Interactive AI with Retrieval-Augmented Generation for Next Generation Networking

TL;DR

This paper proposes an interactive AI framework integrated into next-generation networks, utilizing retrieval-augmented generation and large language models to enhance network management, user experience, and system adaptability.

Contribution

It introduces a novel IAI-enabled network management framework with pluggable LLM and RAG modules, advancing AI integration in networking for improved performance.

Findings

Effective IAI framework demonstrated through case studies

Enhanced network management and user experience achieved

Potential research directions outlined for future development

Abstract

With the advance of artificial intelligence (AI), the emergence of Google Gemini and OpenAI Q* marks the direction towards artificial general intelligence (AGI). To implement AGI, the concept of interactive AI (IAI) has been introduced, which can interactively understand and respond not only to human user input but also to dynamic system and network conditions. In this article, we explore an integration and enhancement of IAI in networking. We first comprehensively review recent developments and future perspectives of AI and then introduce the technology and components of IAI. We then explore the integration of IAI into the next-generation networks, focusing on how implicit and explicit interactions can enhance network functionality, improve user experience, and promote efficient network management. Subsequently, we propose an IAI-enabled network management and optimization framework, which consists of environment, perception, action, and brain units. We also design the pluggable large language model (LLM) module and retrieval augmented generation (RAG) module to build the knowledge base and contextual memory for decision-making in the brain unit. We demonstrate the effectiveness of the framework through case studies. Finally, we discuss potential research directions for IAI-based networks.