190 citations · 202 across the 22 of their papers we have counts for
12 papers · 1 filter
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…
Enhanced SPS Velocity-adaptive Scheme: Access Fairness in 5G NR V2I Networks
Xiao Xu, Qiong Wu, Pingyi Fan +1
Vehicle-to-Infrastructure (V2I) technology enables information exchange between vehicles and road infrastructure. Specifically, when a vehicle approaches a roadside unit (RSU), it…
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…
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…
Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning
Wenhua Wang, Qiong Wu, Pingyi Fan +4
With the rapid development of intelligent vehicles and Intelligent Transport Systems (ITS), the sensors such as cameras and LiDAR installed on intelligent vehicles provides higher…
Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networks
Kangwei Qi, Qiong Wu, Pingyi Fan +4
Reconfigurable Intelligent Surface (RIS) is a pivotal technology in communication, offering an alternative path that significantly enhances the link quality in wireless communicati…
Semantic-Aware Spectrum Sharing in Internet of Vehicles Based on Deep Reinforcement Learning
Zhiyu Shao, Qiong Wu, Pingyi Fan +4
This work aims to investigate semantic communication in high-speed mobile Internet of vehicles (IoV) environments, with a focus on the spectrum sharing between vehicle-to-vehicle (…
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…
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'…
Deep Reinforcement Learning Based Vehicle Selection for Asynchronous Federated Learning Enabled Vehicular Edge Computing
Qiong Wu, Siyuan Wang, Pingyi Fan +1
In the traditional vehicular network, computing tasks generated by the vehicles are usually uploaded to the cloud for processing. However, since task offloading toward the cloud wi…