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Toward zero trust in 5G Industrial Internet collaboration systems

Digital communications and networks 2025年第2期11卷 547-555页

作者： Han Zhang Ziyan Zhang Liquan Chen School of Cyber Science and Engineering Southeast University ZTE Corporation State Key Laboratory of Mobile Network and Mobile Multimedia Technology Purple Mountain Laboratories for Network and Communication Security

With the introduction of 5G, users and devices can access the industrial network from anywhere in the world. Therefore, traditional perimeter-based security technologies for industrial networks can no longer work well. To solve this problem, a new security model called Zero Trust(ZT) is desired, which believes in “never trust and always verify”. Every time the asset in the industrial network is accessed, the subject is authenticated and its trustworthiness is assessed. In this way, the asset in industrial network can be well protected, whether the subject is in the internal network or the external network. However, in order to construct the zero trust model in the 5G Industrial Internet collaboration system, there are still many problems to be solved. In this paper, we first introduce the security issues in the 5G Industrial Internet collaboration system, and illustrate the zero trust architecture. Then, we analyze the gap between existing security techniques and the zero trust architecture. Finally, we discuss several potential security techniques that can be used to implement the zero trust model. The purpose of this paper is to point out the further direction for the realization of the Zero Trust Architecture(ZTA) in the 5G Industrial Internet collaboration system.

关键词： Zero trust 5G Industrial Internet collaboration system Trust assessment Access control Authentication

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Spatial Super-Channel Provisioning in Fiber Space Division Multiplexing (FSDM) Optical networks

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Journal of Lightwave Technology 2025年第10期43卷 4591-4606页

作者： Han, Shudan Li, Yongcheng Chang, Zeshan Li, Liangchuan Chang, Tianhai Shen, Gangxiang Soochow University Jiangsu New Optical Fiber Technology and Communication Network Engineering Research Center Suzhou Key Laboratory of Advanced Optical Communication Network Technology School of Electronic and Information Engineering Suzhou215006 China Huawei Technology Shenzhen518129 China

Space Division Multiplexing (SDM) and Spatial Super-Channel (Spa SCh) technology provide scalable solutions to meet the growing capacity demands of optical networks. This study focuses on multi-fiber optical networks integrated with innovative Spa SChs, enabling the creation of various Spa SCh granularities by spatially combining the same spectrum across multiple fibers, a transmission method referred to as Fiber-SDM (FSDM). To assess the performance of the FSDM-based network, we develop an analytical model to predict the blocking probability (BP) of Spa SChs with different granularities. Additionally, we propose an efficient adaptive Routing, Fiber, and Spectrum Assignment (RFSA) algorithm, based on the concepts of Spatial Spectrum Window (SSW) and the SSW plane (SSWP). Simulations are conducted to evaluate lightpath BP, considering the trade-off between system cost and power consumption. The results demonstrate the accuracy of the analytical model in predicting lightpath BP in the FSDM network. Furthermore, the Adaptive SSW Plane (A-SSWP) algorithm outperforms the benchmark RFSA algorithms, achieving the best performance under an optimal Spa SCh configuration. © 1983-2012 IEEE.

关键词： Blocking probability

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Improving Speech Enhancement by Cross- and Sub-band Processing with State Space Model

Improving Speech Enhancement by Cross- and Sub-band Processi...

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

作者： Li, Jizhen Tu, Weiping Yang, Yuhong Xu, Xinmeng Zhang, Yiqun Ren, Yanzhen NERCMS School of Computer Science Hubei Luojia Laboratory Wuhan University China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China School of Cyber Science and Engineering Wuhan University China

ISBN: (纸本)9798350368741

Recently, the state space model (SSM) represented by Mamba has shown remarkable performance in long-term sequence modeling tasks, including speech enhancement. However, due to substantial differences in sub-band features, applying the same SSM to all sub-bands limits its inference capability. Additionally, when processing each time frame of the time-frequency representation, the SSM may forget certain high-frequency information of low energy, making the restoration of structure in the high-frequency bands challenging. For this reason, we propose Cross- and Sub-band Mamba (CSMamba). To assist the SSM in handling different sub-band features flexibly, we propose a band split block that splits the full-band into four sub-bands with different widths based on their information similarity. We then allocate independent weights to each subband, thereby reducing the inference burden on the SSM. Furthermore, to mitigate the forgetting of low-energy information in the high-frequency bands by the SSM, we introduce a spectrum restoration block that enhances the representation of the cross-band features from multiple perspectives. Experimental results on the DNS Challenge 2021 dataset demonstrate that CSMamba outperforms several state-of-the-art (SOTA) speech enhancement methods in three objective evaluation metrics with fewer parameters. © 2025 IEEE.

关键词： band split spectrum restoration speech enhancement state space model

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HAPG-SAQAM: Human Auditory Perception Guided Spatial Audio Quality Assessment Metric

HAPG-SAQAM: Human Auditory Perception Guided Spatial Audio Q...

