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Security and Reliability Tradeoff of NOMA Based Hybrid Satellite-Terrestrial network With a Friendly Jammer

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第2期74卷 3439-3444页

作者： Li, Bin Zou, Yulong Wu, Tonghua Zhang, Zaichen Chen, Mingkai Jiang, Yuhan NUPT Key Lab Broadband Wireless Commun & Sensor Network Minist Educ Nanjing 210003 Peoples R China NUPT Jiangsu Engn Res Ctr Commun & Network Technol Nanjing 210003 Peoples R China NUPT Sch Telecommun & Informat Engn Nanjing 210003 Peoples R China Nari Grp Corp Nanjing 211106 Peoples R China Southeast Univ Frontiers Sci Ctr Mobile Informat Commun & Secur Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China

This paper analyzes the security and reliability tradeoff (SRT) performance for a hybrid satellite-terrestrial network (HSTN) using the non-orthogonal multiple access (NOMA) protocol. To improve the SRT performance of the NOMA based HSTN, a friendly jammer assisted transmission (FJT) scheme is proposed and its outage and intercept probability expressions are carried out in closed-form. The transmission secrecy outage probability is given to show the tradeoff between the security and reliability of the FJT scheme. For the purposes of comparison, the none jammer aided transmission (NJT) scheme is also considered in simulations. Numerical results reveal that the FJT scheme significantly enhances the SRT performance of each user and the whole system compared to the NJT scheme. Moreover, the power allocation between users and that between the relay and the friendly jammer can be adjusted to achieve better SRT performance.

关键词： Security and reliability tradeoff (SRT) non-orthogonal multiple access (NOMA) friendly jammer hybrid satellite-terrestrial network Security and reliability tradeoff (SRT) non-orthogonal multiple access (NOMA) friendly jammer hybrid satellite-terrestrial network

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High spatial resolution and dynamic OFDR-based method for detecting ship structural response

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第3期36卷 035214-035214页

作者： He, Bo Fu, Xuelei Gui, Xin Li, Zhengying Wuhan Univ Technol Sch Informat Engn Luoshi Rd 122 Wuhan 430070 Peoples R China Hubei Key Lab Broadband Wireless Commun & Sensor N Luoshi Rd 122 Wuhan 430070 Peoples R China Wuhan Univ Technol Opt Fiber Sensing Technol & Network Natl Engn Res Ctr Luoshi Rd 122 Wuhan 430070 Peoples R China

Ships, as thin-walled structures, experience various structural responses at critical components and may even fail under sustained time-varying wave forces. Understanding the structural behaviors of key components during operation is essential for accurate ship safety assessments. The lack of an effective strain monitoring system that integrates both high spatial resolution and dynamic monitoring capabilities affects the timely detection of these structural responses. This paper presents a high spatial resolution dynamic strain monitoring system using optical frequency domain reflectometry, achieving a system spatial resolution of 0.11 mm, a sensing spatial resolution of 20 mm, and a sampling frequency of 10 Hz. A box girder model of a river-sea-going ship is employed to simulate structural responses under wave-induced conditions. Simulation results validate the system's accuracy. By analyzing strain distribution and variation, this study explores the dynamic collapse behavior of structures under wave forces.

关键词： ship behavior strain detection structural response OFDR experimental investigation

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communication-Efficient Distributed Learning in Massive IoT: A Graph-Based Perspective

Communication-Efficient Distributed Learning in Massive IoT:...

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IEEE Conference on wireless communications and networking

作者： Lindong Zhao Jingyue Tang Mingzhe Chen Liang Zhou Weihua Zhuang Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing China Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Various distributed learning approaches emerge for enabling ubiquitous intelligence in Internet of Things (IoT) without sacrificing data privacy. To improve communication efficiency in frequent knowledge exchange over resource-constrained IoT, different techniques for client selection have been proposed. However, the intractable scalability issues remain to be addressed in massive IoT, since highly-coupled co-channel interference adds exponential complexity to combinatorial client selection. In this work, we develop a client selection framework highly-scalable to large-scale networks with thousands of devices, which exploits the inherent graph structure derived from knowledge exchange and co-channel interference. Specifically, we first model a client selection problem for jointly optimizing learning performance and system cost under volatile network conditions. The formulated problem is encoded into a node classification problem by a directed graph. Subsequently, a general yet simple solver is designed based on graph neural networks, which selects clients by classifying node status with recursive neighborhood aggregation of node representations. Finally, extensive experimental results demonstrate that the proposed approach can perform on par with state-of-the-art methods, while scaling to networks whose size is orders of magnitude larger than they can handle.

