Special Issues
Table of Content

Artificial Intelligence for 6G Wireless Networks

Submission Deadline: 01 July 2026 View: 2599 Submit to Special Issue

Guest Editors

Prof. Dr. Cheng-Chi Lee

Email: cclee@mail.fju.edu.tw

Affiliation: Department of Library and Information Science, Fu Jen Catholic University, New Taipei City 24205, Taiwan

Homepage:

Research Interests: Artificial Intelligence, wireless networks, network security, mobile communications, wireless computing, wireless communications

图片3.png


Dr. Agbotiname Lucky Imoize

Email: aimoize@unilag.edu.ng

Affiliation: Department of Electrical and Electronics Engineering, University of Lagos, Akoka, Lagos 100213, Nigeria

Homepage:

Research Interests: wireless communications, wireless security systems, Artificial Intelligence

图片4.png


Dr. Mohsin Murtaza

Email: mohsin.murtaza@rmit.edu.au

Affiliation: School of Engineering, Science Technology Engineering and Mathematics College, RMIT University, Melbourne, VIC 3001, Australia

Homepage:

Research Interests: Artificial Intelligence, wireless networks, cyber security, and autonomous vehicles

图片5.png


Summary

Artificial Intelligence (AI) is a key enabler of the envisioned 6G wireless communication systems. AI can be deployed to address critical challenges, such as interference, latency, and path loss, in 6G wireless networks. When designing wireless systems, it is crucial to account for the imperfections of the wireless channel. In information-theoretic systems, noise and fading can be leveraged to conceal sensitive user data from potential eavesdroppers without requiring an additional secret key. However, designing such schemes can be cost-prohibitive and computationally expensive. Hence, there is a need for AI-based solutions in the design, implementation, and management of wireless networks.

This special issue focuses on new AI-based applications in the general framework of 6G. Specifically, the special issue aims to provide a forum for collecting the latest research findings and high-quality works on the novel applications of artificial intelligence in 6G wireless networks.

The topics are but not limited to the following:

· AI for energy efficiency optimization in 6G networks
· AI for high data rates in 6G wireless communications
· AI for advanced signal processing in 6G wireless networks.
· AI for optimal hardware design of 6G wireless systems.
· AI for training and testing wireless channel models in 6G
· AI-aided reconfigurable intelligent surfaces for 6G
· AI for cooperative communications in 6G networks.
· AI aiding device-to-device communications in 6G.
· AI for enhancing the security and privacy of 6G networks.
· AI for achieving quality of physical experience in 6G
· AI enabling performance evaluation of 6G wireless systems
· AI for interference management in 6G wireless systems
· AI for dynamic spectrum allocation and management in 6G
· AI empowering massive MIMO systems design for 6G
· AI for formulating regulatory policies and standards for 6G.
· AI for green and sustainable communications in 6G
· AI enabling high-performance audio-visual systems in 6G
· AI for autonomous vehicle communications in 6G.


Keywords

Artificial Intelligence, wireless communications, energy efficiency, spectral efficiency, network optimization, channel estimation, Reconfigurable Intelligent Surfaces, data rates, ultra-low latency, reliability, Wireless Security Systems, Mobile Computing

Published Papers


  • Open Access

    REVIEW

    A Comprehensive Survey on AI-Assisted Multiple Access Enablers for 6G and beyond Wireless Networks

    Kinzah Noor, Agbotiname Lucky Imoize, Michael Adedosu Adelabu, Cheng-Chi Lee
    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1575-1664, 2025, DOI:10.32604/cmes.2025.073200
    (This article belongs to the Special Issue: Artificial Intelligence for 6G Wireless Networks)
    Abstract The envisioned 6G wireless networks demand advanced Multiple Access (MA) schemes capable of supporting ultra-low latency, massive connectivity, high spectral efficiency, and energy efficiency (EE), especially as the current 5G networks have not achieved the promised 5G goals, including the projected 2000 times EE improvement over the legacy 4G Long Term Evolution (LTE) networks. This paper provides a comprehensive survey of Artificial Intelligence (AI)-enabled MA techniques, emphasizing their roles in Spectrum Sensing (SS), Dynamic Resource Allocation (DRA), user scheduling, interference mitigation, and protocol adaptation. In particular, we systematically analyze the progression of traditional and modern… More >

    Graphic Abstract

    A Comprehensive Survey on AI-Assisted Multiple Access Enablers for 6G and beyond Wireless Networks

  • Open Access

    ARTICLE

    Cross-Site Map-Free Indoor Localization for 6G ISAC Systems Using Low-Frequency Radio and Transformer Networks

    Bin Zhang, En-Cheng Liou, Yi-Chih Tung, Muhammad Usman, Chiung-An Chen, Chao-Shun Yang
    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2551-2571, 2025, DOI:10.32604/cmes.2025.072471
    (This article belongs to the Special Issue: Artificial Intelligence for 6G Wireless Networks)
    Abstract Indoor localization is a fundamental requirement for future 6G Intelligent Sensing and Communication (ISAC) systems, enabling precise navigation in environments where Global Positioning System (GPS) signals are unavailable. Existing methods, such as map-based navigation or site-specific fingerprinting, often require intensive data collection and lack generalization capability across different buildings, thereby limiting scalability. This study proposes a cross-site, map-free indoor localization framework that uses low-frequency sub-1 GHz radio signals and a Transformer-based neural network for robust positioning without prior environmental knowledge. The Transformer’s self-attention mechanisms allow it to capture spatial correlations among anchor nodes, facilitating accurate… More >

  • Open Access

    ARTICLE

    System Modeling and Deep Learning-Based Security Analysis of Uplink NOMA Relay Networks with IRS and Fountain Codes

    Phu Tran Tin, Minh-Sang Van Nguyen, Quy-Anh Bui, Agbotiname Lucky Imoize, Byung-Seo Kim
    CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2521-2543, 2025, DOI:10.32604/cmes.2025.066669
    (This article belongs to the Special Issue: Artificial Intelligence for 6G Wireless Networks)
    Abstract Digital content such as games, extended reality (XR), and movies has been widely and easily distributed over wireless networks. As a result, unauthorized access, copyright infringement by third parties or eavesdroppers, and cyberattacks over these networks have become pressing concerns. Therefore, protecting copyrighted content and preventing illegal distribution in wireless communications has garnered significant attention. The Intelligent Reflecting Surface (IRS) is regarded as a promising technology for future wireless and mobile networks due to its ability to reconfigure the radio propagation environment. This study investigates the security performance of an uplink Non-Orthogonal Multiple Access (NOMA)… More >

Share Link