Welcome to our research

Welcome to our research on applying generative artificial intelligence (GAI) in intelligent networks. Two core goals of these efforts are to explore how intelligent networks can support various artificial intelligence-generated content (AIGC) services, and how generative AI can enhance network performance. In response to the above goals, our research is mainly divided into the following three main topics, i.e., Diffusion Reinforcement Learning, Large Language Models, and Diffusion Graph Generation. In addition, our research also includes Semantic Communication, Resource Allocation, Integrated Sensing and Communication (ISAC), and Metaverse applications integrated into the GAI.

Main topics

Diffusion Reinforcement Learning
Interactive AI Agent
Resource Allocation

Publications

Recommended Publications

Beyond Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization
Hongyang Du, Ruichen Zhang, Yinqiu Liu, Jiacheng Wang, Yijing Lin, Zonghang Li, Dusit Niyato, Jiawen Kang, Zehui Xiong, Shuguang Cui, Bo Ai, Haibo Zhou, Dong In Kim
IEEE COMST

The first tutorial paper to explain various applications of diffusion models and coding examples.
Paper | Code | YouTube | Bilibili

Generative AI for Space-Air-Ground Integrated Networks (SAGIN)
Ruichen Zhang, Hongyang Du, Dusit Niyato, Jiawen Kang, Zehui Xiong, Abbas Jamalipour, Ping Zhang, Dong In Kim
IEEE Wireless Communications Magazine

This paper explores an integration of generative AI in SAGIN, focusing on potential applications and case study.
Paper | YouTube | Bilibili

AI-Generated Incentive Mechanism and Full-Duplex Semantic Communications for Information Sharing
Hongyang Du, Jiacheng Wang, Dusit Niyato, Jiawen Kang, Zehui Xiong, Dong In Kim
IEEE Journal on Selected Areas in Communications

This paper proposes a contract theoretic AI-generated incentive mechanism that generates the optimal contract design.
Paper | Code

Update Publications

Optimizing Generative AI Networking: A Dual Perspective with Multi-Agent Systems and Mixture of Experts
Ruichen Zhang, Hongyang Du, Dusit Niyato, Jiawen Kang, Zehui Xiong, Ping Zhang, Dong In Kim
Under Review

The paper explores a dual perspective multi-agent systems and mixture of experts for generative AI Networking.
Paper | Code | YouTube | Bilibili

Enhancing Physical Layer Communication Security through Generative AI with Mixture of Experts
Changyuan Zhao, Hongyang Du, Dusit Niyato, Jiawen Kang, Zehui Xiong, Dong In Kim, Xuemin (Sherman) Shen, Khaled B. Letaief
Under Review

The paper explores mixture of experts-enabled generative AI, focusing on physical layer communication security.
Paper

Defining Problem from Solutions: Inverse Reinforcement Learning (IRL) and Its Applications for Next-Generation Networking
Yinqiu Liu, Ruichen Zhang, Hongyang Du, Dusit Niyato, Jiawen Kang, Zehui Xiong, and Dong In Kim
Under Review

This paper introduces Inverse Reinforcement Learning and its applications in networking.
Paper | Code | YouTube | Bilibili

Generative AI for Low-Carbon Artificial Intelligence of Things
Jinbo Wen, Ruichen Zhang, Dusit Niyato, Jiawen Kang, Hongyang Du, Yang Zhang, Zhu Han
Under review

This paper explores the potential of GAI for carbon emissions reduction and proposes a novel GAI-enabled solution for low-carbon AIoT.
Paper

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