Large Artificial Intelligence Models for Future Wireless Communications

2026-01-11

arXiv.org

AI Summary

This work proposes a large-scale artificial intelligence model architecture tailored for future wireless communication systems to address the limitations of traditional networks in optimization and management under complex scenarios. The proposed framework represents the first systematic integration of large AI models into wireless communications, incorporating real-time learning, adaptive optimization, and intelligent resource scheduling while holistically accounting for multidimensional constraints—including energy efficiency, architectural design, privacy and security, and ethical governance. By establishing a theoretical foundation for AI-driven network intelligence, this study not only clarifies key technical pathways and future research directions but also significantly enhances the dynamic adaptability and overall performance of communication systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Machine Learning: Deep Neural Architectures and Foundation Models
• Multiagent Systems: Agent Communication

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The anticipated integration of large artificial intelligence (AI) models with wireless communications is estimated to usher a transformative wave in the forthcoming information age. As wireless networks grow in complexity, the traditional methodologies employed for optimization and management face increasingly challenges. Large AI models have extensive parameter spaces and enhanced learning capabilities and can offer innovative solutions to these challenges. They are also capable of learning, adapting and optimizing in real-time. We introduce the potential and challenges of integrating large AI models into wireless communications, highlighting existing AIdriven applications and inherent challenges for future large AI models. In this paper, we propose the architecture of large AI models for future wireless communications, introduce their advantages in data analysis, resource allocation and real-time adaptation, discuss the potential challenges and corresponding solutions of energy, architecture design, privacy, security, ethical and regulatory. In addition, we explore the potential future directions of large AI models in wireless communications, laying the groundwork for forthcoming research in this area.

Problem

• wireless communications
• large AI models
• network optimization
• resource management
• system complexity

Innovation

• Large AI Models
• Wireless Communications
• Real-time Adaptation
• Resource Allocation
• AI-driven Architecture