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Wei Ni - IEEE Xplore Author Profile
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Wei Ni

Also published under: W. Ni

Publications
344
Citations
6,365
Publications by Year
20092025
Co-Authors:
Show All Co-Authors (421)
Image of author Wei Ni

Wei Ni

Also published under: W. Ni

Affiliation

Data61
Commonwealth Scientific and Industrial Research Organization
Marsfield, NSW, Australia

Publication Topics

Biography

Wei Ni (Fellow, IEEE) received the B.E. and Ph.D. degrees in communication science and engineering from Fudan University, Shanghai, China, in 2000 and 2005, respectively. He is currently a Principal Research Scientist with CSIRO, Sydney, Australia; a Conjoint Professor with the University of New South Wales; an Adjunct Professor with the University of Technology Sydney; and a Honorary Professor with Macquarie University. His research interests include machine learning, online learning, and stochastic optimization, and their applications to the security, integrity, and efficiency of network systems. He has been an Editor of IEEE Transactions on Vehicular Technology since 2022, IEEE Transactions on Wireless Communications since 2018, and Rese...
Publications
344
Citations
6,365
Publications by Year
20092025
Co-Authors:
Show All Co-Authors (421)
Author's Published Works

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Proximal Policy Optimization With Adaptive Weight Adjustment for Airborne Integrated Sensing and Communication

Shihang Zhang;Chao Sun;Shuyan Hu;Wei Ni;Xin Wang

IEEE Wireless Communications Letters
Year: 2025 | Early Access Article |
Integrated sensing and communication (ISAC) technology has emerged as a potential enabler for efficient data transmission and sensing in unmanned aerial vehicle (UAV) networks. In this letter, we investigate a UAV-ISAC system, where a UAV furnishes downlink communication and simultaneous sensing for mobile ground nodes (GNs). To address this non-convex optimization problem and prevent retraining r...Show More

Proximal Policy Optimization With Adaptive Weight Adjustment for Airborne Integrated Sensing and Communication

Shihang Zhang;Chao Sun;Shuyan Hu;Wei Ni;Xin Wang

Year: 2025 | Early Access Article |

Energy-Efficient Resource Allocation for Multi-User Semantic Communications: A Deep Reinforcement Learning Approach

Xiaojing Chen;Jia Xu;Wei Ni;Shuyan Hu;Zhijin Qin;Shunqing Zhang

IEEE Wireless Communications Letters
Year: 2025 | Early Access Article |
Effective resource allocation is critical to the efficiency of semantic communications, especially when faced with limited energy supplies and constrained communication resources. The unique requirements of semantic communications have brought new challenges to the joint optimization of energy and communication resources. This paper presents a novel deep reinforcement learning (DRL)-based algorith...Show More

Energy-Efficient Resource Allocation for Multi-User Semantic Communications: A Deep Reinforcement Learning Approach

Xiaojing Chen;Jia Xu;Wei Ni;Shuyan Hu;Zhijin Qin;Shunqing Zhang

Year: 2025 | Early Access Article |

Edge Intelligence in Satellite-Terrestrial Networks with Hybrid Quantum Computing

Siyue Huang;Lifeng Wang;Xin Wang;Bo Tan;Wei Ni;Kai-Kit Wong

IEEE Wireless Communications Letters
Year: 2025 | Early Access Article |
This paper exploits the potential of edge intelligence empowered satellite-terrestrial networks, where users’ computation tasks are offloaded to the satellites or terrestrial base stations. The computation task offloading in such networks involves the edge cloud selection and bandwidth allocations for the access and backhaul links, which aims to minimize the energy consumption under the delay and ...Show More

Edge Intelligence in Satellite-Terrestrial Networks with Hybrid Quantum Computing

Siyue Huang;Lifeng Wang;Xin Wang;Bo Tan;Wei Ni;Kai-Kit Wong

Year: 2025 | Early Access Article |

Random Caching Strategy Based on Scalable Video Coding: Content Placement and Delivery in Multi-Tier Heterogeneous Networks

