Publications (42)
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…
Predicted high-temperature superconductivity in rare earth hydride ErH2 at moderate pressure
Yiding Liu, Qiang Fan, Jianhui Yang +3
Hydrides offer an opportunity to study high-temperature (Tc) superconductivity at experimentally achievable pressures. However, they remained extremely high. Using density function…
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…
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…
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).…
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…
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…
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…
CS-Agent: LLM-based Community Search via Dual-agent Collaboration
Jiahao Hua, Long Yuan, Qingshuai Feng +2
Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks, yet their application to graph structure analysis, particularly in comm…
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…
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…
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.…
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 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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
Content-Aware User Association and Multi-User MIMO Beamforming over Mobile Edge Caching
Susanna Mosleh, Qiang Fan, Lingjia Liu +3
Mobile edge caching (MEC) has been introduced to support ever-growing end-users' needs. To reduce the backhaul traffic demand and content delivery latency, cache-enabled edge serve…
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…
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'…
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…
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…
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…
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…
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…
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…
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…
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…
Emergence of half-semiconductor behavior and tunable magnetism via carrier doping in Janus VXSe (X=Cl, Br, I) monolayers
Zhixiang Wang, Aining Wang, Zikui Ye +5
Two-dimensional ferromagnetic semiconductors with high Curie temperature, large magnetic anisotropy, and electrically tunable properties are highly sought for nanoscale spintronics…
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…
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 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…
Electrically tunable magnetism and unique intralayer charge transfer in Janus monolayer MnSSe for spintronics applications
Yu Chen, Qiang Fan, Yiding Liu +1
Controlling magnetism and electronic properties of two-dimensional (2D) materials by purely electrical means is crucial and highly sought for high-efficiency spintronics devices si…
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…
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…