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Special Issue "Application of Artificial Intelligence in the New Era of Communication Networks"

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 4985

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Special Issue Editors

Dr. Teodor B Iliev

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Guest Editor

Department of Telecommunications, University of Ruse, 7017 Ruse, Bulgaria
Interests: digital communications; communication theory; signal processing; channel modeling; artificial intelligence; wireless communications; mobile networks; GNSS
Special Issues, Collections and Topics in MDPI journals

Dr. Lorant Andras Szolga

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Guest Editor

Basis of Electronics, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
Interests: VLSI technology; internet of things; spice simulation; electronics; semiconductor engineering; microelectronics

Dr. Gani Balbayev

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Guest Editor

Department of Information Security, Eurasian National University, Nursultan 010000, Kazakhstan
Interests: robotics and mechatronics; artificial intelligence; machine learning; transmissions

Prof. Dr. Francisco Falcone

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Guest Editor

1. Department of Electrical, Electronic and Communication Engineering & Institute for Smart Cities (ISC), Public University of Navarre, 31006 Pamplona, Spain
2. Tecnologico de Monterrey, School of Engineering and Sciences, Monterrey 64849, Mexico
Interests: wireless networks; performance evaluation; distributed systems; context-aware environments; IoT; next-generation wireless systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Applications of machine learning in wireless and mobile communications networks have been receiving increasing attention, especially in the new era of big data and IoT, where data mining and data analysis technologies are effective approaches to solving wireless system issues. Artificial intelligence is one of the leading technologies in 5G, beyond 5G, and future 6G networks. Intelligence is endowing the tendency to throw open the capabilities of the 5G networks and the future 6G mobile wireless networks by leveraging the universal infrastructure, open network architectures, software-defined networking, network function virtualization, multi-access edge computing, vehicular network, etc. The implementation of the blockchain and mobile edge computing have become a significant part of the new wireless and mobile communication networks and will help the calculations to be performed as close to the IoT devices as possible.

The main aim of this Special Issue is to provide an overview of the current research on wireless and mobile communication technologies, based on contributions from machine learning, mobile edge computing, blockchain, and other fields of artificial intelligence, including channel modelling, signal estimation and detection, energy efficiency, vehicular communications, and wireless multimedia communications. The topics of interest include, but are not limited to:

Wireless and wireline communications;
Beyond 5G & 6G access and core networks;
Blockchain services and applications;
Artificial intelligence and intelligent systems;
Big data analysis;
Cloud technologies and applications;
Machine learning;
Internet of Everything;
Autonomous driving and V2X solutions;
Next-generation networks;
Holographic Communication;
Cyber Security;
e-Health.

Dr. Teodor B Iliev
Dr. Lorant Andras Szolga
Dr. Gani Balbayev
Prof. Dr. Francisco Falcone
Guest Editors

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. Electronics is an international peer-reviewed open access semimonthly 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 2200 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

artificial intelligence
wireless networks
5G and beyond
6G mobile networks
radio communications
network function virtualization
data analisys
edge computing
mmWaves
software-defined networkings
extended (XR) and augmented reality (AR)

Published Papers (4 papers)

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Research

Open AccessArticle

Towards Implementation of Emotional Intelligence in Human–Machine Collaborative Systems

and

Electronics 2023, 12(18), 3852; https://doi.org/10.3390/electronics12183852 - 12 Sep 2023

Viewed by 259

Abstract

Social awareness and relationship management components can be seen as a form of emotional intelligence. In the present work, we propose task-related adaptation on the machine side that accounts for a person’s momentous cognitive and emotional state. We validate the practical significance of the proposed approach in person-specific and person-independent setups. The analysis of results in the person-specific setup shows that the individual optimal performance curves for that person, according to the Yerkes–Dodson law, are displaced. Awareness of these curves allows for automated recognition of specific user profiles, real-time monitoring of the momentous condition, and activating a particular relationship management strategy. This is especially important when a deviation is detected caused by a change in the person’s state of mind under the influence of known or unknown factors. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in the New Era of Communication Networks)