Xuewei Zhang;Yuan Ren;Junxuan Wang;Fan Jiang;Wei Ni;Abbas Jamalipour

IEEE Transactions on Communications
Year: 2025 | Early Access Article |
This paper proposes a new joint random caching and hierarchical transmission scheme for delivering multimedia content in cache-assisted heterogeneous networks. We use scalable video coding (SVC) and wireless edge caching to offer personalized video-watching services to end users. Resorting to stochastic geometry, we derive new expressions for successful transmission probabilities (STPs). Using the...Show More

Random Caching Strategy Based on Scalable Video Coding: Content Placement and Delivery in Multi-Tier Heterogeneous Networks

Xuewei Zhang;Yuan Ren;Junxuan Wang;Fan Jiang;Wei Ni;Abbas Jamalipour

Year: 2025 | Early Access Article |

A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoT

Kai Li;Yilei Liang;Xin Yuan;Wei Ni;Jon Crowcroft;Chau Yuen;Ozgur B. Akan

IEEE Networking Letters
Year: 2025 | Early Access Article |
This letter puts forth a new hybrid horizontal-vertical federated learning (HoVeFL) for mobile edge computing-enabled Internet of Things (EdgeIoT). In this framework, certain EdgeIoT devices train local models using the same data samples but analyze disparate data features, while the others focus on the same features using non-independent and identically distributed (non-IID) data samples. Thus, e...Show More

A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoT

Kai Li;Yilei Liang;Xin Yuan;Wei Ni;Jon Crowcroft;Chau Yuen;Ozgur B. Akan

Year: 2025 | Early Access Article |

CAN-Trace Attack: Exploit CAN Messages to Uncover Driving Trajectories

Xiaojie Lin;Baihe Ma;Xu Wang;Guangsheng Yu;Ying He;Wei Ni;Ren Ping Liu

IEEE Transactions on Intelligent Transportation Systems
Year: 2025 | Volume: 26, Issue: 3 | Journal Article |
Driving trajectory data remains vulnerable to privacy breaches despite existing mitigation measures. Traditional methods for detecting driving trajectories typically rely on map-matching the path using Global Positioning System (GPS) data, which is susceptible to GPS data outage. This paper introduces CAN-Trace, a novel privacy attack mechanism that leverages Controller Area Network (CAN) messages...Show More

CAN-Trace Attack: Exploit CAN Messages to Uncover Driving Trajectories

Xiaojie Lin;Baihe Ma;Xu Wang;Guangsheng Yu;Ying He;Wei Ni;Ren Ping Liu

Year: 2025 | Volume: 26, Issue: 3 | Journal Article |

Adaptive Network Function Chain Orchestration Strategy for 6G Inclusive Intelligent Services

Yi Wen;Sai Zou;Yanglong Sun;Wei Ni;Kai Li;Hongfeng Gao

IEEE Transactions on Cognitive Communications and Networking
Year: 2025 | Early Access Article |
In the upcoming 6G era, inclusive intelligent services(IISs) that rely on integrated communications and AI arithmetic will become the norm. These services require efficient distributed intelligent learning or reasoning. However, with the proliferation of complex applications, providing differentiated and customized services through effective network function chain (NFC) orchestration has become a ...Show More

Adaptive Network Function Chain Orchestration Strategy for 6G Inclusive Intelligent Services

Yi Wen;Sai Zou;Yanglong Sun;Wei Ni;Kai Li;Hongfeng Gao

Year: 2025 | Early Access Article |

Optimal Beamforming for MIMO DFRC Systems With Transmit Covariance Constraints

Chenhao Yang;Xin Wang;Wei Ni;Yi Jiang

IEEE Transactions on Signal Processing
Year: 2025 | Volume: 73 | Journal Article |
This paper optimizes the beamforming design of a downlink multiple-input multiple-output (MIMO) dual-function radar-communication (DFRC) system to maximize the weighted communication sum-rate under a prescribed transmit covariance constraint for radar performance guarantee. In the single-user case, we show that the transmit covariance constraint implies the existence of inherent orthogonality amon...Show More