关键词： Performance evaluation Knowledge engineering Computer aided instruction Costs Distance learning Scalability Graph neural networks Data models Internet of Things Interchannel interference

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¹⁸F-FDG PET radiomics score construction by automatic machine learning for treatment response prediction in elderly patients with diffuse large B-cell lymphoma: a multicenter study

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JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY 2025年第3期151卷 1-11页

作者： Zhao, Jincheng Zhao, Wenzhuo Chen, Man Rong, Jian Teng, Yue Chen, Jianxin Xu, Jingyan China Pharmaceut Univ Nanjing Drum Tower Hosp Sch Basic Med & Clin Pharm Dept Hematol Nanjing Peoples R China Nanjing Univ Posts & Telecommun Key Lab Broadband Wireless Commun & Sensor Network Minist Educ Nanjing Peoples R China Nanjing Univ Nanjing Drum Tower Hosp Dept Nucl Med Affiliated HospMed Sch Nanjing Peoples R China

Purpose To explore the development and validation of automated machine learning (AutoML) models for F-18-FDG PET imaging-based radiomics signatures to predict treatment response in elderly patients with diffuse large B-cell lymphoma (DLBCL). Methods A retrospective analysis was conducted on 175 elderly (>= 60 years) DLBCL patients diagnosed between March 2015 and March 2023 at two medical centers, with a total of 1010 lesions. The baseline PET imaging-based radiomics features of the training cohort were processed using AutoML model AutoGluon to generate a radiomics score (radscore) and predict treatment response at the lesion and patient levels. Furthermore, a multivariable logistic analysis was used to design and evaluate a multivariable model in the training and validation cohorts. Results ROC curve analysis showed that the radscore generated by AutoML exhibited higher accuracy in predicting treatment response at the lesion level compared to metabolic parameters (SUVmax, MTV, and TLG) in both the training group (AUC: 0.791, 0.542, 0.667, 0.651, respectively) and the validation group (AUC: 0.712, 0.616, 0.639, 0.657, respectively). Multivariable logistic analysis indicated that NCCN-IPI (OR = 5.427, 95% CI: 1.163-25.317), BCL-2 (OR = 3.714, 95% CI: 1.406-9.816), TMTV (OR = 4.324, 95% CI: 1.095-17.067), and avg-radscore (OR = 3.176, 95% CI: 1.313-7. 686) were independent predictors of treatment response. The multivariable model comprising NCCN-IPI, BCL-2, TMTV, and avg-radscore outperformed conventional models and clinical-pathological models in predicting treatment response. (P<0.05). Conclusion The radscore generated by AutoML can predict the treatment response of elderly DLBCL patients, potentially aiding in clinical decision-making.

关键词： F-18-FDG PET/CT Diffuse large B-cell lymphoma Automated machine learning Treatment response Radiomics

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Multi-Modal Sensing and Lighting Estimation for PoE-Enabled Intelligent and Energy-Efficient Lighting networks

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IEEE Internet of Things Journal 2025年

作者： Tang, Liwei Xu, Yuanyuan Liu, Xiaodong Wang, Yuhao Wang, Jinyuan Shao, Huilong Nanchang University School of Information Engineering Nanchang330031 China Nanchang Laboratory Nanchang330224 China Nanjing University of Posts and Telecommunications Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Nanjing210003 China

The application of power over Ethernet (PoE)-enabled lighting has emerged as a groundbreaking technology with the potential to achieve cooperative control and create an energy-saving lighting network. However, fine-grained user-side lighting environment perception is crucial to improve energy efficiency and user experience. To tackle this issue, a PoE-enabled intelligent lighting network with multi-modal sensing is proposed. Specifically, a multi-modal sensor is designed and installed on the side of the luminaire to provide both fine-grained lighting environment monitor and dimming. Then, a multilayer perceptron (MLP)-based estimation scheme is proposed to estimate the illumination and color temperature information around the user activity area. Moreover, an experimental lighting network is constructed and deployed in a classroom to measure practical lighting dataset. The proposed scheme effectively estimates the lighting quality on the user side based on the measurement data. Furthermore, based on the estimated information and the collaborative control capabilities of PoE, the operating current of the luminaire is updated in real time, and thus the ambient light resource is fully utilized. Finally, the proposed lighting network achieves a 39.99% energy saving. © 2014 IEEE.

