Abstractions for Network Intelligence: A Reference Architecture for AI at the Wireless Edge

2026-08-10

AI Summary

Current Internet architectures struggle to effectively support the sharing of sensing information between networks and AI applications, limiting the joint optimization of resource utilization and performance. This work proposes AI-EDGE, a reference architecture that introduces, for the first time, a network intelligence abstraction framework tailored for wireless edge AI. By incorporating an “information waist” layer, AI-EDGE enables efficient co-design and collaboration between intelligent networks and intelligent applications. The architecture is compatible with mainstream platforms such as O-RAN and Multi-access Edge Computing (MEC), supporting sensing data sharing, application portability, and rapid prototyping. Validation through diverse representative use cases in O-RAN cellular networks and 3GPP/ETSI edge computing environments demonstrates significant advantages across all these dimensions.

Abstract

Networks are increasingly adopting AI as are AI applications leveraging networks. Awareness sharing between networks and AI applications promises to unlock higher levels of network utilization and application performance, but is inadequately supported in the current architecture of the Internet. In this paper, we describe a reference architecture that abstractly enables the synergistic interaction of intelligent applications and the intelligent network, via an information waist, and also supports the network intelligence services in the emerging intelligence plane in networks. We discuss the rationale for our AI-EDGE architecture, its functional requirements, and the core abstractions. We present a reference component-level design of the core abstractions to support experimentation and development on existing platforms for wireless networking (i.e., based on O-RAN cellular networks) and edge computing (i.e., based on 3GPP Edge App and ETSI MEC). Moreover, we provide representative use cases from the perspective of different types of users that demonstrate the benefits of the architecture in contexts of awareness sharing, portability, prototyping, and validation.

Problem

• Network Intelligence
• AI at the Edge
• Awareness Sharing
• Wireless Networks
• Intelligent Applications

Innovation

• AI-EDGE architecture
• information waist
• network intelligence
• edge computing
• awareness sharing