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High spectral efficiency 65536 QAM mobile fronthaul based on filtered Delta-Sigma modulation scheme

Journal of Lightwave technology 2025年第10期43卷 4546-4554页

作者： Yang, Xiongwei Yu, Jianjun Zhang, Long Wei, Yi Tan, Jingwen Zhao, Feng Zhou, Wen Wang, Kaihui the School of Information Science and Technology Shanghai200433 China the School of Information Science and Technology State Key Laboratory of ASIC and System Shanghai200433 China Purple Mountain Laboratories Nanjing211111 China Xian University of Posts and Telecommunications Xian China

In this paper, we experimentally demonstrate high spectral efficiency mobile fronthaul (MFH) for 1024/65536 quadrature amplitude modulation (1024/65536-QAM) signals based on a novel filtered delta-sigma modulation (DSM) scheme. Through a filtered-DSM scheme that combines correlation coding, DSM, and brick-wall filters, we successfully implemented a partial response transmission system, effectively solving the bandwidth multiplication problem caused by oversampling in the traditional DSM scheme. In the case of 1-bit and 2-bit quantization, the demonstrated systems can support the MFH of 1024-QAM and 65536-QAM at an oversampling ratio (OSR) of 7, respectively. Compared with the traditional DSM-based MFH scheme, the bandwidth of the demonstration scheme under 1-bit and 2-bit quantization is compressed by 30% and 15%, and the corresponding spectral efficiency is improved by 42.8% and 17.6%, respectively. At the same quantization SNR, the filtered-DSM scheme shows higher spectral efficiency. In addition, the performance of traditional multi-level quantization DSM and filtered-DSM schemes is compared through numerical simulation. In the same conditions of transmission bandwidth and signal level, the 1-bit and 2-bit filtered DSM schemes can not only use the additional OSR obtained from the compressed spectrum to exchange for 5 dB and 1 dB of quantization SNR, but also effectively solve the problem of uneven amplitude distribution of multi-level signals. Therefore, both experimental and simulation results show that the proposed filtered DSM scheme provides a more effective solution for future 6G mobile fronthaul. © 2025 IEEE.

关键词： Quadrature amplitude modulation

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TMC-GCN: Encrypted Traffic Mapping Classification Method Based on Graph Convolutional networks

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Computers, Materials & Continua 2025年第2期82卷 3179-3201页

作者： Baoquan Liu Xi Chen Qingjun Yuan Degang Li Chunxiang Gu School of Cyberspace Security Zhengzhou UniversityZhengzhou450002China School of Cyberspace Security Information Engineering UniversityZhengzhou450001China Henan Key Laboratory of Network Cryptography Technology Zhengzhou450001China

With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not only reflects the time characteristics of the packet but also strengthens the relationship between the client or server packets. According to FMG, a Traffic Mapping Classification model (TMC-GCN) is designed, which can automatically capture and learn the characteristics and structure information of the top vertex in FMG. The TMC-GCN model is used to classify the encrypted traffic. The encryption stream classification problem is transformed into a graph classification problem, which can effectively deal with data from different data sources and application scenarios. By comparing the performance of TMC-GCN with other classical models in four public datasets, including CICIOT2023, ISCXVPN2016, CICAAGM2017, and GraphDapp, the effectiveness of the FMG algorithm is verified. The experimental results show that the accuracy rate of the TMC-GCN model is 96.13%, the recall rate is 95.04%, and the F1 rate is 94.54%.

关键词： Encrypted traffic classification deep learning graph neural networks multi-layer perceptron graph convolutional networks

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Brillouin Frequency Shift Extraction for BOTDA Based on Gramian Angular Fields 4

Brillouin Frequency Shift Extraction for BOTDA Based on Gram...

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4th International Computational Imaging Conference, CITA 2024

作者： Zhu, Borong Zhang, Zhihao Qian, Yuhao Ma, Xiaole Wang, Liang Yu, Kuanglu Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Institute of Information Science School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China National Engineering Laboratory for Next Generation Internet Access System School of Optics and Electronic Information Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781510688834

To accurately extract Brillouin frequency shift of BOTDA with large sweeping step sizes, a novel structure of GAFCNN is proposed, combining time series coding with convolutional neural networks. The experimental data validation results demonstrate that the GAFCNN is capable of obtaining more accurate results under large sweep steps and low signal-to-noise ratios (SNRs) than other current methods. © 2025 SPIE.