Optimal Beamforming for MIMO DFRC Systems With Transmit Covariance Constraints

Chenhao Yang;Xin Wang;Wei Ni;Yi Jiang

Year: 2025 | Volume: 73 | Journal Article |

Recent Estimation Techniques of Vehicle-Road-Pedestrian States for Traffic Safety: Comprehensive Review and Future Perspectives

Cheng Tian;Chao Huang;Yan Wang;Edward Chung;Anh-Tu Nguyen;Pak Kin Wong;Wei Ni;Abbas Jamalipour;Kai Li;Hailong Huang

IEEE Transactions on Intelligent Transportation Systems
Year: 2025 | Volume: 26, Issue: 3 | Journal Article |
Accurate and real-time acquisition of vehicular system dynamic states, road surface conditions, and motion states of surrounding participants is crucial for the safety, passenger comfort, and operational efficiency of autonomous vehicles (AVs) and connected automated vehicles (CAVs). In recent years, a significant amount of research has contributed to the field of state estimation for vehicles, ro...Show More

Recent Estimation Techniques of Vehicle-Road-Pedestrian States for Traffic Safety: Comprehensive Review and Future Perspectives

Cheng Tian;Chao Huang;Yan Wang;Edward Chung;Anh-Tu Nguyen;Pak Kin Wong;Wei Ni;Abbas Jamalipour;Kai Li;Hailong Huang

Year: 2025 | Volume: 26, Issue: 3 | Journal Article |

Biasing Federated Learning With a New Adversarial Graph Attention Network

Kai Li;Jingjing Zheng;Wei Ni;Hailong Huang;Pietro Liò;Falko Dressler;Ozgur B. Akan

IEEE Transactions on Mobile Computing
Year: 2025 | Volume: 24, Issue: 3 | Journal Article |
Fairness in Federated Learning (FL) is imperative not only for the ethical utilization of technology but also for ensuring that models provide accurate, equitable, and beneficial outcomes across varied user demographics and equipment. This paper proposes a new adversarial architecture, referred to as Adversarial Graph Attention Network (AGAT), which deliberately instigates fairness attacks with an...Show More

Biasing Federated Learning With a New Adversarial Graph Attention Network

Kai Li;Jingjing Zheng;Wei Ni;Hailong Huang;Pietro Liò;Falko Dressler;Ozgur B. Akan

Year: 2025 | Volume: 24, Issue: 3 | Journal Article |

Hybrid NOMA Offloading for Delay-Sensitive Applications in MEC-Based NB-IoT Networks

Fang Liu;Minxin Wang;Yuanan Liu;Wei Ni;Abbas Jamalipour

IEEE Internet of Things Journal
Year: 2025 | Volume: 12, Issue: 5 | Journal Article |
Data traffic has grown exponentially with the rapid development of Narrowband Internet of Things (NB-IoT) technology. Nonorthogonal multiple access (NOMA) and mobile edge computing (MEC) are essential technologies to enhance the performance of NB-IoT networks. This article proposes a new hybrid NOMA offloading strategy, allowing an Internet of Things (IoT) device to execute NOMA with other devices...Show More

Hybrid NOMA Offloading for Delay-Sensitive Applications in MEC-Based NB-IoT Networks

Fang Liu;Minxin Wang;Yuanan Liu;Wei Ni;Abbas Jamalipour

Year: 2025 | Volume: 12, Issue: 5 | Journal Article |

Reconfigurable Intelligent Surface-Assisted Wireless Federated Learning With Imperfect Aggregation

