A Comprehensive Survey of Large AI Models for Future Communications: Foundations, Applications and Challenges

Authors

Feibo Jiang, Cunhua Pan, Li Dong, Kezhi Wang, Merouane Debbah, Dusit Niyato, Zhu Han

Paper

https://arxiv.org/abs/2505.03556

Code

https://github.com/jiangfeibo/ComLAM

Abstract

The 6G wireless communications aim to establish an intelligent world of ubiquitous connectivity, providing an unprecedented communication experience. Large artificial intelligence models (LAMs) are characterized by significantly larger scales (e.g.,billions or trillions of parameters) compared to typical artificial intelligence (AI) models. LAMs exhibit outstanding cognitive abilities, including strong generalization capabilities for fine-tuning to downstream tasks, and emergent capabilities to handle tasks unseen during training. Therefore, LAMs efficiently provide AI services for diverse communication applications, making them crucial tools for addressing complex challenges in future wireless communication systems. This study provides a comprehensive review of the foundations, applications, and challenges of LAMs in communication. First, we introduce the current state of AI-based communication systems, emphasizing the motivation behind integrating LAMs into communications and summarizing the key contributions. We then present an overview of the essential concepts of LAMs in communication. This includes an introduction to the main architectures of LAMs, such as transformer, diffusion models, and mamba. We also explore the classification of LAMs,including large language models (LLMs), large vision models (LVMs), large multimodal models (LMMs), and world models,and examine their potential applications in communication.Additionally, we cover the training methods and evaluation techniques for LAMs in communication systems. Lastly, we introduce optimization strategies such as chain of thought (CoT), retrieval augmented generation (RAG), and agentic systems. Following this,we discuss the research advancements of LAMs across various communication scenarios, including physical layer design, resource allocation and optimization, network design and management,edge intelligence, semantic communication, agentic systems, and emerging applications. Finally, we analyze the challenges in the current research and provide insights into potential future research directions.

Contents

• A Comprehensive Survey of Large AI Models for Communications: Foundations, Applications, and Challenges
  • Abstract
  • Contents
  • I. INTRODUCTION
    • A.Background
      • 1) Deep learning-assisted communication
      • 2) Reinforcement learning-assisted communication
      • 3) Generative AI-assisted communication
    • B. Motivation
      • 1) Definition
      • 2) Distinction between LAMs and GAIs
      • 3) Distinction between LAMs and pretrained FMs
    • C. Related survey work
      • 1) Limited model coverage
      • 2) Incomplete application landscape
    • D. Contributions
      • 1) Foundations of LAMs for communications
      • 2) Applications of LAMs for communications
      • 3) Research challenges of LAMs for communications
  • II. FOUNDATIONS OF LAMS FOR COMMUNICATIONS
    • A. Key architecture of LAMs
      • 1) Transformer model
      • 2) Diffusion model
      • 3) Mamba model
    • B. Classification of LAMs
      • 1) Large language model
      • 2) Large vision model
      • 3) Large multimodal model
      • 4) World model
    • C. Training of LAMs for communications
      • 1) Pre-training of LAMs for communications
      • 2) Fine-tuning of LAMs for communications
      • 3) Alignment of LAMs for communications
    • D. Evaluation of LAMs for communications
      • 1) Communication Q&A
      • 2) Communication tool learning
      • 3) Communication modeling
      • 4) Communication code design
    • E. Optimization of LAMs
      • 1) Chain of thought
      • 2) Retrieval-augmented generation
      • 3) Agentic system
    • F. Summary and lessons learned
      • 1) Summary
      • 2) Lessons learned
  • III. LAMS FOR PHYSICAL LAYER DESIGN
    • A. Channel and beam prediction based on LAMs
    • B. Automated physical layer design based on LAM
    • C. Summary and lessons learned
      • 1) Summary
      • 2) Lessons learned
  • IV. LAMS FOR RESOURCE ALLOCATION AND OPTIMIZATION
    • A. Computing resource allocation
    • B. Spectrum resource allocation
    • C. Energy resource optimization
    • D. Summary and lessons learned
      • 1) Summary
      • 2) Lessons learned
  • V. LAMS FOR NETWORK DESIGN AND MANAGEMENT
    • A. Network design
    • B. Network management
    • C. Summary and lessons learned
      • 1) Summary
      • 2) Lessons learned
  • VI. LAMS FOR EDGE INTELLIGENCE
    • A. Edge training and application of LAMs
    • B. Edge resource scheduling meets LAMs
    • C. Federated learning of LAMs
    • D. Summary and lessons learned
      • 1) Summary
      • 2) Lessons learned
  • VII. LAMS FOR SEMANTIC COMMUNICATION
    • A. LLM-based semantic communication systems
    • B. Other LAM-based semantic communication systems
    • C. Summary and lessons learned
      • 1) Summary
      • 2) Lessons learned
  • VIII. LAM-BASED AGENTIC SYSTEMS
    • A. Agentic systems based on LLMs
    • B. Agentic systems based on other GAI models
    • C. Summary and lessons learned
      • 1) Summary
      • 2) Lessons learned
  • IX. LAMS FOR EMERGING APPLICATIONS
    • A. Smart healthcare
    • B. Carbon emissions
    • C. Digital twins
    • D. Artificial intelligence of things
    • E. Integrated satellite, aerial, and terrestrial networks
    • F. Integration of UAVs and LLMs
    • G. Summary and lessons learned
  • X. RESEARCH CHALLENGES
    • 1). Lack of high-quality communication data
    • 2). Lack of structured communication knowledge
    • 3). Generative hallucination in communication
    • 4). Limitations of reasoning ability
    • 5). Poor explainability in LAMs
    • 6). Adaptability in dynamic environments
    • 7). Diversity of communication tasks
    • 8). Resource constraints at the edge
    • 9). High inference latency
    • 10). Security and privacy
  • Communication datasets for LAMs
  • Classification of LAMs
  • Paper With Code
  • The Team
  • Acknowledgments
  • Update log

Fig. 1: The development history of LAMs.

Fig. 2: The role of LAMs in AI.

Fig. 3: Overall organization of the survey.

Fig. 4: Applications of LAMs in Communication. LAMs can be applied across various domains in communication, including physical layer design, resource allocation and optimization, network design and management, edge intelligence, semantic communication, agentic systems, and emerging applications.

Communication datasets for LAMs

Classification of LAMs

Paper with code

The Team

Here is the list of our student contributors in each section.

Contact Information for Source Code Submission or Update

If you intend to add or update the source code in the repository, please contact the following email addresses: jiangfb@hunnu.edu.cn, Dlj2017@hunnu.edu.cn, 240620854087@stu.hutb.edu.cn and 240620854065@stu.hutb.edu.cn.

Update Log

Citation

 @ARTICLE{2025arXiv250503556J,
      title = {A Comprehensive Survey of Large AI Models for Future Communications: Foundations, Applications and Challenges},
      author = {Feibo Jiang, Cunhua Pan, Li Dong, Kezhi Wang, Merouane Debbah, Dusit Niyato, Zhu Han},
      journal = {arXiv preprint arXiv:2505.03556v1},
      year = {2025}
}