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System Outage Probability and Diversity Analysis of a SWIPT Based Two-Way DF Relay network Under Transceiver Hardware Impairments

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China Communications 2023年第10期20卷 120-135页

作者： Guangyue Lu Zhipeng Liu Yinghui Ye Xiaoli Chu Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts and TelecommunicationsShaanxi 710061China Key Laboratory of Universal Wireless Communications Beijing University of Posts and TelecommunicationsBeijing 100876China Department of Electronic and Electrical Engineering University of SheffieldU.K.

This paper investigates the system outage performance of a simultaneous wireless information and power transfer(SWIPT)based two-way decodeand-forward(DF)relay network,where potential hardware impairments(HIs)in all transceivers are *** harvesting energy and decoding messages simultaneously via a power splitting scheme,the energy-limited relay node forwards the decoded information to both *** terminal combines the signals from the direct and relaying links via selection *** derive the system outage probability under independent but non-identically distributed Nakagami-m fading *** reveals an overall system ceiling(OSC)effect,i.e.,the system falls in outage if the target rate exceeds an OSC threshold that is determined by the levels of ***,we derive the diversity gain of the considered *** result reveals that when the transmission rate is below the OSC threshold,the achieved diversity gain equals the sum of the shape parameter of the direct link and the smaller shape parameter of the terminalto-relay links;otherwise,the diversity gain is *** is different from the amplify-and-forward(AF)strategy,under which the relaying links have no contribution to the diversity *** results validate the analytical results and reveal that compared with the AF strategy,the SWIPT based two-way relaying links under the DF strategy are more robust to HIs and achieve a lower system outage probability.

关键词： decode-and-forward relay diversity gain hardware impairments simultaneous wireless information and power transfer system outage probability

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Differentially Private and Heterogeneity-Robust Federated Learning With Theoretical Guarantee

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6369-6384页

作者： Wang, Xiuhua Wang, Shuai Li, Yiwei Fan, Fengrui Li, Shikang Lin, Xiaodong Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China University of Guelph School of Computer Science GuelphONN1G 2W1 Canada

Federated learning (FL) is a popular distributed paradigm where enormous clients collaboratively train a machine learning (ML) model under the orchestration of a central server without knowing the clients' private raw data. The development of effective FL algorithms faces multiple practical challenges including data heterogeneity and clients' privacy protection. Despite that numerous attempts have been made to deal with data heterogeneity or rigorous privacy protection, none have effectively tackled both issues simultaneously. In this article, we propose a differentially private and heterogeneity-robust FL algorithm, named DP-FedCVR to mitigate the data heterogeneity by following the client-variance-reduction strategy. Besides, it adopts a sophisticated differential privacy (DP) mechanism where the privacy-amplified strategy is applied, to achieve a rigorous privacy protection guarantee. We show that the proposed DP-FedCVR algorithm maintains its heterogeneity-robustness though DP noises are incorporated, while achieving a sublinear convergence rate for a nonconvex FL problem. Numerical experiments based on image classification tasks are presented to demonstrate that DP-FedCVR provides superior performance over the benchmark algorithms in the presence of data heterogeneity and various DP privacy budgets. © 2020 IEEE.

关键词： Federated learning

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Achieving reliable and anti-collusive outsourcing computation and verification based on blockchain in 5G-enabled IoT

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Digital Communications and networks 2022年第5期8卷 644-653页

作者： Linjie Wang Youliang Tian Jinbo Xiong School of Data Science Tongren UniversityTongren554300China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou UniversityGuiyang550025China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber SecurityFujian Normal UniversityFuzhou350117China

Widespread applications of 5G technology have prompted the outsourcing of computation dominated by the Internet of Things(IoT)cloud to improve transmission efficiency,which has created a novel paradigm for improving the speed of common connected objects in ***,although it makes it easier for ubiquitous resource-constrained equipment that outsources computing tasks to achieve high-speed transmission services,security concerns,such as a lack of reliability and collusion attacks,still exist in the outsourcing *** this paper,we propose a reliable,anti-collusion outsourcing computation and verification protocol,which uses distributed storage solutions in response to the issue of centralized storage,leverages homomorphic encryption to deal with outsourcing computation and ensures data ***,we embed outsourcing computation results and a novel polynomial factorization algorithm into the smart contract of Ethereum,which not only enables the verification of the outsourcing result without a trusted third party but also resists collusion *** results of the theoretical analysis and experimental performance evaluation demonstrate that the proposed protocol is secure,reliable,and more effective compared with state-of-the-art approaches.

