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Channel Modeling and Simulation in Wireless Communications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: closed (30 May 2022) | Viewed by 2684

Special Issue Editors


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Guest Editor
Electrical and Electronics Engineering Department, Shamoon College of Engineering, Beer-Sheve 8410802, Israel
Interests: wireless communication; channel modeling; machine learning
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Jerusalem College of Technology, Jerusalem 91160, Israel
Interests: software defined networks; machine learning for wireless networks; Internet protocols; 5G; 6G cellular networks

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Guest Editor
Faculty of Engineering – Department of Electrical and Electronic Engineering, Ariel University, Ariel 40700, Israel
Interests: wireless communications; remote sensing and radars; millimeter and terahertz waves; electromagnetic waves propagation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electromagnetic waves propagate through environments where they are reflected, scattered, and diffracted by walls, buildings, and other objects. Theoretically, the detailed analysis of such a propagation may be performed by a solution of Maxwell’s equations with corresponding boundary conditions that characterize the propagation environment. Nevertheless, these calculations are difficult, and the precise values of the required parameters are often non-available. Practically, different approximations for propagation modeling have been developed for a variety of different frequencies and communication scenarios.

The statistical simulation of communication scenarios is a common practice for an algorithm performance analysis and a further design of communication systems. Such simulations are typically tightly related to a channel model and reflect channel variability over time.

This Special Issue is addressed to all types of channel modeling and simulation for future wireless communications.

Dr. Dima Bykhovsky
Dr. Yoram Haddad
Prof. Dr. Yosef Pinhasi
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • channel modeling
  • wireless communication
  • fading channel
  • propagation modeling
  • channel measurement
  • channel simulator
  • link budget
  • multi-path

Published Papers (1 paper)

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Research

13 pages, 4117 KiB  
Article
Testing a 5G Communication System: Kriging-Aided O2I Path Loss Modeling Based on 3.5 GHz Measurement Analysis
by Melissa Eugenia Diago-Mosquera, Alejandro Aragón-Zavala and Mauricio Rodriguez
Sensors 2021, 21(20), 6716; https://doi.org/10.3390/s21206716 - 9 Oct 2021
Cited by 11 | Viewed by 2011
Abstract
Deep knowledge of how radio waves behave in a practical wireless channel is required for the effective planning and deployment of radio access networks in outdoor-to-indoor (O2I) environments. Using more than 400 non-line-of-sight (NLOS) radio measurements at 3.5 GHz, this study analyzes and [...] Read more.
Deep knowledge of how radio waves behave in a practical wireless channel is required for the effective planning and deployment of radio access networks in outdoor-to-indoor (O2I) environments. Using more than 400 non-line-of-sight (NLOS) radio measurements at 3.5 GHz, this study analyzes and validates a novel O2I measurement-based path loss prediction narrowband model that characterizes and estimates shadowing through Kriging techniques. The prediction results of the developed model are compared with those of the most traditional assumption of slow fading as a random variable: COST231, WINNER+, ITU-R, 3GPP urban microcell O2I models and field measured data. The results showed and guaranteed that the predicted path loss accuracy, expressed in terms of the mean error, standard deviation and root mean square error (RMSE) was significantly better with the proposed model; it considerably decreased the average error for both scenarios under evaluation. Full article
(This article belongs to the Special Issue Channel Modeling and Simulation in Wireless Communications)
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