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Future Internet
Special Issues
Big Data Analytics and Artificial Intelligence in Next-Generation Wireless Networks
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Big Data Analytics and Artificial Intelligence in Next-Generation Wireless Networks

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 1772

Special Issue Editor

Prof. Dr. Matjaz Perc

grade E-Mail Website
Guest Editor
Faculty of Natural Sciences and Mathematics, University of Maribor, Koroška Cesta 160, 2000 Maribor, Slovenia
Interests: statistical physics; cooperation; complex systems; evolutionary game theory; network science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to technological breakthroughs and advancements, next-generation wireless networks will be unlike today's, diverse and complex, and able to adapt to the changing needs of the consumers. The systematic analysis and mining of big data helps in making such complex systems intelligent, and it facilitates their efficient as well as cost-effective operation and optimization. The objective of this Special Issue is to define the framework of the big data analytics and artificial intelligence in next-generation wireless networks, its services, and breakthrough technologies.

We are soliciting original contributions that have not been published and are not currently under consideration by other journals. Particular emphasis is placed on new concepts and ideas. The topics of interest include, but are not limited to, the following:

  • Machine learning;
  • Artificial intelligence;
  • Deep learning;
  • Reinforcement learning;
  • Cloud computing;
  • Next-generation wireless networks.

Prof. Dr. Matjaz Perc
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 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.

Published Papers (1 paper)

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Research

26 pages, 6498 KiB  
Article
Joint Random Forest and Particle Swarm Optimization for Predictive Pathloss Modeling of Wireless Signals from Cellular Networks
by Okiemute Roberts Omasheye, Samuel Azi, Joseph Isabona, Agbotiname Lucky Imoize, Chun-Ta Li and Cheng-Chi Lee
Future Internet 2022, 14(12), 373; https://doi.org/10.3390/fi14120373 - 12 Dec 2022
Cited by 4 | Viewed by 1153
Abstract
The accurate and reliable predictive estimation of signal attenuation loss is of prime importance in radio resource management. During wireless network design and planning, a reliable path loss model is required for optimal predictive estimation of the received signal strength, coverage, quality, and [...] Read more.
The accurate and reliable predictive estimation of signal attenuation loss is of prime importance in radio resource management. During wireless network design and planning, a reliable path loss model is required for optimal predictive estimation of the received signal strength, coverage, quality, and signal interference-to-noise ratio. A set of trees (100) on the target measured data was employed to determine the most informative and important subset of features, which were in turn employed as input data to the Particle Swarm (PS) model for predictive path loss analysis. The proposed Random Forest (RF-PS) based model exhibited optimal precision performance in the real-time prognostic analysis of measured path loss over operational 4G LTE networks in Nigeria. The relative performance of the proposed RF-PS model was compared to the standard PS and hybrid radial basis function-particle swarm optimization (RBF-PS) algorithm for benchmarking. Generally, results indicate that the proposed RF-PS model gave better prediction accuracy than the standard PS and RBF-PS models across the investigated environments. The projected hybrid model would find useful applications in path loss modeling in related wireless propagation environments. Full article
(This article belongs to the Special Issue Big Data Analytics and Artificial Intelligence in Next-Generation Wireless Networks)
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