Generative artificial intelligence (AI), as an emerging paradigm in content generation, has demonstrated its great potentials in creating high-fidelity data including images, texts, and videos. Nowadays wireless networks and applications have been rapidly evolving from achieving “connected things” to embracing “connected intelligence”. Generative AI has been recognized as a fundamentally innovative technology to drive the advancement of intelligent wireless communications and networks. The convergence of the sixth generation enabled (6G-enabled) edge intelligence and generative AI has emerged as a pivotal research direction in interdisciplinary domains covering electronics, communications, and artificial intelligence. On one hand, with end-users’ explosive growing demands for various generative AI applications, the 6G-empowered edge networks are promising to reduce communication latency of mobile intelligent services and improve the user-experiences. On the other hand, as network services become increasingly complex, conventional optimization and management tools and policies cannot meet the growing demands of 6G networks. Generative AI can provide a creative tool to tailor personalized solutions for intricate resource management and performance optimization for 6G networks, thereby effectively enhancing network performance and improving the resource utilization efficiency.
However, the convergence of generative AI and 6G networks still faces several challenges, necessitating deep exploration and research from the perspective of theories, algorithms, and applications. One of the key challenges involves optimizing and adapting generative AI algorithms over heterogeneous 6G networks. Specifically, modern generative models empowered by large neural networks typically comprise of billions of parameters, leading to a significant challenge for effective deployment of generative models on edge devices with limited computation, communication and memory resources. Moreover, the large-scale distributed training and inference of generative models usually result in considerable energy consumption, which necessitates efficient computing patterns to realize low-carbon and energy-efficient implementations of generative AI services in edge networks. Additionally, it is crucial to explore novel generative AI algorithms to empower the design and optimization of future wireless networks. For instance, leveraging the emergent abilities of large models to improve network performance requires domain-adaptive fine-tuning and knowledge-based transfer learning techniques. Moreover, the convergence of generative AI and 6G-empowered edge networks also raises security and privacy concerns, facing the risk that malicious devices fabricate false data to degrade edge network services. To this end, emerging technologies such as multi-party secure computation and adversarial training can be exploited to achieve dependable, reliable, and explainable endogenous security mechanisms in large-scale zero-trust generative edge networks. Last but not least, the convergence of generative AI and 6G-empowered edge networks unlock a wide range of network services and applications, such as generative AI-based management for vehicular networks and generative model-driven decision-making and optimization for industrial Internet of Things.
Motivated by the above considerations, this special issue focuses on the convergence of 6G-empowered edge intelligence and Generative AI, from the perspective of theories, algorithms, and potential applications. The special issue aims at soliciting the recent research work on the following topics (but not limited to):
● Distributed training/finetuning/inference algorithms of generative AI for 6G-empowered edge intelligence
● Generative AI enabled architectures and protocols for 6G-empowered edge intelligence
● Convergence of generative AI and edge intelligence under 6G heterogeneous network architectures
● Performance evaluation and optimization for the convergence of generative AI and 6G-empowered edge intelligence
● Green and low-carbon technologies for the convergence of generative AI and 6G-empowered edge intelligence
● Resource management and task scheduling for the convergence of generative AI and 6G-empowered edge intelligence
● Security and privacy for the convergence of generative AI and 6G-empowered edge intelligence
● Generative AI enhanced applications and services, e.g., intelligent transportations and vehicular networks, space-air-ground integrated networks, and industrial Internet of Things
● Incentive mechanism and economics for the convergence of generative AI and 6G-empowered edge intelligence
● Standards, prototypes, and applications for the convergence of generative AI and 6G-empowered edge intelligence
Submission Deadline: December 15, 2024
The first round of Notification: February 20, 2025
The first round of Revision: March 25, 2025
Final Decision: April 30, 2025
Final Manuscript: May 25, 2025
Publication Date: July 15, 2025
Yuan Wu, University of Macau, China
Dusit Niyato, Nanyang Technological University, Singapore
Shuguang Cui, The Chinese University of Hong Kong (Shenzhen), China
Lian Zhao, Toronto Metropolitan University, Canada
Tony Q.S. Quek, Singapore University of Technology and Design, Singapore
Yan Zhang, University of Oslo, Norway
Liping Qian, Zhejiang University of Technology, China
Rongpeng Li, Zhejiang University, China
This feature topic of “Convergence of 6G empowered Edge Intelligence and Generative AI: Theories, Algorithms, and Applications” seeks for original, UNPUBLISHED research papers reporting substantive new work in various aspects of topics above. Papers MUST clearly indicate the contributions to the topic field and properly cite related work in this field.
Papers should be submitted in two separate .doc files (preferred) or .pdf files: 1) Main Document (including paper title, abstract, key words, and full text); 2) Title page (including paper title, author affiliation, acknowledgement and any other information related with the authors’ identification) through the Manuscript Central. Please register or login at http://mc03.manuscriptcentral.com/chinacomm, then go to the author center and follow the instructions there. Remember to select “Convergence of 6G-empowered Edge Intelligence and Generative AI: Theories, Algorithms, and Applications –July Issue, 2025” as your manuscript type when submitting; otherwise, it might be considered as a regular paper.
Each submission must be accompanied by the following information:
● an abstract of no more than 150 words
● 3-8 keywords
● original photographs with high-resolution (300 dpi or greater); eps. ortif. format is preferred; sequentially numbered references.
● sequentially numbered references. The basic reference format is: author name, “article name”, issue name (italic), vol., no., page, month, year. for example: Y. M. Huang, “peradventure in wireless heterogeneous…”, IEEE Journal on Selected Areas, vol. 27, no. 5, pp 34-50, May, 2009.
● brief biographies of authors (50-75 words)
● contact information, including email and mailing addresses