Topic: AI-Driven Intelligent and Autonomous Networking for Complex 5G/6G Systems

The transition from fifth-generation (5G) to sixth-generation (6G) wireless networks is reshaping communication infrastructures into highly heterogeneous, interconnected, and increasingly intelligent systems. Emerging applications—including connected and autonomous vehicles, industrial automation, immersive services, smart cities, and large-scale Internet of Things (IoT) ecosystems—require communication networks capable of meeting demanding requirements for adaptability, reliability, scalability, and energy efficiency.

Artificial intelligence (AI) and machine learning (ML) offer powerful mechanisms for moving beyond predominantly reactive network management toward predictive, adaptive, and autonomous operation. Techniques such as reinforcement learning, federated learning, and multi-agent intelligence, combined with software-defined networking, network virtualization, and edge computing, can enable selfconfiguration, self-optimization, self-healing, and closed-loop resource orchestration across distributed network environments.

However, realizing AI-driven autonomy in complex 5G/6G systems raises substantial challenges related to real-time decision-making, interoperability, data and computational constraints, trustworthy and explainable AI, cybersecurity, resilience, and sustainable operation. Addressing these challenges requires rigorous methods that connect intelligent algorithms with networking architectures, control and optimization, and reproducible experimental evaluation.

This Special Issue invites original research and high-quality review articles on the theoretical foundations, system architectures, algorithms, and practical validation of intelligent and autonomous 5G/6G networking. It particularly welcomes contributions that demonstrate measurable advances in network performance, robustness, resource efficiency, and trustworthy autonomous operation, including applications in complex cyber-physical environments.

Topics include, but are not limited to:

● AI-native 5G/6G architectures and autonomous, closed-loop network management;

● Self-organizing, self-optimizing, self-healing, and intent-based networking;

● Deep reinforcement learning, multi-agent learning, and intelligent network optimization;

● AI-driven traffic engineering, routing, congestion control, and radio resource management;

● Edge AI, federated learning, and distributed or collaborative network intelligence;

● Intelligent network slicing, SDN/NFV, and edge–cloud service orchestration;

● Trustworthy and explainable AI, cybersecurity, fault tolerance, and network resilience;

● Energy-aware resource management and sustainable 5G/6G infrastructures;

● Digital twins, predictive network management, simulation, emulation, and testbeds;

● Intelligent networking for IoT, cyber-physical systems, V2X, and UAV-assisted communications.