End to End AI Architecture for Next Generation Network

Abstract

Targeting 6G network architecture with native intelligence, a holistic view on the end to end (E2E) Artificial Intelligence (AI) flow across Operation Administration and Maintenance (OAM), Core Network (CN), Edge, Radio Access Network (RAN), and User Equipment (UE) domains is presented in this article. The AI Task Management (AITM) is introduced as the enabler in such network, and a User Equipment Data Analytics (UEDA) is proposed to take advantage of the increasing computing capability of UEs while taking their power consumption limitation into consideration. Conventional control plane and user plane connections assume fixed one-to-one end points and a single chosen transmission mode. Depending on the goal of the AI task and what network entities need to participate, the endpoints of the AI task connection is uncertain, with a group of participant UEs on one side and base station, core network or application server on the other. The transmission mode of such connection may need to be dynamically reconfigured as well. This article proposes an approach of AI task-oriented connections, where logical connections among UE, RAN, CN, and applications are established via AITM for AI model distribution, data collection, as well as parameter synchronization across different domains. A new wireless bearer type, named AI Radio Bearer (AIRB), for AI task-oriented connections is proposed with due design considerations and its associated performance.