Large Artificial Intelligence Models for Future Wireless Communications

TL;DR

The paper argues that future wireless networks will benefit from large, transformer-based AI architectures capable of real-time adaptation, edge coordination, and multi-task optimization. It surveys AI-driven applications (edge computing, semantic communications, security threat prediction, energy efficiency, satellite links, and demand forecasting) and outlines a vision for large AI models that coordinate modular sub-systems to manage complex networks. It identifies energy, architecture, privacy/security, scalability, and ethical/regulatory challenges, proposing solutions such as hybrid cloud-edge-fog designs, privacy-preserving learning, and adaptive algorithms. The work highlights future directions like adaptive learning, global connectivity, and custom AI model development, suggesting that these models could enable personalized, efficient, and globally connected wireless systems, while raising important governance considerations.

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.

Figures (6)

• Figure 1: Traditional AI model for wireless communication tasks.
• Figure 2: Existing large AI models.
• Figure 3: Large language and visual AI models.
• Figure 4: Large AI model in text semantic communications.
• Figure 5: Large AI model in image semantic communications.