13 citations · 88 across the 51 of their papers we have counts for
51 papers
Towards Intelligent Transportation with Pedestrians and Vehicles In-the-Loop: A Surveillance Video-Assisted Federated Digital Twin Framework
Xiaolong Li, Jianhao Wei, Haidong Wang +7
In intelligent transportation systems (ITSs), incorporating pedestrians and vehicles in-the-loop is crucial for developing realistic and safe traffic management solutions. However,…
AoI-aware Sensing Scheduling and Trajectory Optimization for Multi-UAV-assisted Wireless Backscatter Networks
Yusi Long, Songhan Zhao, Shimin Gong +4
This paper considers multiple unmanned aerial vehicles (UAVs) to assist sensing data transmissions from the ground users (GUs) to a remote base station (BS). Each UAV collects sens…
Integration of Mixture of Experts and Multimodal Generative AI in Internet of Vehicles: A Survey
Minrui Xu, Dusit Niyato, Jiawen Kang +6
Generative AI (GAI) can enhance the cognitive, reasoning, and planning capabilities of intelligent modules in the Internet of Vehicles (IoV) by synthesizing augmented datasets, com…
Resource Slicing with Cross-Cell Coordination in Satellite-Terrestrial Integrated Networks
Mingcheng He, Huaqing Wu, Conghao Zhou +2
Satellite-terrestrial integrated networks (STIN) are envisioned as a promising architecture for ubiquitous network connections to support diversified services. In this paper, we pr…
ProSecutor: Protecting Mobile AIGC Services on Two-Layer Blockchain via Reputation and Contract Theoretic Approaches
Yinqiu Liu, Hongyang Du, Dusit Niyato +5
Mobile AI-Generated Content (AIGC) has achieved great attention in unleashing the power of generative AI and scaling the AIGC services. By employing numerous Mobile AIGC Service Pr…
Graph Neural Network Meets Multi-Agent Reinforcement Learning: Fundamentals, Applications, and Future Directions
Ziheng Liu, Jiayi Zhang, Enyu Shi +5
Multi-agent reinforcement learning (MARL) has become a fundamental component of next-generation wireless communication systems. Theoretically, although MARL has the advantages of l…
Collaborative Computing in Non-Terrestrial Networks: A Multi-Time-Scale Deep Reinforcement Learning Approach
Yang Cao, Shao-Yu Lien, Ying-Chang Liang +3
Constructing earth-fixed cells with low-earth orbit (LEO) satellites in non-terrestrial networks (NTNs) has been the most promising paradigm to enable global coverage. The limited…
Collaborative Deep Reinforcement Learning for Resource Optimization in Non-Terrestrial Networks
Yang Cao, Shao-Yu Lien, Ying-Chang Liang +3
Non-terrestrial networks (NTNs) with low-earth orbit (LEO) satellites have been regarded as promising remedies to support global ubiquitous wireless services. Due to the rapid mobi…
Dynamic Human Digital Twin Deployment at the Edge for Task Execution: A Two-Timescale Accuracy-Aware Online Optimization
Yuye Yang, You Shi, Changyan Yi +5
Human digital twin (HDT) is an emerging paradigm that bridges physical twins (PTs) with powerful virtual twins (VTs) for assisting complex task executions in human-centric services…
Channel-Feedback-Free Transmission for Downlink FD-RAN: A Radio Map based Complex-valued Precoding Network Approach
Jiwei Zhao, Jiacheng Chen, Zeyu Sun +4
As the demand for high-quality services proliferates, an innovative network architecture, the fully-decoupled RAN (FD-RAN), has emerged for more flexible spectrum resource utilizat…
Digital Twin-Based User-Centric Edge Continual Learning in Integrated Sensing and Communication
Shisheng Hu, Jie Gao, Xinyu Huang +5
In this paper, we propose a digital twin (DT)-based user-centric approach for processing sensing data in an integrated sensing and communication (ISAC) system with high accuracy an…
Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part I: Communication-Aware Vehicle Control
Tong Liu, Lei Lei, Kan Zheng +2
An intelligent decision-making system enabled by Vehicle-to-Everything (V2X) communications is essential to achieve safe and efficient autonomous driving (AD), where two types of d…
Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part II: Control-Aware Radio Resource Allocation
Lei Lei, Tong Liu, Kan Zheng +2
In Part I of this two-part paper (Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part I: Communication-Aware Vehicle Control), we decomposed the mul…
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…
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…
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…
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…
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…
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…
Beef up mmWave Dense Cellular Networks with D2D-Assisted Cooperative Edge Caching
Wen Wu, Ning Zhang, Nan Cheng +4
Edge caching is emerging as the most promising solution to reduce the content retrieval delay and relieve the huge burden on the backhaul links in the ultra-dense networks by proac…
Performance Analysis and Enhancement of Beamforming Training in 802.11ad
Wen Wu, Nan Cheng, Ning Zhang +4
Beamforming (BF) training is crucial to establishing reliable millimeter-wave communication connections between stations (STAs) and an access point. In IEEE 802.11ad BF training pr…
Model-Driven Deep Learning for Non-Coherent Massive Machine-Type Communications
Zhe Ma, Wen Wu, Feifei Gao +2
In this paper, we investigate the joint device activity and data detection in massive machine-type communications (mMTC) with a one-phase non-coherent scheme, where data bits are e…
Accuracy-Guaranteed Collaborative DNN Inference in Industrial IoT via Deep Reinforcement Learning
Wen Wu, Peng Yang, Weiting Zhang +3
Collaboration among industrial Internet of Things (IoT) devices and edge networks is essential to support computation-intensive deep neural network (DNN) inference services which r…
Spectral Efficiency Analysis of Uplink-Downlink Decoupled Access in C-V2X Networks
Luofang Jiao, Kai Yu, Yunting Xu +4
The uplink (UL)/downlink (DL) decoupled access has been emerging as a novel access architecture to improve the performance gains in cellular networks. In this paper, we investigate…