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Open AccessArticle

Improving the Performance of ALOHA with Internet of Things Using Reinforcement Learning

Sami Acik

Selahattin Kosunalp

Mehmet Baris Tabakcioglu

and

Teodor Iliev

Electronics 2023, 12(17), 3550; https://doi.org/10.3390/electronics12173550 - 22 Aug 2023

Viewed by 284

Abstract

Intelligent medium access control (MAC) protocols have been a vital solution in enhancing the performance of a variety of wireless networks. ALOHA, as the first MAC approach, inspired the development of several MAC schemes in the network domain, with the primary advantage of simplicity. In this article, we present design, implementation, and performance evaluations of the ALOHA approach, through significant improvements in attaining high channel utilization as the most important performance metric. A critical emphasis is currently focused on removing the burden of packet collisions, while satisfying requirements of energy and delay criteria. We first implement the ALOHA protocol to practically explore its performance behaviors in comparison to analytical models. We then introduce the concept of dynamic payload instead of fixed-length packets, whereby a dynamic selection of the length of each transmitted packet is employed. Another specific contribution of this paper is the integration of the transmission policy of ALOHA with the potential of Internet of Things (IoT) opportunities. The proposed policy utilizes a state-less Q-learning strategy to achieve the maximum performance efficiency. Performance outputs prove that the proposed idea ensures a maximum throughput of approximately 58%, while ALOHA is limited to nearly 18% over a single-hop scenario. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in the New Era of Communication Networks)

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Open AccessArticle

An Efficient Classification of Rice Variety with Quantized Neural Networks

and

Electronics 2023, 12(10), 2285; https://doi.org/10.3390/electronics12102285 - 18 May 2023

Viewed by 657

Abstract

Rice, as one of the significant grain products across the world, features a wide range of varieties in terms of usability and efficiency. It may be known with various varieties and regional names depending on the specific locations. To specify a particular rice type, different features are considered, such as shape and color. This study uses an available dataset in Turkey consisting of five different varieties: Ipsala, Arborio, Basmati, Jasmine, and Karacadag. The dataset introduces 75,000 grain images in total; each of the 5 varieties has 15,000 samples with a 256 × 256-pixel dimension. The main contribution of this paper is to create Quantized Neural Network (QNN) models to efficiently classify rice varieties with the purpose of reducing resource usage on edge devices. It is well-known that QNN is a successful method for alleviating high computational costs and power requirements in response to many Deep Learning (DL) algorithms. These advantages of the quantization process have the potential to provide an efficient environment for artificial intelligence applications on microcontroller-driven edge devices. For this purpose, we created eight different QNN networks using the MLP and Lenet-5-based deep learning models with varying quantization levels to be trained by the dataset. With the Lenet-5-based QNN network created at the W3A3 quantization level, a 99.87% classification accuracy level was achieved with only 23.1 Kb memory size used for the parameters. In addition to this tremendous benefit of memory usage, the number of billion transactions per second (GOPs) is 23 times less than similar classification studies. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in the New Era of Communication Networks)

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Open AccessArticle

ML-Based Traffic Classification in an SDN-Enabled Cloud Environment

and

Electronics 2023, 12(2), 269; https://doi.org/10.3390/electronics12020269 - 05 Jan 2023

Viewed by 1522

Abstract

Traffic classification plays an essential role in network security and management; therefore, studying traffic in emerging technologies can be useful in many ways. It can lead to troubleshooting problems, prioritizing specific traffic to provide better performance, detecting anomalies at an early stage, etc. In this work, we aim to propose an efficient machine learning method for traffic classification in an SDN/cloud platform. Traffic classification in SDN allows the management of flows by taking the application’s requirements into consideration, which leads to improved QoS. After our tests were implemented in a cloud/SDN environment, the method that we proposed showed that the supervised algorithms used (Naive Bayes, SVM (SMO), Random Forest, C4.5 (J48)) gave promising results of up to 97% when using the studied features and over 95% when using the generated features. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in the New Era of Communication Networks)

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Special Issue "Application of Artificial Intelligence in the New Era of Communication Networks"

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