Pengcheng Sun;Erwu Liu;Wei Ni;Rui Wang;Zhe Xing;Bofeng Li;Abbas Jamalipour

IEEE Transactions on Communications
Year: 2025 | Volume: 73, Issue: 2 | Journal Article |
This paper proposes a new Signal-to-interference-plus-noise ratio (SINR)-based Device selection, Power control, and Reconfigurable intelligent surface (RIS) configuration (SDPR) algorithm, which allows imperfect aggregation of wireless federated learning (FL) in RIS-assisted Non-Orthogonal Multiple Access (NOMA) systems. The SDPR algorithm selects the local models with SINRs within an acceptable r...Show More

Reconfigurable Intelligent Surface-Assisted Wireless Federated Learning With Imperfect Aggregation

Pengcheng Sun;Erwu Liu;Wei Ni;Rui Wang;Zhe Xing;Bofeng Li;Abbas Jamalipour

Year: 2025 | Volume: 73, Issue: 2 | Journal Article |

Pricing Design for EV Platoon Charging Network With Hybrid Traffic Flows

Liwan Qi;Lifeng Wang;Jie Ding;Wei Ni

IEEE Transactions on Transportation Electrification
Year: 2025 | Volume: 11, Issue: 1 | Journal Article |
This article concentrates on the pricing design in electric vehicle (EV) platoon charging networks with hybrid traffic flows, where there exist different sizes of EV platoons, and charging stations have different amounts of available renewable energy. Two pricing schemes are proposed in terms of behavior awareness and congestion awareness to strike a balance between network revenue, renewable ener...Show More

Pricing Design for EV Platoon Charging Network With Hybrid Traffic Flows

Liwan Qi;Lifeng Wang;Jie Ding;Wei Ni

Year: 2025 | Volume: 11, Issue: 1 | Journal Article |

Leverage Variational Graph Representation for Model Poisoning on Federated Learning

Kai Li;Xin Yuan;Jingjing Zheng;Wei Ni;Falko Dressler;Abbas Jamalipour

IEEE Transactions on Neural Networks and Learning Systems
Year: 2025 | Volume: 36, Issue: 1 | Journal Article |
Cited by: Papers (2)
This article puts forth a new training data-untethered model poisoning (MP) attack on federated learning (FL). The new MP attack extends an adversarial variational graph autoencoder (VGAE) to create malicious local models based solely on the benign local models overheard without any access to the training data of FL. Such an advancement leads to the VGAE-MP attack that is not only efficacious but ...Show More

Leverage Variational Graph Representation for Model Poisoning on Federated Learning

Kai Li;Xin Yuan;Jingjing Zheng;Wei Ni;Falko Dressler;Abbas Jamalipour

Year: 2025 | Volume: 36, Issue: 1 | Journal Article |

IronForge: An Open, Secure, Fair, Decentralized Federated Learning

Guangsheng Yu;Xu Wang;Caijun Sun;Qin Wang;Ping Yu;Wei Ni;Ren Ping Liu

IEEE Transactions on Neural Networks and Learning Systems
Year: 2025 | Volume: 36, Issue: 1 | Journal Article |
Cited by: Papers (8)
Federated learning (FL) offers an effective learning architecture to protect data privacy in a distributed manner. However, the inevitable network asynchrony, overdependence on a central coordinator, and lack of an open and fair incentive mechanism collectively hinder FL’s further development. We propose IronForge, a new generation of FL framework, that features a directed acyclic graph (DAG)-base...Show More

IronForge: An Open, Secure, Fair, Decentralized Federated Learning

Guangsheng Yu;Xu Wang;Caijun Sun;Qin Wang;Ping Yu;Wei Ni;Ren Ping Liu

Year: 2025 | Volume: 36, Issue: 1 | Journal Article |

Privacy-Preserving Multi-Agent Deep Reinforcement Learning for Effective Resource Auction in Multi-Access Edge Computing

Feiran You;Xin Yuan;Wei Ni;Abbas Jamalipour

IEEE Transactions on Cognitive Communications and Networking
Year: 2024 | Early Access Article |
Multi-access edge computing (MEC) offloads services for mobile users to facilitate the integration of idle cloudlet resources and bring cloud services closer to users. Existing studies have focused primarily on task coordination and resource allocation with strict time constraints, and typically overlooked the potential privacy leakage of users’ participation strategies in MEC. This paper proposes...Show More

