Big AI Models for 6G Wireless Networks: Opportunities, Challenges, and Research Directions

TL;DR

This paper explores the potential of big AI models in 6G wireless networks, discussing their design, deployment, and future research directions to enable intelligent communication, sensing, and computing.

Contribution

It provides a comprehensive analysis of wireless BAIMs (wBAIMs), highlighting their design principles, system evaluation, and differences from existing BAIMs, along with future research prospects.

Findings

wBAIM can enable high-efficiency, sustainability, and versatility in 6G networks

Core characteristics and principles guide the design of wBAIMs

Future research directions include system evaluation and deployment challenges

Abstract

Recently, big artificial intelligence models (BAIMs) represented by chatGPT have brought an incredible revolution. With the pre-trained BAIMs in certain fields, numerous downstream tasks can be accomplished with only few-shot or even zero-shot learning and exhibit state-of-the-art performances. As widely envisioned, the big AI models are to rapidly penetrate into major intelligent services and applications, and are able to run at low unit cost and high flexibility. In 6G wireless networks, to fully enable intelligent communication, sensing and computing, apart from providing other intelligent wireless services and applications, it is of vital importance to design and deploy certain wireless BAIMs (wBAIMs). However, there still lacks investigation on architecture design and system evaluation for wBAIM. In this paper, we provide a comprehensive discussion as well as some in-depth prospects on the demand, design and deployment aspects of the wBAIM. We opine that wBAIM will be a recipe of the 6G wireless networks to build high-efficient, sustainable, versatile, and extensible wireless intelligence for numerous promising visions. Then, we provide the core characteristics, principles, and pilot studies to guide the design of wBAIMs, and discuss the key aspects of developing wBAIMs through identifying the differences between the existing BAIMs and the emerging wBAIMs. Finally, related research directions and potential solutions are outlined.