190 citations · 617 across the 19 of their papers we have counts for
19 papers
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…
Deep Reinforcement Learning Based Power Allocation for Minimizing AoI and Energy Consumption in MIMO-NOMA IoT Systems
Hongbiao Zhu, Qiong Wu, Qiang Fan +3
Multi-input multi-out and non-orthogonal multiple access (MIMO-NOMA) internet-of-things (IoT) systems can improve channel capacity and spectrum efficiency distinctly to support the…
Asynchronous Federated Learning Based Mobility-aware Caching in Vehicular Edge Computing
Wenhua Wang, Yu Zhao, Qiong Wu +3
Vehicular edge computing (VEC) is a promising technology to support real-time applications through caching the contents in the roadside units (RSUs), thus vehicles can fetch the co…
Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning
Qiong Wu, Yu Zhao, Qiang Fan +3
The vehicular edge computing (VEC) can cache contents in different RSUs at the network edge to support the real-time vehicular applications. In VEC, owing to the high-mobility char…
Time-Dependent Performance Modeling for Platooning Communications at Intersection
Qiong Wu, Yu Zhao, Qiang Fan
With the development of internet of vehicles, platooning strategy has been widely studied as the potential approach to ensure the safety of autonomous driving. Vehicles in the form…
Asynchronous Federated Learning for Edge-assisted Vehicular Networks
Siyuan Wang, Qiong Wu, Qiang Fan +2
Vehicular networks enable vehicles support real-time vehicular applications through training data. Due to the limited computing capability, vehicles usually transmit data to a road…
High stable and accurate vehicle selection scheme based on federated edge learning in vehicular networks
Qiong Wu, Xiaobo Wang, Qiang Fan +3
Federated edge learning (FEEL) technology for vehicular networks is considered as a promising technology to reduce the computation workload while keeping the privacy of users. In t…
Towards V2I Age-aware Fairness Access: A DQN Based Intelligent Vehicular Node Training and Test Method
Qiong Wu, Shuai Shi, Ziyang Wan +3
Vehicles on the road exchange data with base station (BS) frequently through vehicle to infrastructure (V2I) communications to ensure the normal use of vehicular applications, wher…
Optimizing Number, Placement, and Backhaul Connectivity of Multi-UAV Networks
Javad Sabzehali, Vijay K. Shah, Qiang Fan +3
Multi-Unmanned Aerial Vehicle (UAV) Networks is a promising solution to providing wireless coverage to ground users in challenging rural areas (such as Internet of Things (IoT) dev…
Velocity-adaptive Access Scheme for MEC-assisted Platooning Networks: Access Fairness Via Data Freshness
Qiong Wu, Ziyang Wan, Qiang Fan +2
Platooning strategy is an important part of autonomous driving technology. Due to the limited resource of autonomous vehicles in platoons, mobile edge computing (MEC) is usually us…
Decentralized Power Allocation for MIMO-NOMA Vehicular Edge Computing Based on Deep Reinforcement Learning
Hongbiao Zhu, Qiong Wu, Xiaojun Wu +3
Vehicular edge computing (VEC) is envisioned as a promising approach to process the explosive computation tasks of vehicular user (VU). In the VEC system, each VU allocates power t…
Differential Privacy Meets Federated Learning under Communication Constraints
Nima Mohammadi, Jianan Bai, Qiang Fan +3
The performance of federated learning systems is bottlenecked by communication costs and training variance. The communication overhead problem is usually addressed by three communi…
Delay Sensitive Task Offloading in the 802.11p Based Vehicular Fog Computing Systems
Qiong Wu, Hanxu Liu, Ruhai Wang +3
Vehicular fog computing (VFC) is envisioned as a promising solution to process the explosive tasks in autonomous vehicular networks. In the VFC system, task offloading is the key t…
Time-dependent Performance Analysis of the 802.11p-based Platooning Communications Under Disturbance
Qiong Wu, Hongmei Ge, Pingyi Fan +3
Platooning is a critical technology to realize autonomous driving. Each vehicle in platoons adopts the IEEE 802.11p standard to exchange information through communications to maint…
Federated Learning in Mobile Edge Computing: An Edge-Learning Perspective for Beyond 5G
Shashank Jere, Qiang Fan, Bodong Shang +2
Owing to the large volume of sensed data from the enormous number of IoT devices in operation today, centralized machine learning algorithms operating on such data incur an unbeara…
Delay-aware Resource Allocation in Fog-assisted IoT Networks Through Reinforcement Learning
Qiang Fan, Jianan Bai, Hongxia Zhang +2
Fog nodes in the vicinity of IoT devices are promising to provision low latency services by offloading tasks from IoT devices to them. Mobile IoT is composed by mobile IoT devices…
Resource Allocation in Dynamic TDD Heterogeneous Networks under Mixed Traffic
Qiang Fan, Hancheng Lu, Peilin Hong +1
Recently, Dynamic Time Division Duplex (TDD) has been proposed to handle the asymmetry of traffic demand between DownLink (DL) and UpLink (UL) in Heterogeneous Networks (HetNets).…
Load Coupling Power Optimization in Cloud Radio Access Networks
Qiang Fan, Hancheng Lu, Wei Jiang +3
Recently, Cloud-based Radio Access Network (C-RAN) has been proposed as a potential solution to reduce energy cost in cellular networks. C-RAN centralizes the baseband processing c…
Green Energy Aware Avatar Migration Strategy in Green Cloudlet Networks
Xiang Sun, Nirwan Ansari, Qiang Fan
We propose a Green Cloudlet Network (\emph{GCN}) architecture to provide seamless Mobile Cloud Computing (\emph{MCC}) services to User Equipments (\emph{UE}s) with low latency in w…