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Fifth Generation (5G) wireless technology is revolutionizing communication networks by providing ultra-low latency, massive device connectivity, and ultra-high-speed data transmission. The emergence of 6G will further amplify these changes. One of the primary visions of 6G is ubiquitous intelligence, where artificial intelligence (AI) has evolved from traditional discriminative/generative AI algorithms to large AI models (LAMs), significantly impacting wireless communication systems. Renowned LAMs, such as ChatGPT, LLaMA, DeepSeek, and Gemini exhibit human-level understanding and cognitive abilities, demonstrating exceptional performance in various applications. In native AI wireless systems, LAMs can utilize cognitive abilities, such as perception, analogy, and reasoning to rapidly simulate communication environments, enabling efficient generalization and adaptation to unforeseen communication scenarios, and providing fully personalized services. This highly autonomous and adaptive new communication paradigm holds immense potential in future 6G systems.

Despite the broad application prospects of LAMs in communications, there are numerous challenges for both LAMs for communications and communications for LAMs. These challenges include the high computational resources and energy consumption required by LAMs, the collaboration between LAMs and small communication models, the lack of specialized communication knowledge in LAMs, and the absence of datasets and evaluation standards for LAMs in the communication field. Hence, this Special Issue (SI) aims to bring together researchers and experts from academia and industry to consider the deep integration of communications and LAMs. Topics of interest include, but are not limited to:

• LAMs for semantic communications.
• LAMs for integrated sensing and communication (ISAC) systems.
• LAMs for network protocol design, control and architecture development.
• LAMs for intelligent edge computing systems.
• LAMs for quantum communications.
• LAMs for digital twin and metaverse.
• LAMs for reconfigurable intelligent surfaces (RIS).
• LAMs for wireless federated learning.
• LAMs for non-terrestrial networks (NTN), e.g., unmanned aerial vehicles (UAVs), and satellite communication systems.
• Testbeds and prototypes of 6G network-enabled LAM services and applications.
• LAMs for channel estimation, resource allocation, and optimization.
• Standardization and industrial progress for LAMs in wireless communications.
• Other key issues of LAMs in communication systems, e.g., explainability, privacy, and security.

Submission Guidelines

Manuscripts should conform to the standard format as indicated in the Information for Authors section of the Manuscript Submission Guidelines. Please, check these guidelines carefully before submitting since submissions not complying with them will be administratively rejected without review.

Solicited and invited papers shall undergo the standard IEEE peer review process. After initial screening, the paper will immediately go to the review process. Prospective authors should submit their manuscripts following the IEEE Communication Magazine guidelines. Authors should submit a PDF version of their complete manuscript to IEEE Submission Portal.

Important Dates

Manuscript Submission Deadline: 31 July 2025 (Deadline Extended)
Initial Decision: 1 October 2025
Revised Manuscript Due: 1 November 2025
Final Decision: 1 December 2025
Final Manuscript Due: 10 December 2025
Publication Date: January 2026

Guest Editors

Kezhi Wang (Lead Guest Editor)
Brunel University of London, U.K.

Feibo Jiang
Hunan Normal University, China

Merouane Debbah
Khalifa University of Science and Technology, UAE

Zhu Han
University of Houston, USA