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Augmented V2V Radio Fingerprinting With Heterogeneous Federated Scattering Network for Autonomous Vehicle Road Cooperation

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhang, Tiantian Xu, Dongyang Ma, Jing Alharbi, Abdullah Yu, Keping Guizani, Mohsen Leung, Victor C.M Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an710049 China Xi'an Jiaotong University School of Information and Communication Engineering Xi'an710049 China Xidian University State Key Laboratory of Integrated Services Networks China King Saud University Computer Science Department Community College Riyadh11437 Saudi Arabia Hosei University Graduate School of Science and Engineering Tokyo184-8584 Japan 999041 United Arab Emirates Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen528060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Augmented Internet of vehicle (IoV) network, providing natural infrastructure support for autonomous vehicle road cooperation (VRC), has been deemed as one of key enablers for autonomous VRC while simultaneously facing the challenge of impersonation, tampering or replay attack towards the high layer digital certificate for critical information. In this paper, we propose a heterogeneous scattering fingerprinting network (HSFNet) which amalgamates wavelet scattering and deep learning to achieve lightweight, rapid and reliable physical layer authentication by radio frequency fingerprinting (RFF) which employs the unique inherent hardware impairments within for vehicle-to everything (V2X) communication. Particularly, we first exploit a wavelet scattering network to extract RFF coefficients and eliminate redundancies within heterogeneous datasets. The establishment of a high-information-density datasets facilitates the efficient processing of extensive heterogeneous data, especially for resource-limited vehicles nodes. To improve the interpretability, we combine the multi-scale coefficients of input waveform and shapley values, formulating a complete interpretable framework and completing parameters tuning. Finally, considering heterogeneous applications in practical scenarios, we design a distributed federated learning framework which not only completes distributed training but also protects privacy with the resources-constrained intelligent IoV nodes. Experimental results demonstrate that the proposed scheme maintains excellent performance while utilizing only about 25.7% of original samples, significantly improving identification accuracy. Furthermore, the identification accuracy of federated learning ultimately converges to 98.1%. © 1967-2012 IEEE.

关键词： Deep learning

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One Step Further: Investigating Problematic Files of Architecture Anti-patterns

One Step Further: Investigating Problematic Files of Archite...

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International Symposium on software Reliability engineering (ISSRE)

作者： Jingwen Liu Wuxia Jin Qiong Feng Xinyu Zhang Yitong Dai Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University School of Software Engineering Xi'an Jiaotong University Xi'an China Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781665425889

Architecture anti-patterns violate design principles and negatively impact software internal quality. Both academia and industry have designed methods and tools to detect anti-patterns. However, these tools tend to report a large number of defects, hindering developers from prioritizing true debts. In this work, we take one step further to explore the most problematic files (we define them as root files) in the architecture anti-patterns, which are potential causes leading to the difficulty of software maintenance. Using 45 Python projects as subjects, we investigate root files' maintainability, evolution (i.e., birth, living, and death), and their interactions in different architecture anti-patterns. Our results reveal that, compared with other files in anti-patterns, these root files take only a small proportion but incur heavy maintenance costs. Our study of their evolution and interactions can help developers identify potential causes of anti-patterns. We believe our findings will benefit the practice of design problem fixing.

关键词： Industries software maintenance Costs Computer architecture Maintenance engineering software reliability Python

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RBDN: Residual Bottleneck Dense Network for Image Super-Resolution

Research Square

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Research Square 2021年

作者： An, Zeyu Zhang, Junyuan Sheng, Ziyu Er, Xuanhe Lv, Junjie College of Electronics and Information Engineering Southwest University Chongqing400715 China College of Software Engineering Chengdu University of Information Technology Chengdu610200 China The Key Laboratory of Networks and Cloud Computing Security of Universities in Chongqing Chongqing400715 China

