Special Issue on Agentic AI for 6G Networks

Introduction:

6G networks are poised to provide full coverage across air, land, and sea, deliver terabit-per-second data rates, and achieve microsecond-level latency. They promise comprehensive upgrades across industries through embedded intelligence, ushering in an era of intelligent interconnection of all things. However, managing real-time interactions among devices, infrastructure, and services in 6G networks is much more complex than in previous generations. Massive data streams from terrestrial nodes (e.g., edge devices, sensors, distributed computing) and non-terrestrial nodes (LEO/MEO/GEO satellites) demand more intelligent and efficient processing.

Traditional AI-driven network optimization techniques and current generative AI techniques often rely on predefined rules or centralized control, making them less adaptable to real-world dynamic changes and ill-suited for the demands of 6G. In response, Agentic AI has emerged—an AI paradigm capable of autonomous perception, decision-making, and task execution. Unlike passive content generation, Agentic AI enables a shift toward fully autonomous, self-learning, and self-optimizing networks by actively interacting with the environment, learning from data, and making real-time, energy-efficient decisions—fulfilling the intelligent promise of 6G.

By leveraging distributed intelligence, multi-agent learning, and other intelligent related algorithms, Agentic AI can optimize 6G networks and enhance the intelligence. For instance, Agentic AI can play a vital role in sustainable 6G network operation by coordinating edge computing, wireless access, and core network functionalities, ensuring optimal service deployment, resource allocation, latency minimization, and real-time processing. Agentic AI can also prolong the lifespan of resource-constrained devices by adjusting workloads, optimizing routing paths, and managing computing demands adaptively.

This special issue seeks to accelerate fundamental research on Agentic AI for 6G network optimization, intelligent radio-environment interaction, and self-evolving network management systems. We invite original contributions addressing challenges in Agentic AI empowered 6G network resource allocation design, energy-efficient semantic communication frameworks, and trustworthy autonomous network operation for 6G ecosystems.

We invite high-quality papers within the scope of Agentic AI empowered 6G networks, with subjects covering the entire range from theories to applications. 

Topics covered include, but are not limited to:

• Agentic AI for 6G network wireless/wired resource configuration;
• Agentic AI for 6G dynamic spectrum management;
• Agentic AI for 6G network management;
• Agentic AI for 6G mobile edge computing;
• Agentic AI for 6G mobile fog computing;
• Agentic AI for 6G cloud-edge-end collaborative computing;
• Agentic AI for 6G native security;
• Agentic AI for 6G physical layer security;
• Agentic AI for 6G network softwarization, virtualization, and slicing;
• Agentic AI for 6G all-perception and computing integration;
• Agentic AI for 6G network resource orchestration;
• Agentic AI for 6G dynamic spectrum ecosystems;
• Agentic AI for 6G cloud-edge-end cognitive collaboration;
• Agentic AI for 6G testbeds and practical deployments 

Important dates:

• Submission deadline: January 1st, 2026
• First notification: March 1st, 2026
• Revised version deadline: April 15st, 2026
• Final notification: June 15st, 2026
• Publication date: August 2026

Submission instructions: 

Please read the Guide for Authors before submitting. All articles should be submitted online via the editorial management system; please select article type AAI 6G. 

Guest editors:

• Haotong Cao (lead Guest Editor), Nanjing University of Posts and Telecommunications, China. Email: haotong.cao@njupt.edu.cn
• Weiwei Jiang, Beijing University of Posts and Telecommunications, China. Email: jww@bupt.edu.cn
• Shahid Mumtaz, Nottingham Trent University, UK. Email: shahid.mumtaz@ntu.ac.uk
• Mohsen Guizani, Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE. Email: mguizani@ieee.org