Privacy-Preserving Multi-Agent Deep Reinforcement Learning for Effective Resource Auction in Multi-Access Edge Computing

Feiran You;Xin Yuan;Wei Ni;Abbas Jamalipour

Year: 2024 | Early Access Article |

Over-the-Air Federated Learning in User-Centric Cell-Free Networks

Yingping Cui;Tiejun Lv;Weicai Li;Wei Ni;Ekram Hossain

IEEE Wireless Communications Letters
Year: 2024 | Volume: 13, Issue: 12 | Journal Article |
This letter presents a new over-the-air federated learning (OTA-FL) system supported by a user-centric cell-free (UCCF) network. We propose a two-level hierarchical deep reinforcement learning (HDRL) framework that minimizes mean squared error (MSE) derived from convergence analysis by jointly optimizing AP-device association (ADA) and power control (PC). Specifically, a soft actor-critic (SAC) wi...Show More

Over-the-Air Federated Learning in User-Centric Cell-Free Networks

Yingping Cui;Tiejun Lv;Weicai Li;Wei Ni;Ekram Hossain

Year: 2024 | Volume: 13, Issue: 12 | Journal Article |

TLSCG: Transfer Learning-Based Efficient Anomalous Smart Contract Generation to Empower Unknown Vulnerability Detection

Chunhong Liu;Zihang Sang;Li Duan;Wei Ni;Wei Wang;Chao Li

2024 IEEE International Conference on Web Services (ICWS)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Security vulnerabilities in smart contracts can have serious economic consequences. Existing smart contract vulnerability detection methods rely primarily on strict rules defined by experts, making current research limited to detecting specific known vulnerabilities and difficult to deal with other types of anomalous contracts (i.e., the variants of contracts with potentially known vulnerabilities...Show More

TLSCG: Transfer Learning-Based Efficient Anomalous Smart Contract Generation to Empower Unknown Vulnerability Detection

Chunhong Liu;Zihang Sang;Li Duan;Wei Ni;Wei Wang;Chao Li

Year: 2024 | Conference Paper |

A Secure and Lightweight Aggregation Method for Blockchain-based Distributed Federated Learning

He Huang;Li Duan;Chao Li;Wei Ni

2024 IEEE International Conference on Web Services (ICWS)
Year: 2024 | Conference Paper |
Federated learning (FL), typically coordinated by a centralized server, faces the risk of a single-point failure during the model aggregation process. Moreover, existing privacy protection methods for FL model parameters, such as homomorphic encryption (HE) and differential privacy (DP), still suffer from high computational costs or reduced model availability. Malicious trainers can also transmit ...Show More

A Secure and Lightweight Aggregation Method for Blockchain-based Distributed Federated Learning

He Huang;Li Duan;Chao Li;Wei Ni

Year: 2024 | Conference Paper |

Minimizing Adversarial Training Samples for Robust Image Classifiers: Analysis and Adversarial Example Generator Design

Yulong Wang;Tong Sun;Xin Yuan;Shenghong Li;Wei Ni

IEEE Transactions on Information Forensics and Security
Year: 2024 | Volume: 19 | Journal Article |
Training deep neural networks (DNNs) with altered data, known as adversarial training, is essential for improving their robustness. A significant challenge emerges as the robustness strengthened during training often diminishes during inference, resulting in drops in robust pronounced accuracy. Contemporary strategies either necessitate excessively large training data or risk compromising the natu...Show More

Minimizing Adversarial Training Samples for Robust Image Classifiers: Analysis and Adversarial Example Generator Design