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

作者： Zheng, Yuanming Yao, Jiaxuan Deng, Xiangyu Yang, Yuhong Liao, Ruiqi Tu, Weiping Lin, Cedar National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan China Guangdong OPPO Mobile Telecommunications Corp. Guangdong China

ISBN: (纸本)9798350368741

Spatial audio quality evaluation is essential for applications like virtual and augmented reality, where accurate sound reproduction enhances user immersion. While subjective listening tests are the gold standard, they are costly and time-consuming. To address this, we propose HAPG-SAQAM, an objective metric for assessing timbre quality, spatial quality, and overall quality of binaural audio, guided by human auditory perception. Our contributions include: (1) the Multi-scale Auditory Guided Feature Extraction (MAGFE) module, incorporating gammatone frequency cepstral coefficients for better alignment with human perception;(2) Perceptual Weighted Loss (PWL), optimizing the weighting of timbre quality (TQ) and spatial quality (SQ) loss based on subjective test data;and (3) data augmentation techniques to enhance robustness by amplifying perceptual distortions. Experimental results show HAPG-SAQAM improves correlation with subjective scores by 10%, with ablation studies confirming the contributions of its components to enhanced spatial and overall audio quality. © 2025 IEEE.

关键词： auditory perception spatial audio Speech and audio quality assessment subjective experiment timbre quality

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Long-Term Decision-Optimal Access Mechanism in the Large-Scale Satellite network: A Multiagent Reinforcement Learning Approach

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IEEE Internet of Things Journal 2025年第9期12卷 12686-12699页

作者： Zhang, Bo Gu, Yiyang Wang, Ye Yang, Zhihua Peng Cheng Laboratory Network Communication Department Shenzhen518055 China Harbin Institute of Technology Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen518055 China Harbin Institute of Technology Communication Engineering Research Center Guangdong Shenzhen518055 China

The large-scale low-Earth orbit (LEO) satellite network presents an obvious challenge for user access with obviously dynamical coverage resulting from the fast-changing locations of LEO satellites, i.e., time-varying overlapped range in spatial and temporal coverage by multiple overhead satellites, making existing studies low throughput for supporting various users with fluctuating access demands. In this article, we propose a multiagent deep deterministic policy gradient-based access (MADDPGA) mechanism for the large-scale satellite network, which allows each user to adjust its strategy autonomously by learning the changing network conditions in the long term. By solving a throughput-maximizing optimization problem, we develop a fully decentralized multiagent deep reinforcement learning (MADRL) algorithm by exploiting optimal dormancy probability (DP) and a well-designed weighted allocation strategy. The simulation results show that the proposed method can effectively improve the throughput performance compared with the Random algorithm and fixed DP algorithm. © 2014 IEEE.

关键词： Satellite communication systems

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Optimal Illumination Distance Metrics for Person Re-identification 21st

Optimal Illumination Distance Metrics for Person Re-identif...

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

作者： Wang, Chao Wang, Zhongyuan Hu, Ruimin Wang, Xiaochen Zhou, Wen National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China College of Information Engineering Nanjing University of Finance Economics Nanjing China

ISBN: (纸本)9789819601240

Person re-identification (Re-ID) has been widely used in public security and surveillance. Due to the influence of different shooting times and locations, can lead to lighting variations in the images captured by the camera, and the lighting variations seriously affect the Re-ID performance. Existing methods utilize images under different lighting conditions to transform to uniform lighting conditions to eliminate the effect of lighting on Re-ID. However, these methods ignore the fact that different illumination metric distances have different recognition performances. Therefore, we propose the optimal illumination distance metric, by analyzing different light distances for pedestrians in different light conditions, we find that there exists an optimal light condition in which the distance between image pairs is smaller than the distance in other light conditions. The distance under the optimal lighting condition is used in the retrieval process as the distance between its final sorted image pairs. To verify the effectiveness of our method, Market-1501-IA and Duke-MTMC-IA datasets are constructed, and a large number of experiments are conducted under different lighting conditions to verify the existence of the optimal lighting conditions and the effectiveness of the proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lighting

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A Hybrid EKF/WUFIR Filter for Indoor Localization Integrating INS and UWB Data

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IEEE Transactions on network Science and engineering 2025年第3期12卷 2266-2276页

作者： Cheng, Long Song, Jiahe Zhao, Wenhao Northeastern University Department of Computer and Communication Engineering Qinhuangdao066004 China Northeastern University Hebei Key Laboratory of Marine Perception Network and Data Processing Qinhuangdao066004 China