关键词： Lighting fixtures

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Cross-Modal Haptic Generation for Emergency Rescue in Internet of Robotic Things

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IEEE Internet of Things Journal 2025年

作者： Liu, Hengfa Wei, Xin Gao, Yun Zhou, Liang Qian, Yi Nanjing University of Posts and Telecommunications School of Communications and Information Engineering Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing210003 China University of Nebraska-Lincoln Department of Electrical and Computer Engineering OmahaNE68588 United States

Robots equipped with multi-modal sensing capabilities play an important role in the Internet of Robotic Things (IoRT), especially in emergency rescue. However, existing rescue robots pose challenges for human operators in achieving precise manipulation due to the absence of haptic signals. Additionally, limited and fluctuating bandwidth in emergency rescue renders current cross-modal haptic generation schemes ineffective. To overcome this dilemma, we propose a novel cross-modal haptic generation scheme that enhances scalability across diverse network conditions by leveraging correlations among audio-visual-haptic modalities. Specifically, we first propose an edge-device collaboration-based architecture that dynamically extracts semantics from audio-visual signals, tailored to the current network conditions, for haptic generation. This is achieved by predicting the network state at the edge and performing multi-modal encoding and fusion at the device. Next, we design a scalable cross-modal haptic generation scheme that implements the optimal generation strategy based on received audio-visual semantics of different granularities, ensuring real-time acquisition of coarse-grained or fine-grained haptic feedback. Finally, numerical experimental results conducted on a multi-modal dataset and a simulated emergency environment indicate that the proposed scheme reliably generates haptic signals in emergency rescue. © 2025 IEEE.

关键词： Robots

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Cross-Modal Haptic Compression Inspired by Embodied AI for Haptic communications

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IEEE Transactions on Multimedia 2025年

作者： Lu, Hang Tan, Xinmeng Chen, Mingkai Zhang, Zhe Zhang, Xuguang Chen, Jianxin Wei, Xin Zhao, Tiesong Nanjing University of Posts and Telecommunications Key Laboratory of Broadband Wireless Communication and Sensor Network Technology China Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou350108 China Fuzhou University Fujian Science & Technology Innovation Laboratory for Optoelectronic Information Fuzhou350108 China

Haptic data compression has gradually become a key issue for emerging real-time haptic communications in Tactile Internet (TI). However, it is challenging to achieve a tradeoff between high perceptual quality and compression ratio in haptic data compression scheme. Inspired by the perspective of embodied AI, we propose a cross-modal haptic compression scheme for haptic communications to improve the perception quality on TI devices in this paper. Since multimodal fusion is routinely employed to improve the ability of system in cognition, we assume that haptic codec is guided by visual semantics to optimize parameter settings in the coding process. We first design a multi-dimensional tactile feature fusion network (MTFFN) relying on multi-head attention mechanism. The MTFFN extracts the multi-dimensional features from the material surface and maps them to infer the coding parameters. Secondly, we provide second-order difference and linear interpolation to establish an criterion for the determination of optimal codec parameters, which are customized by the material categories so as to give high robustness. Finally, the simulation results reveal that our compression scheme can efficiently make a personalized codec procedure for different materials, obtaining more than 17% improvement in terms of compression ratio with high perceptual quality at the same time. © 2025 IEEE.

