Home
Author Guide
Editor Guide
Reviewer Guide
Special Issues
Special Issue Introduction
Special Issues List
Topics
Published Issues
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2010
2009
2008
2007
2006
journal menu
Aims and Scope
Editorial Board
Indexing Service
Article Processing Charge
Open Access Policy
Publication Ethics
Digital Preservation Policy
Editorial Process
Subscription
Contact Us
General Information
ISSN:
1796-2021 (Online); 2374-4367 (Print)
Abbreviated Title:
J. Commun.
Frequency:
Monthly
DOI:
10.12720/jcm
Abstracting/Indexing:
Scopus
;
DBLP
;
CrossRef
,
EBSCO
,
Google Scholar
;
CNKI,
etc.
E-mail questions
or comments to
editor@jocm.us
Acceptance Rate:
27%
APC:
800 USD
Average Days to Accept:
88 days
3.4
2023
CiteScore
51st percentile
Powered by
Article Metrics in Dimensions
Editor-in-Chief
Prof. Maode Ma
College of Engineering, Qatar University, Doha, Qatar
I'm very happy and honored to take on the position of editor-in-chief of JCM, which is a high-quality journal with potential and I'll try my every effort to bring JCM to a next level...
[Read More]
What's New
2024-11-25
Vol. 19, No. 11 has been published online!
2024-10-16
Vol. 19, No. 10 has been published online!
2024-08-20
Vol. 19, No. 8 has been published online!
Home
>
Published Issues
>
2021
>
Volume 16, No. 9, September 2021
>
The Effect of Medium Inhomogeneity in Modeling Underwater Optical Wireless Communication
Safiy Sabril
1
, Faezah Jasman
1
, Wan Hafiza Wan Hassan
2
, Zaiton A. Mutalip
3
, Rosmiwati Mohd-Mokhtar
4
, and Zainuriah Hassan
1
1. Institute of Nano Optoelectronics Research and Technology (INOR), Universiti Sains Malaysia, Penang, Malaysia
2. Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Terengganu, Malaysia
3. Centre for Telecommunication Research & Innovation (CeTRI), Faculty of Electronic & Computer Engineering, Universiti Teknikal Malaysia Melaka, Malaysia
4. School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Penang, Malaysia
Abstract
—This paper introduces a stratified approach to modeling underwater optical wireless communication (UOWC). The influence of medium inhomogeneity, which many researchers ignore, was considered in modeling the UOWC channel to achieve an accurate model. The Monte Carlo technique to simulate the photon propagation was adapted to include medium inhomogeneity to estimate the received power, channel bandwidth, and delay spread of the proposed model. We use the depth-dependent chlorophyll profile that was established in Kameda empirical model to constitute the medium inhomogeneity. The empirical model used 0.5 mg m-3 and 2 mg m-3 of surface chlorophyll concentration to represent clear and coastal water. Besides, the comparison between collimated and diffused links was also studied to highlight the effect of the medium inhomogeneity on both links. Our findings indicate that the homogeneous model produces an underestimation result compared to the stratified model. The stratified model estimated significant increases in received power, lower delay spread, and higher bandwidth, which indicates the medium inhomogeneity is important for a realistic channel model.
Index Terms
—Monte carlo, underwater wireless communication, depth dependent attenuation, channel modeling, chlorophyll concentration
Cite: Safiy Sabril, Faezah Jasman, Wan Hafiza Wan Hassan, Zaiton A. Mutalip, Rosmiwati Mohd-Mokhtar, and Zainuriah Hassan, "The Effect of Medium Inhomogeneity in Modeling Underwater Optical Wireless Communication," Journal of Communications vol. 16, no. 9, pp. 386-393, September 2021. Doi: 10.12720/jcm.16.9.386-393
Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License (
CC BY-NC-ND 4.0
), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.
4-SK1016
PREVIOUS PAPER
A Cost-Effective Two-Way Household Wireless Door Intercom System
NEXT PAPER
Predicting Rectangular Patch Microstrip Antenna Dimension Using Machine Learning