is partially supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-CRG2017-3417. JAHL further acknowledge access to the KAUST Nanofabrication Core Lab for the fabrication of devices.
    Publisher
    Opto-Electronic Advances
    Journal
    Opto-Electronic Advances
    DOI
    10.29026/oea.2020.200009
    Additional Links
    http://www.oejournal.org/J/OEA/Article/Details/A200821000003
    ae974a485f413a2113503eed53cd6c53
    10.29026/oea.2020.200009
    Scopus Count
    Collections
    Articles; Electrical and Computer Engineering Program; Photonics Laboratory; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

    entitlement

     
    DSpace software copyright © 2002-2023  DuraSpace
    Quick Guide | Contact Us | KAUST University Library
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.