Synthetic LiFi channel model using generative adversarial networks

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Purwita, Ardimas Andi and Yesilkaya, Anil and Haas, Harald; (2022) Synthetic LiFi channel model using generative adversarial networks. In: ICC 2022 - IEEE International Conference on Communications. IEEE International Conference on Communications . IEEE, Piscataway, NJ, pp. 577-582. ISBN 9781538683477 (https://doi.org/10.1109/icc45855.2022.9838481)

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Abstract

In this paper, we present our research on modeling a synthetic light fidelity (LiFi) channel model that uses a deep learning architecture called generative adversarial networks (GAN). A research in LiFi that requires the generation of many multipath channel impulse responses (CIRs) can benefit from our proposed model. For example, future developments of autonomous (deep learning-based) network management systems that use LiFi as one of its high-speed wireless access technologies might require a dataset of many CIRs. In this paper, we use TimeGAN, which is a GAN architecture for time-series data. We will show that modifications are necessary to adopt TimeGAN in our use case. Consequently, synthetic CIRs generated by our model can track long-term dependency of LiFi multipath CIRs. The Kullback–Leibler divergence (KLD) is used in this paper to measure the small difference between samples of synthetic CIRs and real CIRs. Lastly, we also show a simple demonstration of our model that can run on a small virtual machine hosted over the Internet.

  • Item type:Book Section
    ID code:82302
    Dates:
    Date
    Event
    11 August 2022
    Published
    16 May 2022
    Accepted
    Notes:© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:12 Sep 2022 10:38
    Last modified:27 Nov 2024 01:31
    URI:https://strathprints.strath.ac.uk/id/eprint/82302
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