Special Issue on 6G in the Era of Large Models: Design, Applications and Beyond
Published 19 March, 2024
The sixth-generation (6G) wireless network features artificial intelligence (AI) and is envisioned to provide ubiquitous AI service from core infrastructure to end-user devices. AI is not confined to over-the-top applications but also plays an indispensable role in the development of 6G, such as the design of architectures, protocols and operations, as well as hardware optimization. Various AI tools, such as end-to-end deep neural network (DNN), reinforcement learning (RL), and federated learning (FL), stand out as solutions to address the ever-increasing scalability, complexity and heterogeneity of 6G, making the network smart, agile, and adaptive.
Recently, AI has entered a new era of Large Models (LMs). With billions or trillions of parameters trained on vast amounts of corpus, LMs have achieved unprecedented successes in many challenging applications, such as math solving and text and image generation. Compared with traditional AI, LMs have exhibited remarkable intelligence and generalization, which will no doubt propel 6G to another level.
The significance of LMs for 6G is profound, as those LMs are driving a revolution in how we design, optimize and apply 6G technologies. On one hand, LMs with emerging abilities provide advanced optimization tools that can adapt to the changing network dynamics, and empower innovative applications for 6G. On the other, 6G and LMs need to be co-designed for seamlessly integrating communication, computation and intelligence, and as a result, it is of great interest to investigate the integration of 6G and LMs.
In light of the potential and challenges, this special issue focuses on 6G design, applications, and beyond in the era of LMs. It aims to promote the exchange of innovative ideas, findings, and research from academia and industry, thereby pushing 6G a big step towards the era of LMs.
For this special issue, we are seeking high-quality submissions that will help to advance the theoretical and practical frontiers of 6G and LMs, so that we can gain a deeper understanding from both the academic and industrial viewpoints.
Topics Covered include, but are not limited to:
- LM-enabled optimization technologies for 6G network
- LM-enabled privacy protection for 6G network
- LM-enabled sustainable green 6G networks
- LM-enabled network slicing for 6G network
- LM-enabled efficient bandwidth/resource allocation for 6G network
- Innovative LM-empowered applications for 6G network
- 6G architecture and protocols design for training, inference, and deployment of LMs
- 6Gdomain-specific LMs
- Novel LM training and adaptation methods for 6G network
- Integrating edge computing with 6G for real-time processing of LMs
- Integration of LMs and other AI technologies (such as DNN, RL, and FL) for 6G network
Important Dates:
- Submission deadline: 31 October 2024
- First notification:31 January 2025
- Revised version deadline:31 March 2025
- Final notification: 31May 2025
- Publication date: 31 October 2025
Submission Instructions:
Please read the Guide for Authors before submitting. All articles should be submitted online via the editorial management system. Please select the option ‘SI: 6GLM’.
Guest Editors:
- Managing Guest Editor, Jianhui Lv, Peng Cheng Laboratory, China. Email: lvjh@pcl.ac.cn
- Byung-Gyu Kim, Sookmyung Women’s University, Korea.Email: kim@sookmyung.ac.kr
- Keqin Li, The State University of New York, USA. Email: lik@newpaltz.edu
- Adam Slowik, Koszalin University of Technology, Poland. Email: slowik@tu.koszalin.pl
- Yuhui Shi, Southern University of Science and Technology, China. Email: shiyh@sustech.edu.cn