Cited by 151Software-Defined Networks and 5GAdvanced Data and IoT TechnologiesArtificial Intelligence Applications

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Interactive AI With Retrieval-Augmented Generation for Next Generation Networking

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Zhang, Ruichen, Du, Hongyang, Liu, Yinqiu, Niyato, Dusit, Kang, Jiawen, Sun, Sumei, Shen, Xuemin, Poor, H. Vincent·2024·IEEE Network

DOI 10.1109/mnet.2024.3401159

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Abstract

With the advance of artificial intelligence (AI), 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 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 a 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 through case studies that our IAI framework can effectively perform optimization problem design. Finally, we discuss potential research directions for IAI-based networks.

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Research works printed but not linked: 2

Zhang, 2024, Interactive generative AI agents for satellite networks through a mixture of experts transmission, arXiv:2404.09134 · arXiv
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Authors: Zhang, R., Du, H., Liu, Y., Niyato, D., Kang, J., Sun, S., Shen, X., & Poor, H. V.
Affiliations: Nanyang Technological University (SG)
Guangdong University of Technology (CN)
Agency for Science, Technology and Research (SG)
University of Waterloo (CA)
Princeton University (US)
ORCID: Zhang, Ruichen
Du, Hongyang
Liu, Yinqiu
Niyato, Dusit
Kang, Jiawen
Sun, Sumei
Shen, Xuemin
Poor, H. Vincent
Venue: IEEE Network
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2024-11
Volume: 38
Issue: 6
Pages: 414-424
ISSN: 0890-8044 (ISSN-L)
1558-156X
DOI: 10.1109/mnet.2024.3401159
OpenAlex: W4396941482
Cited by: 147
References: 14
Citations by year: 2026: 61 · 2025: 73 · 2024: 13
FWCI: 24.47
Type: journal article
Language: en
Open access: green
arxiv.org
Licence: cc-by
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (am)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Network (publishedVersion)
arXiv (Cornell University) (submittedVersion, cc-by)
DR-NTU (Nanyang Technological University) (submittedVersion)
The HKU Scholars Hub (University of Hong Kong) (submittedVersion)
arXiv (Cornell University) (cc-by)
Topics: Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Data and IoT Technologies
Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Keywords: interactive AI, retrieval-augmented generation, next generation networks, network management, network optimization, large language models, contextual memory, knowledge base, user experience

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Interactive AI With Retrieval-Augmented Generation for Next Generation Networking

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