Recent studies have shown that Super-Resolution Generative Adversarial Network (SRGAN) can significantly improve the quality of single-image super-resolution. However, the existing SRGAN approaches also have drawbacks, such as inadequate of features utilization, huge number of parameters and poor scalability. To further enhance the visual quality, we thoroughly study three key components of SRGAN: network architecture, adversarial loss and perceptual loss, and propose a DenseNet with Residual-in-Residual Bottleneck Block (RRBB) named as Residual Bottleneck Dense Network (RBDN) for single-image super-resolution. In particular, RBDN combines ResNet and DenseNet with different roles, in which ResNet refines feature values by addition and DenseNet memorizes feature values by concatenation. Specifically, the DenseNet adopts the Bottleneck structure to reduce the network parameters and improve the convergence rate. In addition, the proposed RRBB, as the basic network building unit, removes the batch normalization (BN) layer and employs the ELU function to reduce the opposite effects in the absence of BN. In this way, RBDN can enjoy the merits of the sufficient feature value refined by residual groups and the refined feature value memorized by dense connections, thus achieving better performance compared with most current residual blocks. © 2021, CC BY.

关键词： Generative adversarial networks

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Performance Analysis of the 2D-Unitary ESPRIT Algorithm for 5G mmWave networks

Performance Analysis of the 2D-Unitary ESPRIT Algorithm for ...

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Wave Electronics and its Application in Information and Telecommunication Systems (WECONF)

作者： Grigoriy Fokin Ilya Grishin Alexander Sinilnikov Vladimir Sevidov Software Defined Radio Laboratory The Bonch-Bruevich Saint Petersburg State University of Telecommunications Saint Petersburg Russia Department of Communication Networks The Bonch-Bruevich Saint Petersburg State University of Telecommunications Saint Petersburg Russia Center for Satellite Communication Federal State Budgetary Institution Order of the Red Banner of Labor Radio Research and Development Institute named after M.I. Krivosheev Saint Petersburg Russia Department of Radio Engineering S.M. Budyonny VAS Saint Petersburg Russia

ISBN: (数字)9798350364989

ISBN: (纸本)9798350364996

This paper analyzes the 2d-unitary ESPRIT algorithm and investigates its feasibility in 5G networks. The content of this paper can be divided into three parts. In the first two parts of the paper, the mathematical model of the direction of arrival (DoA) algorithm is given. The third part of the paper describes the simulation results of beamforming scenarios considering DoA location in 5G mmWave ultra-dense networks (UDNs). The contribution of this study is the investigation of the dependence of the resolution threshold and accuracy of the 2d unitary ESPRIT algorithm on the number of elements in the antenna array and the value of SINR.

关键词： Accuracy 5G mobile communication Simulation Mathematical models Telecommunications Performance analysis Millimeter wave communication

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Adaptive Traffic Forecasting on Daily Basis: A Spatio-Temporal Context Learning Approach

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IEEE Transactions on Knowledge and Data engineering 2025年

作者： Li, Xiaoyu Zhang, Yitian Long, Guodong Hu, Yupeng Lu, Wenpeng Chen, Meng Zhang, Chengqi Gong, Yongshun Shandong University School of Software Jinan 250100 China McGill University Electrical and Computer Engineering Montreal H3A 0G4 QC Canada University of Technology Sydney School of Computer Science Sydney 2007 NSW Australia Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan 250316 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan 250100 China

Traffic forecasting plays a crucial role in establishing an Intelligent Transportation System (ITS) by providing essential insights. Existing traffic forecasting relies on the assumption that there is a hidden invariant spatial-temporal pattern in the large-scale dataset. However, the traffic patterns are easily influenced by many unpredictable external factors, such as policy interventions and climate changes. Due to the dynamic nature of these exogenous factors, the traffic network's spatial-temporal patterns are also changed, thus impacting the performance of traffic forecasting models. Thus, there is an urgent need to rethink the traffic forecasting model in a fast-adaptive manner. To solve this challenge, this paper proposes an Adaptive Spatio Temporal Context Learning framework named ASTCL, which achieves desired forecasting accuracy using daily basis traffic data collected from dozens of sensors. ASTCL constructs adaptive spatio-temporal contexts for target locations in the traffic network and generates dynamic sequence graphs based on semantic similarities. The adaptive contexts aggregate valuable information from available data, while the graphs reveal dynamic trends in traffic properties. Further, ASTCL introduces a joint convolution and attention mechanism to model intricate spatio-temporal relationships from multiple perspectives. Extensive experiments conducted on four real-world datasets demonstrate that ASTCL achieves remarkable fast adaptability and outperforms other state-of-the-art methods by a significant margin. © 1989-2012 IEEE.