关键词： Outsourcing computation Ethereum Smart contract Public verification Homomorphic encryption

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Enhancing Parameter-Efficient Transformers with Contrastive Syntax and Regularized Dropout for Neural Machine Translation 21st

Enhancing Parameter-Efficient Transformers with Contrastive...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Fan, Yunlong Yang, Zhiheng Li, Baixuan Gao, Zhiqiang School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China

ISBN: (纸本)9789819601219

Transformers have significantly improved Neural Machine Translation (NMT) models, accompanied by the inherent space complexity of O(n2). While recent approaches aim to be parameter-efficient, they often exhibit limited generalization capabilities with fewer parameters and decreased performances in longer sentences. To this end, we propose two methods, Syntax-enhanced Contrastive Learning (Syn-CL) and JS divergence-based Regularized Dropout (JSR-Drop) for the cross-layer parameter-sharing Transformer (baseline) in NMT. In Syn-CL, we add the corresponding target-to-source instances for the same batch in an extra bidirectional training phase, and minimize the distances of syntax-enhanced representation between the bilingual sentence-pair while maximizing the ones among others. In JSR-Drop, we refine the regularized dropout strategy with JS divergence, and improve generalization capabilities by reducing Transformers’ inconsistencies between training and inference introduced by dropout. Extensive experiments on six NMT tasks, IWSLT2014 (German↔English) and IWSLT2017 (English↔French and English↔Romanian), show an average BLEU score of 37.55, surpassing the vanilla Transformer and the baseline up to 1.75 BLEU, and outperforming models with 1.5×, 2.0× parameters in translating long sentences. Our code is available at https://***/Anon1214/EnPeNMT. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Associative Task Computation Offloading for Industrial Internet of Things 16

Associative Task Computation Offloading for Industrial Inter...

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Jiang, Fan Zhang, Hang Liu, Lei Wang, Chaowei Xian University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Xi'an China Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350390643

In this paper, we propose a computational offloading strategy tailored for multiple industrial equipment (IE) within an Industrial Internet of Things (IIoT) framework. Our primary focus is on addressing the computational offloading problem, specifically by modeling the associative relationship between tasks as a Directed Acyclic Graph (DAG). Initially, we transform the DAG task offloading problem into a Markov decision process (MDP). Subsequently, to derive the optimal offloading decision, we propose a novel approach termed DAG Task offloading with Message Passing Neural network (MPNN) and Double Deep Q-network (DAGTO-MAD). The proposed algorithm aims to min-imize the completion time of the task set. Moreover, we enhance the performance of the MPNN by adapting it to extract structural information from the DAG more efficiently. Simulation results demonstrate that our proposed algorithm achieves a shorter completion time compared to existing offloading algorithms across various environments. Additionally, our algorithm exhibits robust convergence and versatility, highlighting its effectiveness in real-application scenarios. © 2024 IEEE.

关键词： Markov processes

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LESaET: Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER

LESaET: Low-Dimensional Embedding Method for Link Prediction...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ding, Lichao Zhao, Jing Lu, Kai Hao, Zenghao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

Knowledge graphs (KGs) provide a structured representation of the real world through entity-relation triples. However, current KGs are often incomplete, typically containing only a small fraction of all possible facts. This involves inferring missing content from existing information, a task known as link prediction. Existing methods in the field of link prediction struggle with controlling the dimensionality of embedding vectors or suffer from overly complex models. In order to tackle these challenges, we introduce a method in this paper, named Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER (LESaET). LESaET leverages both self-attention mechanisms and tensor factorization to learn expressive contextual-enhanced representations of KGs. Specifically, LESaET employs the multi-head self-attention mechanism of Transformer as an encoder to capture the mutual information between entities and relationships, and utilizes Enhanced-TuckER as a decoder, ultimately achieving expressive low-dimensional embeddings for link prediction tasks. LESaET demonstrates competitive performance compared to advanced methods on standard datasets. © 2024 IEEE.

关键词： Prediction models

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Secrecy Communications for Wireless-Powered Full-Duplex Cooperative NOMA Systems: A User Selection and Friendly Jamming Approach

Secrecy Communications for Wireless-Powered Full-Duplex Coop...

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2023 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2023

作者： Ren, Yuan Ren, Jing Zhang, Xuewei Lu, Guangyue Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security School of Communications and Information Engineering Xi'an710121 China

ISBN: (纸本)9798350345407

This paper studies the secure communications of a wireless-powered full-duplex cooperative non-orthogonal multiple access (CoNOMA) system with a source node, a group of near users, a far user and an external eavesdropper. In particular, the eavesdropper intends to wiretap the transmitted signals from the source. Two of the near users are picked up, served as a wireless-powered relay and a wireless friendly jammer, respectively. We propose a user selection and cooperative jamming based secure transmission scheme. Specifically, the source transmits a superimposed signal to the selected near user. The selected near user, who is employed as a relay, can harvest energy from the radio frequency signals and decode the information simultaneously. The harvested energy are then utilized to forward the signal to the far user. The jammer also collects energy from the source, and emits artificial noise by using the harvested energy to confuse the eavesdropper. Based on the proposed scheme, the closed-form expression for secrecy outage probability (SOP) and effective secrecy throughput (EST) are derived. The correctness of our theoretical analysis is verified by Monte-Carlo simulations, and the superiority of the proposed scheme in improving the system secure performance is also validated. © 2023 IEEE.

