Space-ground Fluid AI for 6G Edge Intelligence

TL;DR

This paper introduces space-ground fluid AI, a novel framework extending edge AI to space via satellite mobility, enabling seamless AI service delivery across the planet despite high satellite mobility.

Contribution

It proposes the space-ground fluid AI framework with key components for fluid learning, inference, and model downloading, leveraging satellite mobility for continuous AI services.

Findings

Fluid AI supports non-disruptive AI services despite satellite mobility.

The framework enables inter-satellite and space-ground cooperation for AI tasks.

Discussion on deployment considerations and future research directions.

Abstract

Edge artificial intelligence (AI) and space-ground integrated networks (SGINs) are two main usage scenarios of the sixth-generation (6G) mobile networks. Edge AI supports pervasive low-latency AI services to users, whereas SGINs provide digital services to spatial, aerial, maritime, and ground users. This article advocates the integration of the two technologies by extending edge AI to space, thereby delivering AI services to every corner of the planet. Beyond a simple combination, our novel framework, called space-ground fluid AI, leverages the predictive mobility of satellites to facilitate fluid horizontal and vertical task/model migration in the networks. This ensures non-disruptive AI service provisioning in spite of the high mobility of satellite servers. The aim of the article is to introduce the (space-ground) fluid AI technology. First, we outline the network architecture and unique characteristics of fluid AI. Then, we delve into three key components of fluid AI, i.e., fluid learning, fluid inference, and fluid model downloading. They share the common feature of coping with satellite mobility via inter-satellite and space-ground cooperation to support AI services. Finally, we discuss the considerations for the real-world deployment of fluid AI and identify further research opportunities.