The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Interactive AI With Retrieval-Augmented Generation for Next Generation Networking
Open accessAbstract
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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Citations
Cites
A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects
Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3084827 · Cited by 5,273 · cited 1×
“Phase 2: Predictive AI (PAI) /Discriminative AI (DAI) With the use of deep neural networks such as convolutional neural networks (CNNs), PAI and DAI are good at learning special paradigms from large data sets [4].”
BackgroundRetrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-T., Rocktäschel, T., et al. (2020) · UCL Discovery (University College London) · Cited by 1,187
The Rise and Potential of Large Language Model Based Agents: A Survey
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The rise and potential of large language model based agents: a survey
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Artificial intelligence: a survey on evolution, models, applications and future trends
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Learning Graph Structures With Transformer for Multivariate Time-Series Anomaly Detection in IoT
Chen, Z., Chen, D., Zhang, X., Yuan, Z., & Cheng, X. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3100509 · Cited by 638
Automatic Steganographic Distortion Learning Using a Generative Adversarial Network
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Du, H., Zhang, R., Liu, Y., Wang, J., Lin, Y., Li, Z., Niyato, D. T., Kang, J., Xiong, Z., Cui, S. R., et al. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3400011 · Cited by 315
Self-citationEnergy Efficiency Maximization in RIS-Assisted SWIPT Networks With RSMA: A PPO-Based Approach
Zhang, R., Xiong, K., Lu, Y., Fan, P., Ng, D. W. K., & Letaief, K. B. (2023) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2023.3240707 · Cited by 297
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Zhang, R., Du, H., Liu, Y., Niyato, D. T., Kang, J., Xiong, Z., Jamalipour, A., & Kim, D. I. (2024) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2024.3459037 · Cited by 277
Self-citationZhang, R., Liu, G., Liu, Y., Zhao, C., Wang, J., Xu, Y., Niyato, D. T., Kang, J., Li, Y., Mao, S., et al. (2026) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2026.3651702 · Cited by 114
Self-citationGenerative AI for Advanced UAV Networking
Sun, G., Xie, W., Niyato, D. T., Du, H., Kang, J., Wu, J., Sun, S., & Zhang, P. (2024) · IEEE Network · doi:10.1109/mnet.2024.3494862 · Cited by 67
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Self-citationLarge Language Models for Zero Touch Network Configuration Management
Lira, O. G., Rendón, O. M. C., & da Fonseca, N. L. S. (2024) · IEEE Communications Magazine · doi:10.1109/mcom.001.2400368 · Cited by 61
Metadata
- 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
- Venue
- IEEE Network
- Published
- 2024-11
- Volume
- 38
- Issue
- 6
- Pages
- 414-424
- ISSN
- 0890-8044 (ISSN-L)
- 1558-156X
- 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
- 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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