China Communications (ISSN 1673-5447) is an English-language monthly journal cosponsored by the China Institute of Communications (CIC) and IEEE Communications Society (IEEE ComSoc) towards readers in industry, universities, research and development organizations and government agencies in the field of Information and Communications Technologies (ICTs) all over the world.

Aiming at promoting worldwide academic exchange in the ICTs sector, CIC and the IEEE ComSoc are committed to publishing high-quality papers in China Communications to contribute to the global ICTs industry. The publication provides instant access to the latest articles and papers presenting leading-edge research achievement, tutorial overviews, and descriptions of significant practical applications of technology.

China Communications has been indexed in SCIE (Science Citation Index-Expanded) since January, 2007. In addition, all articles have been available in the IEEE Xplore digital library since January, 2013.  

Special Issue on Convergence of 6G-empowered Edge Intelligence and Generative AI: Theories, Algorithms, and Applications
Día de Entrega: 2024-12-15

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

Schedule of the Special Issue

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

Guest Editors

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 

Special Issue on Non-Terrestrial Network: Architecture, Technologies and Applications
Día de Entrega: 2025-03-15

 With the coming of digital era, profound changes are happening in communication field. Within these, non-terrestrial network (NTN) is considered as a leading-edge technology. NTN not only represents an innovation, but also signifies the main development trend of future global communication. As a layered heterogeneous network, NTN will integrate multiple communication platforms, including satellites, high altitude platform systems (HAPS), and unmanned aerial systems (UAS), these provide flexible and composable solutions for achieving ubiquitous global communication coverage.

The layered heterogeneous architecture of NTN is a typical characteristic. Unlike traditional ground networks, NTN's core ideology is to fully utilize aerial communication platforms, that naturally breaks through the constrain of geographical environment. Namely, satellites provide highly reliable long-distance communication services, HAPS offer more flexible selection for communication at stratospheric altitudes, and UAS support highly customized communication services at low altitudes in specific regions. These enable NTN to achieve global coverage. Accordingly, implementing on-demand coverage of NTN architecture has become the primary issue.

In NTN, multiple communication platforms work collaboratively, forming a layered and wide coverage global network. This novel architecture provides a new perspective for global communication development, that can simultaneously offer flexible and customizable communication services for various application scenarios. However, how to design and develop new radio access and network technologies suitable for ground terminals and aerial base stations to address large dynamic spatial mobility, optimize the use of air network sites to reduce transmission and routing delays, remains an open research question.

The research on NTN is crucial for promoting global communication to achieve ubiquitous and high-quality services. By focusing on the architectures, key technologies, and practical applications of NTN, we hope to explore possible methods and solutions for achieving global coverage. This, in turn, will propel innovation in communication technology across various fields, including agriculture, emergency response, intelligent transportation, healthcare, etc. Ultimately, the goal is to construct a more intelligent, reliable, and ubiquitous communication network.

This special issue will focus on exploring the architecture, technologies, and applications of NTN.

Topics include (but not limited to):

● NTN Network Architecture, Standards, Protocols, and Applications
● Seamless Coverage in Three-Dimensional Space
● Cross-Layer Resource Management in NTN
● NTN Integrated Perception and Positioning Optimization
● NTN network communication technology
● NTN Network Multi-Platform Collaborative Resource Management
● NTN Internet integrated sensing and communication
● Computing and cloud-edge-end intelligent collaboration technology over NTN
● Machine learning over NTN
● NTN Network Security Technology
● Novel RF technologies for the NTN, including wideband RF techniques for supporting multiple platforms, and novel architectures for super-massive multiple-beam or phased arrays.  

Schedule

Submission Deadline: 15th March, 2025
Acceptance Notification (1st round): 20th May, 2025
Minor Revision Due: 25th June, 2025
Final Decision Due: 30th July, 2025
Final Manuscript Due: 25th August, 2025
Publication Date: 15th October, 2025

Guest editors 

Yuan Gao, Academy of Military Science of the PLA, China
Gang Wu, University of Electronic Science and Technology of China, China
Zhangcheng Hao, Southeast University, China
Nan Zhao, Dalian University of Technology, China
Dusit Niyato, Nanyang Technological University, Singapore
Arumugam Nallanathan, Queen Mary University of London, UK