关键词： adaptive learning Spatio-temporal data mining traffic forecasting

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基于低成本联邦宽度学习的智能超表面辅助车联网中隐私保护知识共享（英文）

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engineering 2024年第2期 178-189页

作者： Xiaoming Yuan Jiahui Chen Ning Zhang Qiang (John) Ye Changle Li Chunsheng Zhu Xuemin Sherman Shen Qinhuangdao Branch Campus Northeastern University State Key Laboratory of Integrated Services Networks & the Research Institute of Smart Transportation Xidian University Department of Electrical and Computer Engineering University of Windsor Department of Electrical and Software Engineering University of Calgary SUSTech Institute of Future Networks Southern University of Science and Technology Department of Electrical and Computer Engineering University of Waterloo

High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility *** order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL) framework that integrates broad learning(BL) into federated learning(FL).In FBL,we design a broad fully connected model(BFCM) as a local model for training client *** enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS) configuration optimization framework for *** problem is decoupled into two convex *** to improve the resource scheduling efficiency in FBL,a double Davidon-Fletcher-Powell(DDFP) algorithm is presented to solve the time slot allocation and RIS configuration *** on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL) in FBL to compensate clients for their *** simulation results show that the proposed FBL framework achieves better performa nce than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.

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A Summary Evaluation Method Combining Linguistic Quality and Semantic Similarity

A Summary Evaluation Method Combining Linguistic Quality and...

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2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020

作者： Wang, Xingwen Liu, Bo Shen, Libin Li, Yong Gu, Rentao Qu, Guangzhi Beijing University of Technology School of Software Engineering Faculty of Information Technology Beijing100124 China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing Laboratory of Advanced Information Networks Beijing100876 China Oakland University Computer Science and Engineering Dept. RochesterMI48309 United States

ISBN: (纸本)9781728176246

Summary evaluation method is crucial to promote the development of summarization technologies. However, most of the existing summary evaluation methods seldom consider the content integrity and readability of summaries simultaneously. This paper proposed a Linguistic Quality and Semantic Similarity Model (LQSSM) to evaluate generated summaries more comprehensively. Considering the readability of summaries, a linguistic quality evaluation network (LQEN) is proposed to evaluate summaries automatically. Meanwhile, a semantic similarity evaluation network (SSEN) is introduced to directly measure the informativeness between the summary and original . Additionally, there is also a comprehensive evaluation using fusion indicators. The LQSSM and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) methods were used to score the summaries generated by five models. The results showed that the proposed method gave further feedback from different angles on the basis of reaching the level of other methods. © 2020 IEEE.

关键词： Semantics

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Sparse random compressive sensing based data aggregation in wireless sensor networks

Sparse random compressive sensing based data aggregation in ...

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作者： Yin, Li Liu, Cuiye Guo, Songtao Yang, Yuanyuan Key Laboratory of Networks and Cloud Computing Security of Universities in Chongqing College of Electronic and Information Engineering Southwest University Chongqing400715 China School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Department of Electrical and Computer Engineering Stony Brook University Stony BrookNY11794 United States

In wireless sensor networks (WSNs), the volume of data is increasing at an unpredictable rate, which inevitably leads to high spatial-temporal correlation. To eliminate data redundancy, some researchers have proposed many data aggregation methods. However, a few of aggregation approaches can handle energy consumption and latency simultaneously. Therefore, in this paper, we propose an efficient algorithm, called Delay-Minimum Energy-Balanced (DMEB) data aggregation, which benefits from the superiority of the sparse random measurement matrix and minimum delay algorithm. Owing to the sparsity characteristics of the measurement matrix, only the nodes whose corresponding elements in the matrix are non-zero take part in the measurement. Each measurement can form an aggregation tree with minimum delay. After a sink node receives all the measurements, original readings can be recovered precisely. In addition, we adopt a novel scheduling method to avoid information interference. Experiment results demonstrate that, under recovering the original data accurately, the proposed data aggregation algorithm can not only shorten delay in data collection process but also reduce communication cost and prolong network lifetime during data transmission process. © 2018 John Wiley & Sons, Ltd.

