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The proliferation of mobile devices and continuously increasing demands for user services has stimulated an explosive surge of data in wireless networks. Recently, mobile artificial intelligence-generated content (AIGC), an emerging paradigm for mobile data generation, manipulation, and modification, has been applied to a variety of applications, such as ChatGPT and DALL-E. Compared with discriminative AI models that optimize networks under predefined operations, the generative model leverages innovative generative AI (GAI) algorithms, e.g., Generative Adversarial Network (GAN), Transformer, and diffusion model, to automatically and creatively generate customized network solutions. However, how current mobile networks can efficiently support the fancy mobile AIGC services and how GAI as a state-of-the-art machine learning tool can be exploited to improve network performance require immediate attention and investigation.

On one hand, since mobile AIGC models usually consist of billions of parameters, it is difficult to deploy mobile AIGC on the edge due to the limited computing, communication, and storage resources of mobile edge devices and servers. Furthermore, the deployment of mobile AIGC models in core networks and the large-scale distributive data to be collected and trained in the cloud will also lead to tremendous transmission latency. On the other hand, how to explore novel GAI algorithms for novel network designs and optimization strategies to support emerging mobile services is of great necessity as they are envisioned to provide unprecedented capability in enhancing network performance, security, and privacy in various mobile applications for the next-generation mobile network (6G), such as vehicular networks, Internet of things (IoT), space-air-ground integrated network (SAGIN), unmanned aerial vehicle (UAV) networks, and various physical layer communications. Moreover, the heterogeneity, high dynamics, and large-scale characteristics of 6G network deployment have posed significant challenges to mobile GAI-aided network control and resource management. Thereby, with the observation of the recent surge in relevant research, this Special Issue (SI) seeks to bring together researchers from both academia and industry to share recent research results, technology advances, and future trends, including novel architecture, theory, evaluation, and applications towards mobile GAI-aided 6G as well as 6G-supported emerging AIGC applications and services. Authors are invited to submit manuscripts on the topics including, but not limited to, the following:

• Network architecture design for mobile AIGC and AIGC applications.
• Novel frameworks and new protocols for mobile AIGC in 6G.
• Emerging technologies (e.g., quantum communication, semantic communication, blockchain) for mobile AIGC in 6G.
• Edge AI and federated learning for mobile AIGC in 6G.
• Network performance evaluation for mobile AIGC in 6G.
• Mobile AIGC for network trust, privacy, and cyber security.
• Mobile AIGC for vehicular networks, IoT, SAGIN, UAV networks.
• Mobile AIGC for network services provisioning.
• Mobile AIGC for 6G simulations and testbeds.
• Mobile GAI for physical layer communications (e.g., IRS and XL-MIMO).
• Mobile GAI for green communications.
• Mobile GAI for massive radio access.
• Mobile GAI for wireless resource management.
• Mobile AIGC & GAI for mobility management.
• Mobile AIGC & GAI for network operation and control.

Submission Guidelines

Prospective authors should prepare their submissions in accordance with the rules specified in the "Information for Authors" of the IEEE Wireless Communications guidelines.

Authors should submit a PDF version of their complete manuscript to Manuscript Central. The timetable is as follows:

Important Dates

Manuscript Submission Deadline: 8 December 2023 (Extended Deadline)
Initial Decision Date: 1 February 2024
Revised Manuscript Due: 1 March 2024
Final Decision Date: 1 April 2024
Final Manuscript Due: 1 June 2024
Publication Date: August 2024

Guest Editors

Haibo Zhou
Nanjing University, China

Hong-Ning Dai
Hong Kong Baptist University, Hong Kong, China

Xiang Cheng
Peking University, China

Diep N. Nguyen
University of Technology Sydney, Australia

Hina Tabassum
York University, Canada