Application of Artificial Intelligence in the New Era of Communication Networks, 2nd Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: 15 November 2025 | Viewed by 328

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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
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Guest Editor
Department of Power Engineering, University of Ruse, 7004 Ruse, Bulgaria
Interests: renewable energy sources; electromagnetic compatibility; electrotechnical safety; smart grid; electric power transmission
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Guest Editor
Department of Robotics and Technical Means of Automation, Satbayev University, Almaty 050013, Kazakhstan
Interests: automation and control; electronics; robotics; rehabilitation devices; mechanical engineering

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Guest Editor
Department of Electronics and Robotics, Almaty University of Power Engineering and Telecommunications Named After G. Daukeev, Almaty 050013, Kazakhstan
Interests: electronic and computational systems; robotics; renewable energy

Special Issue Information

Dear Colleagues,

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

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

  • Wireless and wireline communications;
  • Beyond 5G and 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. Ivaylo Stoyanov
Prof. Dr. Kassymbek Ozhikenov
Dr. Alina Fazylova
Guest Editors

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Keywords

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

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Published Papers (1 paper)

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Research

16 pages, 2313 KiB  
Article
False Information Mitigation Using Pattern-Based Anomaly Detection for Secure Vehicular Networks
by Abinash Borah and Anirudh Paranjothi
Electronics 2025, 14(9), 1848; https://doi.org/10.3390/electronics14091848 - 1 May 2025
Viewed by 220
Abstract
Vehicular networks utilize wireless communication among vehicles and between vehicles and infrastructures. While vehicular networks offer a wide range of benefits, the security of these networks is critical for ensuring public safety. The transmission of false information by malicious nodes (vehicles) for selfish [...] Read more.
Vehicular networks utilize wireless communication among vehicles and between vehicles and infrastructures. While vehicular networks offer a wide range of benefits, the security of these networks is critical for ensuring public safety. The transmission of false information by malicious nodes (vehicles) for selfish gain is a security issue in vehicular networks. Mitigating false information is essential to reduce the potential risks posed to public safety. Existing methods for false information detection in vehicular networks utilize various approaches, including machine learning, blockchain, trust scores, and statistical techniques. These methods often rely on past information about vehicles, historical data for training machine learning models, or coordination between vehicles without considering the trustworthiness of the vehicles. To address these limitations, we propose a technique for False Information Mitigation using Pattern-based Anomaly Detection (FIM-PAD). The novelty of FIM-PAD lies in using an unsupervised learning approach to learn the usual patterns between the direction of travel and speed of vehicles, considering the variations in vehicles’ speeds in different directions. FIM-PAD uses only real-time network characteristics to detect the malicious vehicles that do not conform to the identified usual patterns. The objective of FIM-PAD is to accurately detect false information in vehicular networks with minimal processing delays. Our performance evaluations in networks with high proportions of malicious nodes confirm that FIM-PAD on average offers a 38% lower data processing delay and at least 19% lower false positive rate compared to three other existing techniques. Full article
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