关键词： Compressed sensing

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Wireless Large AI Model: Shaping the AI-Native Future of 6G and Beyond

arXiv

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arXiv 2025年

作者： Zhu, Fenghao Wang, Xinquan Li, Xinyi Zhang, Maojun Chen, Yixuan Huang, Chongwen Yang, Zhaohui Chen, Xiaoming Zhang, Zhaoyang Jin, Richeng Huang, Yongming Feng, Wei Yang, Tingting Bai, Baoming Gao, Feifei Yang, Kun Liu, Yuanwen Muhaidat, Sami Yuen, Chau Huang, Kaibin Wong, Kai-Kit Niyato, Dusit Debbah, Mérouane College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China School of Automation Southeast University Nanjing210096 China Tsinghua University Beijing100084 China Pengcheng Laboratory Shenzhen518066 China State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China State Key Laboratory of Novel Software Technology Nanjing University Nanjing210008 China Suzhou215163 China The University of Hong Kong Hong Kong KU 6G Research Center Computer and Communication Engineering Khalifa University Abu Dhabi127788 United Arab Emirates School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates

The emergence of sixth-generation and beyond communication systems is expected to fundamentally transform digital experiences through introducing unparalleled levels of intelligence, efficiency, and connectivity. A promising technology poised to enable this revolutionary vision is the wireless large AI model (WLAM), characterized by its exceptional capabilities in data processing, inference, and decision-making. In light of these remarkable capabilities, this paper provides a comprehensive survey of WLAM, elucidating its fundamental principles, diverse applications, critical challenges, and future research opportunities. We begin by introducing the background of WLAM and analyzing the key synergies with wireless networks, emphasizing the mutual benefits. Subsequently, we explore the foundational characteristics of WLAM, delving into their unique relevance in wireless environments. Then, the role of WLAM in optimizing wireless communication systems across various use cases and the reciprocal benefits are systematically investigated. Furthermore, we discuss the integration of WLAM with emerging technologies, highlighting their potential to enable transformative capabilities and breakthroughs in wireless communication. Finally, we thoroughly examine the high-level challenges hindering the practical implementation of WLAM and discuss pivotal future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Sorting

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SEMICON: A Learning-to-hash Solution for Large-scale Fine-grained Image Retrieval

arXiv

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arXiv 2022年

作者： Shen, Yang Sun, Xuhao Wei, Xiu-Shen Jiang, Qing-Yuan Yang, Jian School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of Integrated Services Networks Xidian University China State Key Laboratory for Novel Software Technology Nanjing University China Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security Nanjing University of Science and Technology China

In this paper, we propose Suppression-Enhancing Mask based attention and Interactive Channel transformatiON (SEMICON) to learn binary hash codes for dealing with large-scale fine-grained image retrieval tasks. In SEMICON, we first develop a suppression-enhancing mask (SEM) based attention to dynamically localize discriminative image regions. More importantly, different from existing attention mechanism simply erasing previous discriminative regions, our SEM is developed to restrain such regions and then discover other complementary regions by considering the relation between activated regions in a stage-by-stage fashion. In each stage, the interactive channel transformation (ICON) module is afterwards designed to exploit correlations across channels of attended activation tensors. Since channels could generally correspond to the parts of fine-grained objects, the part correlation can be also modeled accordingly, which further improves fine-grained retrieval accuracy. Moreover, to be computational economy, ICON is realized by an efficient two-step process. Finally, the hash learning of our SEMICON consists of both global- and local-level branches for better representing fine-grained objects and then generating binary hash codes explicitly corresponding to multiple levels. Experiments on five benchmark fine-grained datasets show our superiority over competing methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Image retrieval

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