关键词： Data compression ratio

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Progressive Masking Oriented Self-Taught Learning for Occluded Facial Expression Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Kang, Bin Wang, Shuangshuang Wang, Zongyu Li, Xin Dou, Haie Wang, Lei Xia, Zhijie College of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education Nanjing China School of Mechanical Engineering Southeast University Nanjing China

Self-taught learning (STL) is a promising solution that reduces the performance gap between weakly supervised and fully supervised learning for easily accessible, label-free images. The success of traditional STL solutions relies on the assumption that the target appearance is completely visible and well-defined. In real-world facial expression recognition scenarios, however, saliency regions are often partially occluded, which significantly hampers the generalization capability of STL methods. Nevertheless, few studies have investigated the impact of occlusion on STL. In this paper, we propose an interweaved autoencoder network for weakly supervised facial expression recognition in occlusion scenarios. The key innovation of our network lies in the Residual Connection Union (RCU) blocks that can integrate the Convolutional Neural network (CNN) and Transformer layers into a multi-scale structure. The RCU enables a progressive masking strategy to accurately identify and focus on contributive yet often overlooked image patches by analyzing the relationships among region-level target representations. In addition, we introduce a self-knowledge distillation module for the effective training of the proposed autoencoder network. Extensive experiments are conducted on four public datasets to demonstrate the superiority of our method over related works. © 2010-2012 IEEE All rights reserved.

关键词： Self-supervised learning

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Improved finite-size analysis for measurement-device-independent quantum digital signature

arXiv

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arXiv 2025年

作者： Zhu, Jia-Li Zhang, Chun-Hui Wang, Yue-Ying Wang, Qin Institute of quantum information and technology Nanjing University of Posts and Telecommunications Nanjing210003 China "Broadband Wireless Communication and Sensor Network Technology" Key Lab of Ministry of Education NUPT Nanjing210003 China "Telecommunication and Networks" National Engineering Research Center NUPT Nanjing210003 China

Quantum digital signatures (QDS), based on the principles of quantum mechanics, provide information-theoretic security, ensuring the integrity, authenticity, and non-repudiation of data transmission. With present QDS protocols, measurement-device-independent QDS (MDI-QDS) can resist all attacks on detections, yet it suffers from finite-size effect. In this work, we present and compare three parameter estimation models for finite-size analysis of two-decoy MDI-QDS. The first model is a commonly used model in previous schemes, and we propose two new models to improve the performance. Subsequently, we perform numerical simulations to evaluate the performance of the three models. The results demonstrate that the proposed methods are less affected by finite-size effect, thereby effectively enhancing the signature rate. This work contributes to the practical development of QDS. Copyright © 2025, The Authors. All rights reserved.

关键词： Authentication

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Hybrid Implementation for Untrusted-node-based Quantum key Distribution network

arXiv

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arXiv 2025年

作者： Liu, Jingyang Zhou, Xingyu Ding, Huajian Xu, Jiaxin Zhang, Chunhui Li, Jian Wang, Qin Institute of quantum information and technology Nanjing University of Posts and Telecommunications Nanjing210003 China "Broadband Wireless Communication and Sensor Network Technology"Key Lab of Ministry of Education Ministry of Education Nanjing210003 China "Telecommunication and Networks"National Engineering Research Center NUPT Nanjing210003 China

Quantum key distribution (QKD) serves as a cornerstone of secure quantum communication, providing unconditional security grounded in quantum mechanics. While trusted-node networks have facilitated early QKD deployment, their vulnerability to node compromise underscores the need for untrusted-node architectures. Measurement-device-independent QKD (MDI-QKD) and twin-field QKD (TF-QKD) have emerged as leading candidates, addressing security vulnerabilities and extending transmission distances. Despite the wide adoptions in various fiber scaling, no integrated implementation of these two protocols has been demonstrated to date. Here, we present a hybrid system that seamlessly integrates TF-QKD and MDI-QKD into one untrusted-node-based architecture. Utilizing an efficient phase estimation method based on asymmetric interferometers, we convert twin-field global phase tracking to relative phase calibration, allowing near continuous running of both protocols. Experiments demonstrate secure finite-size key rates for sending-or-not-sending QKD and MDI-QKD over fiber distances of 150 to 431 km. The results align with theoretical simulations and show the ability to surpass the absolute repeaterless key capacity. Our work offers an unified framework for deploying multi-protocol QKD networks, laying the foundation for adaptable and scalable quantum infrastructures that can meet a wide range of security and performance needs. Copyright © 2025, The Authors. All rights reserved.

关键词： Quantum communication

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