124 citations · 297 across the 10 of their papers we have counts for
10 papers
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…
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…
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…
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…
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…
A Swarming Approach to Optimize the One-hop Delay in Smart Driving Inter-platoon Communications
Qiong Wu, Shuzhen Nie, Pingyi Fan +3
In this paper, we propose a swarming approach and optimize the one-hop delay for interplatoon communications through adjusting the minimum contention window size of each backbone v…