4 citations · 16 across the 17 of their papers we have counts for
17 papers
A Unified Successive Approximation Framework for General Coupled Multiplicative and Fractional Optimization: HM-GM-AM-QM Transforms and Applications
Liangxin Qian, Wenhan Yu, Peiyuan Si +4
Optimization problems in communication networks and information systems often contain coupled multiplicative or fractional terms, such as sum-of-products, sum-of-ratios, and logari…
PrivTuner with Homomorphic Encryption and LoRA: A P3EFT Scheme for Privacy-Preserving Parameter-Efficient Fine-Tuning of AI Foundation Models
Yang Li, Wenhan Yu, Jun Zhao
AI foundation models have recently demonstrated impressive capabilities across a wide range of tasks. Fine-tuning (FT) is a method of customizing a pre-trained AI foundation model…
Mobile Edge Computing and AI Enabled Web3 Metaverse over 6G Wireless Communications: A Deep Reinforcement Learning Approach
Wenhan Yu, Terence Jie Chua, Jun Zhao
The Metaverse is gaining attention among academics as maturing technologies empower the promises and envisagements of a multi-purpose, integrated virtual environment. An interactiv…
Orchestration of Emulator Assisted Mobile Edge Tuning for AI Foundation Models: A Multi-Agent Deep Reinforcement Learning Approach
Wenhan Yu, Terence Jie Chua, Jun Zhao
The efficient deployment and fine-tuning of foundation models are pivotal in contemporary artificial intelligence. In this study, we present a groundbreaking paradigm integrating M…
FedPEAT: Convergence of Federated Learning, Parameter-Efficient Fine Tuning, and Emulator Assisted Tuning for Artificial Intelligence Foundation Models with Mobile Edge Computing
Terence Jie Chua, Wenhan Yu, Jun Zhao +1
The emergence of foundation models, including language and vision models, has reshaped AI's landscape, offering capabilities across various applications. Deploying and fine-tuning…
Heterogeneous 360 Degree Videos in Metaverse: Differentiated Reinforcement Learning Approaches
Wenhan Yu, Jun Zhao
Advanced video technologies are driving the development of the futuristic Metaverse, which aims to connect users from anywhere and anytime. As such, the use cases for users will be…
Semantic communications, semantic edge computing, and semantic caching
Wenhan Yu, Jun Zhao
The increasing popularity of applications like the Metaverse has led to the exploration of new, more effective ways of communication. Semantic communication, which focuses on the m…
Human-Centric Resource Allocation in the Metaverse over Wireless Communications
Jun Zhao, Liangxin Qian, Wenhan Yu
The Metaverse will provide numerous immersive applications for human users, by consolidating technologies like extended reality (XR), video streaming, and cellular networks. Optimi…
Detection of Uncertainty in Exceedance of Threshold (DUET): An Adversarial Patch Localizer
Terence Jie Chua, Wenhan Yu, Jun Zhao
Development of defenses against physical world attacks such as adversarial patches is gaining traction within the research community. We contribute to the field of adversarial patc…
Mobile Edge Adversarial Detection for Digital Twinning to the Metaverse with Deep Reinforcement Learning
Terence Jie Chua, Wenhan Yu, Jun Zhao
Real-time Digital Twinning of physical world scenes onto the Metaverse is necessary for a myriad of applications such as augmented-reality (AR) assisted driving. In AR assisted dri…
Virtual Reality in Metaverse over Wireless Networks with User-centered Deep Reinforcement Learning
Wenhan Yu, Terence Jie Chua, Jun Zhao
The Metaverse and its promises are fast becoming reality as maturing technologies are empowering the different facets. One of the highlights of the Metaverse is that it offers the…
Play to Earn in the Metaverse with Mobile Edge Computing over Wireless Networks: A Deep Reinforcement Learning Approach
Terence Jie Chua, Wenhan Yu, Jun Zhao
The Metaverse play-to-earn games have been gaining popularity as they enable players to earn in-game tokens which can be translated to real-world profits. With the advancements in…
User-centric Heterogeneous-action Deep Reinforcement Learning for Virtual Reality in the Metaverse over Wireless Networks
Wenhan Yu, Terence Jie Chua, Jun Zhao
The Metaverse is emerging as maturing technologies are empowering the different facets. Virtual Reality (VR) technologies serve as the backbone of the virtual universe within the M…
UAV aided Metaverse over Wireless Communications: A Reinforcement Learning Approach
Peiyuan Si, Wenhan Yu, Jun Zhao +2
Metaverse is expected to create a virtual world closely connected with reality to provide users with immersive experience with the support of 5G high data rate communication techni…
Unified, User and Task (UUT) Centered Artificial Intelligence for Metaverse Edge Computing
Terence Jie Chua, Wenhan Yu, Jun Zhao
The Metaverse can be considered the extension of the present-day web, which integrates the physical and virtual worlds, delivering hyper-realistic user experiences. The inception o…
Asynchronous Hybrid Reinforcement Learning for Latency and Reliability Optimization in the Metaverse over Wireless Communications
Wenhan Yu, Terence Jie Chua, Jun Zhao
Technology advancements in wireless communications and high-performance Extended Reality (XR) have empowered the developments of the Metaverse. The demand for the Metaverse applica…
Resource Allocation for Mobile Metaverse with the Internet of Vehicles over 6G Wireless Communications: A Deep Reinforcement Learning Approach
Terence Jie Chua, Wenhan Yu, Jun Zhao
Improving the interactivity and interconnectivity between people is one of the highlights of the Metaverse. The Metaverse relies on a core approach, digital twinning, which is a me…