关键词： Monte Carlo methods

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DSDIR: A Two-Stage Method for Addressing Noisy Long-Tailed Problems in Malicious Traffic Detection

DSDIR: A Two-Stage Method for Addressing Noisy Long-Tailed P...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Guoliang Zhang, Ruiqi Sun, Zhe Xing, Lingkai Zhang, Yu College of Cyber Science Nankai University Tianjin China College of Computer Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Key Laboratory of Data and Intelligent System Security Ministry of Education Tianjin China

ISBN: (纸本)9798350368741

In recent years, deep learning based malicious traffic detection (MTD) systems have demonstrated remarkable success. However, their effectiveness tend to decrease because most malicious traffic datasets are suffered from noisy-labeled and long-tailed problems. While numerous approaches have been developed to address these two problems individually, they become inefficient when confronting the combined challenge of noisy long-tailed data, as they typically tackle only a single adverse factor at a time. This paper proposes a two-stage method called Distribution-aware sample Selection and Dynamic Instance-based Relabeling (DSDIR), which simultaneously addresses the impacts of noisy-labeled and long-tailed problems. In the first stage, a noise-independent clean sample selection method is designed to obtain a clean dataset, which converts negative effects of the long-tailed problem into positive ones. In the second stage, dynamic instance-based relabeling is designed to train a model and improve the dataset's quality simultaneously. Eventually, DSDIR not only produces a balanced and noise-tolerant model but also obtains a clean dataset. Experimental results demonstrate that in the high noise condition of 60% and 80%, the accuracy rate of DSDIR is 5% higher than the state-of-the-art methods. Our code is available at https://***/nku-ligl/DSDIR. © 2025 IEEE.

关键词： deep learning distribution estimation dynamic relabeling malicious traffic detection noisy long-tailed problem

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Privacy-Preserving Access Control Model for E-Medical Systems 2

Privacy-Preserving Access Control Model for E-Medical System...

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2nd International Conference on Big Data and Privacy Computing, BDPC 2024

作者： Cai, Rongsheng Chen, Lanxiang Zhu, Yizhao City University of Macau Faculty of Data Science 999078 China College of Computer and Cyber Security Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou China

ISBN: (纸本)9798350307597

With the high speed development of information technology, the healthcare system increasingly relies on cloud servers for data storage and complex computation, patient data privacy issues stem from healthcare systems' vulnerability to breaches, despite data encryption emphasis, insufficient focus on preventing unauthorized access risks compromises patient information confidentiality. Consequently, the need to protect privacy within the healthcare system has gained significant attentions. However, existing research primarily concentrates on data encryption processes, but there is comparatively less attention given to addressing potential privacy leaks concerning data visitors. In this paper, we propose a privacy-preserving access control model for healthcare cloud data visitors. Our model combines the RBAC (Role-Based Access Control) model with the PSI (Private Set Intersection) technique to ensure the privacy and security of data visitors. The performance analysis of our proposed solution demonstrates its relative efficiency and practicality when applied to E-medical systems. © 2024 IEEE.

关键词： Differential privacy

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Trajectory Planning of Rechargeable UAVs for AoS Minimization in IoT networks 16

Trajectory Planning of Rechargeable UAVs for AoS Minimizatio...

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Liu, Yunchao Lin, Suling Gong, Jie School of Computer Science and Engineering Sun Yat-sen University Guangdong Key Laboratory of Information Security Technology Guangzhou510006 China

ISBN: (纸本)9798350390643

Freshness of information is a critical issue for real-time Internet of Things (IoT) applications. Unmanned aerial vehicles (UAVs), due to their high flexibility and low cost, are widely deployed in time-sensitive IoT scenarios. In this article, we explore the trajectory planning problem in rechargeable-UAV-aided IoT networks. To accurately measure the freshness of information, we introduce Age of information (AoI) and Age of Synchronization (AoS). AoI is utilized to plan the UAVs' trajectories for accessing sensors (SNs) and reduce the average AoS of the system. UAVs can recharge at charging stations to maintain energy above a certain threshold. We formulate a Markov decision process with the objective of minimizing the average AoS under the energy constraint. To adjust the dynamic environment, we propose a multi-agent deep reinforcement learning method to jointly optimize the UAVs' flight trajectories and battery recharging. Simulation results demonstrate that the proposed algorithm can significantly reduce the total average AoS under the energy constraint. © 2024 IEEE.

关键词： Multi agent systems

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