Yulong Wang;Tong Sun;Xin Yuan;Shenghong Li;Wei Ni

Year: 2024 | Volume: 19 | Journal Article |

Joint User and Target Tracking in Multi-Static Sensing Systems

Boyang Hu;Hui Tian;Wei Ni;Shaoshuai Fan

2024 IEEE/CIC International Conference on Communications in China (ICCC Workshops)
Year: 2024 | Conference Paper |
Perceiving the mobility of the passive targets is critical in the context of Integrated Sensing and Communication (ISAC). While target localization has been extensively studied in radio sensing, the motion estimation and tracking of the targets have not been revealed. This paper proposes a new framework for joint UE and target tracking in the multi-static sensing system under the promising scenari...Show More

Joint User and Target Tracking in Multi-Static Sensing Systems

Boyang Hu;Hui Tian;Wei Ni;Shaoshuai Fan

Year: 2024 | Conference Paper |

Detecting Unknown Threats in Smart Contracts With Domain Adaptation

Weiwei Ye;Liu Yang;Li Duan;Chao Li;Wei Ni

2024 IEEE International Conference on Software Services Engineering (SSE)
Year: 2024 | Conference Paper |
Unknown smart contract vulnerabilities objectively exist in addition to common vulnerabilities, and their potential risks cannot be ignored. Therefore, it is crucial to enhance the model's ability to detect these unknown threats. Detection models are typically trained for specific types of vulnerability data. As a result, their effectiveness in detecting new vulnerabilities can be unsatisfactory. ...Show More

Detecting Unknown Threats in Smart Contracts With Domain Adaptation

Weiwei Ye;Liu Yang;Li Duan;Chao Li;Wei Ni

Year: 2024 | Conference Paper |

Intent-Oriented Network Slicing with Hypergraphs

Sai Zou;Minghui Liwang;Bing Wu;Wen Wu;Yanglong Sun;Wei Ni

IEEE Network
Year: 2024 | Early Access Article |
Establishing network slices in line with the intent of particular applications through closed-loop automation represents a key goal on designing future advanced networks. However, the lack of association between application intents and network services imposes challenges to accurately capture user preference and generate customized network slices. This article introduces Intent-Oriented Network Sl...Show More

Intent-Oriented Network Slicing with Hypergraphs

Sai Zou;Minghui Liwang;Bing Wu;Wen Wu;Yanglong Sun;Wei Ni

Year: 2024 | Early Access Article |

Objective-Driven Differentiable Optimization of Traffic Prediction and Resource Allocation for Split AI Inference Edge Networks

Xinchen Lyu;Yuewei Li;Ying He;Chenshan Ren;Wei Ni;Ren Ping Liu;Pengcheng Zhu;Qimei Cui

IEEE Transactions on Machine Learning in Communications and Networking
Year: 2024 | Volume: 2 | Journal Article |
Split AI inference partitions an artificial intelligence (AI) model into multiple parts, enabling the offloading of computation-intensive AI services. Resource allocation is critical for the performance of split AI inference. The challenge arises from the time-sensitivity of many services versus time-varying traffic arrivals and network conditions. The conventional prediction-based resource alloca...Show More

Objective-Driven Differentiable Optimization of Traffic Prediction and Resource Allocation for Split AI Inference Edge Networks

Xinchen Lyu;Yuewei Li;Ying He;Chenshan Ren;Wei Ni;Ren Ping Liu;Pengcheng Zhu;Qimei Cui

Year: 2024 | Volume: 2 | Journal Article |

Optimal Charging Scheduling and Speed Control for Delay-Bounded Drone Delivery

Bin Liu;Wei Ni;Hongbo Zhu

IEEE Transactions on Vehicular Technology
Year: 2024 | Volume: 73, Issue: 11 | Journal Article |
Unmanned aerial vehicles (UAVs) have demonstrated success in delivering goods, but their delivery distances are limited due to their finite battery capacity. While roadside charging stations can replenish the battery, they cause delays and prevent timely delivery. In this paper, we present a novel UAV charging scheduling and speed control framework that optimizes the decisions on flight speed and ...Show More

Optimal Charging Scheduling and Speed Control for Delay-Bounded Drone Delivery

Bin Liu;Wei Ni;Hongbo Zhu

Year: 2024 | Volume: 73, Issue: 11 | Journal Article |

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