3 citations · 8 across the 19 of their papers we have counts for
19 papers
PPO-Based Hybrid Optimization for RIS-Assisted Semantic Vehicular Edge Computing
Wei Feng, Jingbo Zhang, Qiong Wu +2
To support latency-sensitive Internet of Vehicles (IoV) applications amidst dynamic environments and intermittent links, this paper proposes a Reconfigurable Intelligent Surface (R…
Optimizing System Latency for Blockchain-Encrypted Edge Computing in Internet of Vehicles
Cui Zhang, Maoxin Ji, Qiong Wu +2
As Internet of Vehicles (IoV) technology continues to advance, edge computing has become an important tool for assisting vehicles in handling complex tasks. However, the process of…
A Survey on Semantic Communications in Internet of Vehicles
Sha Ye, Qiong Wu, Pingyi Fan +1
Internet of Vehicles (IoV), as the core of intelligent transportation system, enables comprehensive interconnection between vehicles and their surroundings through multiple communi…
Optimizing Age of Information in Internet of Vehicles Over Error-Prone Channels
Cui Zhang, Maoxin Ji, Qiong Wu +2
In the Internet of Vehicles (IoV), Age of Information (AoI) has become a vital performance metric for evaluating the freshness of information in communication systems. Although man…
Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learning
Wenjun Zhang, Qiong Wu, Pingyi Fan +4
Semantic communication transmits the extracted features of information rather than raw data, significantly reducing redundancy, which is crucial for addressing spectrum and energy…
A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learning
Jingbo Zhang, Qiong Wu, Pingyi Fan +1
Federated Edge Learning (FEL), an emerging distributed Machine Learning (ML) paradigm, enables model training in a distributed environment while ensuring user privacy by using phys…
DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoV
Xueying Gu, Qiong Wu, Pingyi Fan +4
In the Internet of Vehicles (IoV), Federated Learning (FL) provides a privacy-preserving solution by aggregating local models without sharing data. Traditional supervised learning…
Mobility-Aware Federated Self-supervised Learning in Vehicular Network
Xueying Gu, Qiong Wu, Pingyi Fan +1
Federated Learning (FL) is an advanced distributed machine learning approach, that protects the privacy of each vehicle by allowing the model to be trained on multiple devices simu…
Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing
Cui Zhang, Wenjun Zhang, Qiong Wu +4
Federated Learning (FL) can protect the privacy of the vehicles in vehicle edge computing (VEC) to a certain extent through sharing the gradients of vehicles' local models instead…
Joint Optimization of Age of Information and Energy Consumption in NR-V2X System based on Deep Reinforcement Learning
Shulin Song, Zheng Zhang, Qiong Wu +2
Autonomous driving may be the most important application scenario of next generation, the development of wireless access technologies enabling reliable and low-latency vehicle comm…
Reconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement Learning
Kangwei Qi, Qiong Wu, Pingyi Fan +3
Vehicular edge computing (VEC) is an emerging technology that enables vehicles to perform high-intensity tasks by executing tasks locally or offloading them to nearby edge devices.…
Semantic-Aware Resource Allocation Based on Deep Reinforcement Learning for 5G-V2X HetNets
Zhiyu Shao, Qiong Wu, Pingyi Fan +3
This letter proposes a semantic-aware resource allocation (SARA) framework with flexible duty cycle (DC) coexistence mechanism (SARADC) for 5G-V2X Heterogeneous Network (HetNets) b…
Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing
Cui Zhang, Xiao Xu, Qiong Wu +4
In vehicle edge computing (VEC), asynchronous federated learning (AFL) is used, where the edge receives a local model and updates the global model, effectively reducing the global…
Blockchain-Enabled Variational Information Bottleneck for IoT Networks
Qiong Wu, Le Kuai, Pingyi Fan +3
In Internet of Things (IoT) networks, the amount of data sensed by user devices may be huge, resulting in the serious network congestion. To solve this problem, intelligent data co…
Vehicle Selection for C-V2X Mode 4 Based Federated Edge Learning Systems
Qiong Wu, Xiaobo Wang, Pingyi Fan +3
Federated learning (FL) is a promising technology for vehicular networks to protect vehicles' privacy in Internet of Vehicles (IoV). Vehicles with limited computation capacity may…
Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Network
Qiong Wu, Wenhua Wang, Pingyi Fan +3
Edge caching is a promising solution for next-generation networks by empowering caching units in small-cell base stations (SBSs), which allows user equipments (UEs) to fetch users'…
URLLC-Awared Resource Allocation for Heterogeneous Vehicular Edge Computing
Qiong Wu, Wenhua Wang, Pingyi Fan +3
Vehicular edge computing (VEC) is a promising technology to support real-time vehicular applications, where vehicles offload intensive computation tasks to the nearby VEC server fo…
Enhanced C-V2X Mode 4 to Optimize Age of Information and Reliability for IoV
Jiahou Chu, Qiong Wu, Qiang Fan +1
Internet of vehicles (IoV) has emerged as a key technology to realize real-time vehicular application. For IoV, vehicles adopt cellular vehicle-to-everything (C-V2X) standard to su…
Delay-sensitive Task Offloading in Vehicular Fog Computing-Assisted Platoons
Qiong Wu, Siyuan Wang, Hongmei Ge +3
Vehicles in platoons need to process many tasks to support various real-time vehicular applications. When a task arrives at a vehicle, the vehicle may not process the task due to i…