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20212023
most citedNetworking Architecture and Key Supporting Technologies for Human Digital Twin in Personalized Healthcare: A Comprehensive Survey

247 citations · 565 across the 34 of their papers we have counts for

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Showing 2023Show all

16 papers · 1 filter

cs.NI2023★ 1 cited

A Unified Framework for Guiding Generative AI with Wireless Perception in Resource Constrained Mobile Edge Networks

Jiacheng Wang, Hongyang Du, Dusit Niyato +6

With the significant advancements in artificial intelligence (AI) technologies and powerful computational capabilities, generative AI (GAI) has become a pivotal digital content gen…

cs.AI2023★ 5 cited

Artificial Intelligence for Web 3.0: A Comprehensive Survey

Meng Shen, Zhehui Tan, Dusit Niyato +7

Web 3.0 is the new generation of the Internet that is reconstructed with distributed technology, which focuses on data ownership and value expression. Also, it operates under the p…

cs.NI2023★ 1 cited

Digital Twin Based User-Centric Resource Management for Multicast Short Video Streaming

Xinyu Huang, Wen Wu, Shisheng Hu +3

Multicast short video streaming (MSVS) can effectively reduce network traffic load by delivering identical video sequences to multiple users simultaneously. The existing MSVS schem…

cs.NI2023★ 4 cited

Semantic Communications for Artificial Intelligence Generated Content (AIGC) Toward Effective Content Creation

Guangyuan Liu, Hongyang Du, Dusit Niyato +5

Artificial Intelligence Generated Content (AIGC) Services have significant potential in digital content creation. The distinctive abilities of AIGC, such as content generation base…

cs.NI2023★ 6 cited

A Revolution of Personalized Healthcare: Enabling Human Digital Twin with Mobile AIGC

Jiayuan Chen, Changyan Yi, Hongyang Du +5

Mobile Artificial Intelligence-Generated Content (AIGC) technology refers to the adoption of AI algorithms deployed at mobile edge networks to automate the information creation pro…

eess.SY2023★ 3 cited

Knowledge-Driven Resource Allocation for D2D Networks: A WMMSE Unrolled Graph Neural Network Approach

Hao Yang, Nan Cheng, Ruijin Sun +5

This paper proposes an novel knowledge-driven approach for resource allocation in device-to-device (D2D) networks using a graph neural network (GNN) architecture. To meet the milli…

eess.SY2023

Scalable Resource Management for Dynamic MEC: An Unsupervised Link-Output Graph Neural Network Approach

Xiucheng Wang, Nan Cheng, Lianhao Fu +5

Deep learning has been successfully adopted in mobile edge computing (MEC) to optimize task offloading and resource allocation. However, the dynamics of edge networks raise two cha…

cs.HC2023★ 2 cited

A Unified Framework for Integrating Semantic Communication and AI-Generated Content in Metaverse

Yijing Lin, Zhipeng Gao, Hongyang Du +4

As the Metaverse continues to grow, the need for efficient communication and intelligent content generation becomes increasingly important. Semantic communication focuses on convey…

cs.HC2023★ 6 cited

Realizing Immersive Communications in Human Digital Twin by Edge Computing Empowered Tactile Internet: Visions and Case Study

Hao Xiang, Changyan Yi, Kun Wu +5

Human digital twin (HDT) is expected to revolutionize the future human lifestyle and prompts the development of advanced human-centric applications (e.g., Metaverse) by bridging ph…

cs.AI2023

Guiding AI-Generated Digital Content with Wireless Perception

Jiacheng Wang, Hongyang Du, Dusit Niyato +5

Recent advances in artificial intelligence (AI), coupled with a surge in training data, have led to the widespread use of AI for digital content generation, with ChatGPT serving as…

cs.CR2023

Blockchain-Empowered Lifecycle Management for AI-Generated Content (AIGC) Products in Edge Networks

Yinqiu Liu, Hongyang Du, Dusit Niyato +6

The rapid development of Artificial IntelligenceGenerated Content (AIGC) has brought daunting challenges regarding service latency, security, and trustworthiness. Recently, researc…

cs.NI2023★ 56 cited

Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services

Minrui Xu, Hongyang Du, Dusit Niyato +9

Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This s…

eess.SP2023

On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds

Cheng-Xiang Wang, Xiaohu You, Xiqi Gao +16

Fifth generation (5G) mobile communication systems have entered the stage of commercial development, providing users with new services and improved user experiences as well as offe…

cs.AI2023★ 3 cited

Semantic Information Marketing in The Metaverse: A Learning-Based Contract Theory Framework

Ismail Lotfi, Dusit Niyato, Sumei Sun +2

In this paper, we address the problem of designing incentive mechanisms by a virtual service provider (VSP) to hire sensing IoT devices to sell their sensing data to help creating…

cs.AI2023★ 45 cited

Enabling AI-Generated Content (AIGC) Services in Wireless Edge Networks

Hongyang Du, Zonghang Li, Dusit Niyato +5

Artificial Intelligence-Generated Content (AIGC) refers to the use of AI to automate the information creation process while fulfilling the personalized requirements of users. Howev…

cs.NI2023★ 247 cited

Networking Architecture and Key Supporting Technologies for Human Digital Twin in Personalized Healthcare: A Comprehensive Survey

Jiayuan Chen, Changyan Yi, Samuel D. Okegbile +3

Digital twin (DT), refers to a promising technique to digitally and accurately represent actual physical entities. One typical advantage of DT is that it can be used to not only vi…