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Special Issue on Large AI Model for Future Wireless Communication Systems
Submission Date: 2025-05-01

The emergence of future wireless networks, e.g., 6G aims to further enhance users’ experience by offering services of superior reliability, speed, and quality compared to existing wireless technologies. Artificial Intelligence (AI) has evolved from traditional algorithms to deep learning and now to Large AI Models (LAMs), increasingly revolutionizing wireless communication systems. Renowned LAMs like SORA, GPT-4o and Gemini have demonstrated amazing performance across diverse applications, positioning them as powerful tools for tackling complex challenges in future wireless communication systems. For example, by integrating advanced cognitive modules such as comprehensive perception, world modeling, and action planning, LAMs can achieve fully autonomous and intelligent network operations, handle unforeseen communication scenarios, and provide revolutionary capabilities and experiences in various complex environments. These applications not only demonstrate the powerful learning and optimization capabilities of LAMs but also offer new directions and insights for the development of future wireless network technologies.

Despite the potential benefits of LAMs in future wireless communication systems, deploying them in the practical environment introduces several challenges, such as computational power requirements, efficient training methodologies, and model parameter scaling along with the characteristics of wireless networks. The primary goal of this Special Issue is to encourage researchers to tackle the crucial challenges that emerge when applying LAMs to future wireless communication systems. The topics of interest include (but are not limited to):

    LAMs for cognitive-level resource allocation, channel estimation, mobility management and physical layer algorithm design
    LAMs for semantic communications
    LAMs for intelligent operation and maintenance
    LAMs for telecom language understanding
    LAMs for low-latency digital twins
    LAMs for network protocol design, control and architecture development
    LAMs for intent-driven edge intelligence applications
    LAMs for future energy-efficient and green networks
    Performance analysis of LAMs in wireless networks, e.g., explainability, trust, privacy, and security-related issues
    Novel testbeds, complex simulations and experiment of LAMs in future wireless networks