Special Issues
Table of Content

AI-Driven Next-Generation Networks: Innovations, Challenges, and Applications

Submission Deadline: 20 February 2026 View: 724 Submit to Special Issue

Guest Editors

Dr. Vu Khanh Quy

Email: quyvk@utehy.edu.vn

Affiliation: Faculty of Information Technology, Hung Yen University of Technology and Education, Hungyen 170000, Vietnam

Homepage:

Research Interests: beyond 5G/6G communications, cloud/fog/edge computing, UAV and internet of vehicles, advanced AI techniques, and federated learning

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Prof. Abdellah Chehri

Email: chehri@rmc.ca

Affiliation: Department of Mathematics and Computer Science, Royal Military College of Canada, Kingston, Ontario, K7K 7B4, Canada

Homepage:

Research Interests: internet of things, beyond 5G/6G, big data, data analytics/AI/ML, ML/federated learning in wireless systems, unmanned aerial vehicle communications, cloud/fog/edge computing and networking, smart cities and public safety technology

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Summary

Recently, the advent of 5G/6G next-generation networks (NGN) and emerging communication technologies such as AI, edge/fog/cloud computing solutions, Robotic, Metaverse, Digital Twins, etc., is shaping the 5.0 Industry revolution (so-called Industry 5.0) to provide unprecedented values and services for humanity in almost domains.

Nevertheless, these solutions and systems face various challenges, including service response time, quality of service (QoS), energy consumption, privacy, and security. The Special Issue (SI) focuses on advancements in AI, high-performance computing, and optimizing routing algorithms for NGNs and/or NGNs-driven advanced solutions.

The topics include but are not limited to
• Combining Advanced AI techniques and NGNs
• Optimizing Routing Algorithms for NGNs
• Sustainable and Green Solutions for NGNs
• Offloading Strategies for NGNs
• NGNs-driven Smart Industrial IoT ecosystems


Keywords

AI, Federated Learning, 5G/6G, Intelligent Transport, Computing, Ad Hoc, Big Data, Smart Healthcare, IoV, Smart Cities

Published Papers


  • Open Access

    ARTICLE

    An Improved Reinforcement Learning-Based 6G UAV Communication for Smart Cities

    Vi Hoai Nam, Chu Thi Minh Hue, Dang Van Anh
    CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-15, 2026, DOI:10.32604/cmc.2025.070605
    (This article belongs to the Special Issue: AI-Driven Next-Generation Networks: Innovations, Challenges, and Applications)
    Abstract Unmanned Aerial Vehicles (UAVs) have become integral components in smart city infrastructures, supporting applications such as emergency response, surveillance, and data collection. However, the high mobility and dynamic topology of Flying Ad Hoc Networks (FANETs) present significant challenges for maintaining reliable, low-latency communication. Conventional geographic routing protocols often struggle in situations where link quality varies and mobility patterns are unpredictable. To overcome these limitations, this paper proposes an improved routing protocol based on reinforcement learning. This new approach integrates Q-learning with mechanisms that are both link-aware and mobility-aware. The proposed method optimizes the selection of… More >

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