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

TL;DR

It is opine that wBAIM will be a recipe for the 6G wireless networks to build high-efficient, sustainable, versatile, and extensible wireless intelligence for numerous promising visions.

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.

Figures (5)

• Figure 1: BAIM, also termed as foundation model, is a new machine learning paradigm that captures broad-adaptable intelligence and provides numerous downstream applications through appropriate modeling, sufficient pre-training and customized adaptations. Establishing BAIM dedicated to wireless networks will enable an evolution of wirless systems toward more integrated functions, more flexible architectures, and more differentiated services.
• Figure 2: The numerous use scenarios in 6G wireless networks place urgent demands for high-efficient, sustainable, versatile, and extensible wireless intelligence. This required intelligence is envisioned to be built through the wBAIM.
• Figure 3: The distinguishing features of wBAIM-based architecture are as following. First pre-training a wBAIM that captures universal wireless intelligence, and then providing a unified paradigm in deployment based on the wBAIM: integrating multiple wireless tasks, unifying multiple communication scenarios, and network-wide all-in-one scheduling.
• Figure 4: A graphic illustration of pilot studies in wireless pre-training and unified deployments. The left part shows how to use a CMixer cmixer model to serve both channel estimation and feedback tasks and provides the parameter settings of this model. The middle part shows the communication scenarios and system settings from the open source dataset DeepMIMO deepmimo. We assume that the training data is collected from scenario 1 and 2 and the trained models are deployed in scenario 1, 2 and 3. The right part shows the test performance in each scenario with 10 independent repeated experiments. The colorful bars are the mean values and the error bars are upper and lower limits.
• Figure 5: The challenges and key problems of the wBAIM.