Due to the complex and variable indoor environment, ultra-wideband (UWB) signal transmission is often obstructed by walls and obstacles, resulting in non-line-of-sight (NLOS), which reduces localization accuracy. Inertial navigation system (INS) is an autonomous navigation system that does not rely on external information and is not affected by NLOS. Therefore, a hybrid EKF/WUFIR filter indoor localization algorithm that integrates INS and UWB data is proposed. The proposed algorithm is composed of three parts: INS localization, UWB localization and data fusion. In the INS localization part, the motion model is used to determine the state of the target in real time using measurement data obtained from the inertial measurement unit (IMU). In the UWB localization part, a resettable residual weighted particle filter algorithm is proposed to mitigate the effect of NLOS on the localization results. In the data fusion part, a hybrid filtering algorithm combining extended Kalman filter (EKF) and weighted unbiased finite impulse response (WUFIR) filtering is proposed to fuse the INS and UWB localization data. Simulation and experimental results show that the proposed algorithm outperforms other comparative algorithms in terms of robustness and localization accuracy. © 2013 IEEE.

关键词： Inertial navigation systems

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Predictive Generation for Digital Twin Channel:Long-Batch Time-Frequency Construction

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China communications 2025年第4期22卷 161-173页

作者： Che Chang Hu Jie Kang Honghui Rui Hua Lyu Xingzai Wang Bo School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China State Key Laboratory of Mobile Network and Mobile Multimedia Technology ZTE CorporationShenzhen 518055China

Wireless communication systems that incorporate digital twin(DT)alongside artificial intelligence(AI)are expected to transform 6G networks by providing advanced features for predictive modeling and decision *** key component is the creation of DT channels,which form the basis for upcoming ***,the existing work of channel predictive generation only considers time dimension,distribution-oriented or multi-step slidingwindow prediction schemes,which is not accurate and efficient for real-time DT communication ***,we propose the wireless channel generative adversarial network(WCGAN)to tackle the issue of generating authentic long-batch channels for DT *** generator based on convolutional neural networks(CNN)extracts features from both the time and frequency domains to better capture the *** loss function is designed to ensure that the generated channels consistently match the physical channels over an extended period while sharing the same probability ***,the accumulating error from the slicing window has been *** simulation demonstrates that an accurate and efficient DT channel can be generated by employing our proposed WCGAN in various scenarios.

关键词： channel generation channel prediction deep learning digital twin(DT) generative adversarial network(GAN)

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A Robust Indoor Vehicle Localization Strategy based on UWB and INS

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 7292-7302页

作者： Cheng, Long Cui, Chen Zhang, Hao Northeastern University Department of Computer and Communication Engineering Qinhuangdao066004 China Northeastern University Hebei Key Laboratory of Marine Perception Network and Data Processing Hebei Qinhuangdao066004 China

High-precision indoor vehicle localization technology has recently been a research hotspot for indoor location services. This paper investigate the indoor combined positioning technology based on Ultra wide band (UWB) and inertial navigation system(INS), aiming to overcome the limitations of UWB in non-line of sight (NLOS) environment and cumulative errors of INS. For dealing with NLOS errors conveniently, we propose a two-layer NLOS error weakening method. Firstly, k-mediods clustering method is used to preprocess the obtained distance measurements, and some of the measurements containing NLOS errors are replaced with the measurements undisturbed by NLOS transmission. The processed result is processed again with gray Kalman filter, which reduces the process noise and NLOS error. A hybrid Gaussian fitting approach is suggested to determine the propagation conditions and simulate the likelihood of propagation through the residual after calculating the difference between the distance that was filtered and the distance that was measured. The two-sided correction technique changed the findings of the fitting method and the improved particle swarm optimization method. The adaptive Kalman filter was then used to combine the INS and UWB location results to get the final location data. Simulation and experimental results show that the proposed algorithm owns higher localization accuracy and dependability. © 1967-2012 IEEE.

关键词： Inertial navigation systems

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Temporal feature-based memory neural network for probabilistic-shaping polarization-division multiplexed ultrahigh-order QAM coherent optical transmission

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Chinese Optics Letters 2025年第05期 28-32页

作者：黄雪晶高明义范佳敏戈懿凡由骁迪沈纲祥 Jiangsu Engineering Research Center of Novel Optical Fiber Technology and Communication Network Suzhou Key Laboratory of Advanced Optical Communication Network TechnologySchool of Electronic and Information EngineeringSoochow University

High-speed single-carrier transmission can be achieved by increasing the modulation format cardinality for higher spectral ***,ultrahigh-order QAM signals are usually more susceptible to various ***,we propose a temporal feature-based memory (TFM) neural network (NN) equalizer to effectively mitigate signals'impairments in ultrahigh-order *** temporal convolutional network is utilized as a feature extraction layer to significantly improve the performance of the bidirectional long short-term memory *** TFM-NN equalizer was experimentally validated in a probabilistically shaped polarization-division multiplexed 1024/4096-QAM coherent optical transmission system,and raw spectral efficiencies of 16.190 and 21.188 bit/s/Hz have been achieved at normalized generalized mutual information thresholds.

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