关键词： Convolutional neural networks

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Enhancing GNSS Signal Authentication through Multi-Antenna systems

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IEEE Transactions on Wireless Communications 2025年第5期24卷 4361-4376页

作者： Tan, Haijun Xie, Ning Huang, Lei Li, Hongbin Pengcheng National Laboratory of China Control and Network Foundation Laboratory Shenzhen518107 China Shenzhen University State Key Laboratory of Radio Frequency Heterogeneous Integration Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen518060 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States

The Global Navigation Satellite system (GNSS) receiver has become indispensable in navigation applications due to its affordability and reliable accuracy. Despite its widespread use, GNSS is vulnerable to manipulation by malicious entities within the inherently insecure wireless landscape. This paper proposes innovative GNSS signal authentication strategies that employing multiple antennas to counteract spoofing attacks. Leveraging a multi-antenna framework enhances the distinguishability of spoofing signals over traditional single-antenna configurations. Importantly, the proposed approaches capitalize on spatial characteristics, which are independent of the GNSS signal's intrinsic features, to accurately identify spoofing attacks. Specifically, we propose a Direction-of-Arrival (DOA)-based authentication scheme to address scenarios where authentic signals are blocked during spoofing attacks. Additionally, for situations where both authentic and spoofing signals coexist at the receiver, we propose a Spatial Filter (SF)-based authentication scheme. Through rigorous theoretical analysis and comprehensive simulations, we not only validate the proposed schemes but also demonstrate their robustness and effectiveness in enhancing GNSS signal security. The simulation results, aligning closely with theoretical predictions, underscore the superiority of the proposed schemes in safeguarding against spoofing threats. © 2002-2012 IEEE.

关键词： Satellite antennas

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Laser image refractive index sensor based on the combination of a coreless optical fiber and a digital camera

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Optoelectronics Letters 2025年第2期21卷 65-68页

作者： GUO Jingzhong LAN Feng CHEN Xiang LIU Haifeng Xi’an Institute of Space Radio Technology Xi’an 710100China Key Laboratory of Optoelectronic Sensor and Sensing Network Technology College of Electronic Information and Optical EngineeringNankai UniversityTianjin 300350China

As a laser passes through a scattering medium,the light interacts with the irregular reflections within the medium,resulting in light scattering and the formation of *** this paper,an image sensor based on the combination of a coreless optical fiber and a digital camera is proposed for liquid refractive index sensing *** coreless fiber is used as a sensing unit,and the change in the speckle pattern is measured using the digital correlation method to detect the magnitude of the liquid's refractive *** experimental results indicate that the laser image sensing technique is capable of effectively distinguishing liquid samples with refractive indices ranging from 1.332 8 to1.390 8,with a sensing sensitivity of-1.306 ***,the laser image sensing technique,with its advantages of high experimental reproducibility,simple system design,remote over-control,holds great research significance and potential application in laser communication and sensor integration.

关键词： fiber scattering optical

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AdaptHM: A Fully Adaptive Data Migration Strategy for Hybrid Memory systems

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and systems 2025年第3期44卷 923-936页

作者： Peng, Zhouxuan Feng, Dan Chen, Jianxi Hu, Jing Liu, Yachun Hu, Jinlei Zhang, Jintong Wan, Tianyu Chen, Zuoning Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology MoE of China Wuhan430074 China Chinese Academy of Engineering Beijing100088 China

Data migration strategies (DMSs) improve the overall performance of hybrid memory systems by migrating frequently accessed (hot) data to faster memory. However, designing an efficient DMS is challenging since the key metrics of DMS - hot data selection, migration granularity, and migration frequency - are sensitive to access patterns of workloads. Most existing strategies focus on only one of these metrics and often overlook the crucial impact of access patterns, resulting in suboptimal performance and unnecessary migration traffic. In this article, we propose AdaptHM, a fully access-pattern-aware Adaptive DMS for Hybrid Memory systems. AdaptHM achieves adaptability on all three metrics through its unique multilevel data framework. First, AdaptHM adopts a group-level competition policy to select hot blocks, which responds faster to access patterns than threshold-based policies. Second, AdaptHM enables segment-level dynamic migration granularity by decoupling migration from remapping, which shows better-access pattern resilience than existing schemes with fixed-size global migration granularity. Third, AdaptHM adjusts the migration frequency at set-level by periodically assessing the migration benefit, avoiding unnecessary migrations. Experimental results demonstrate that AdaptHM improves the performance by an average of 12.78% and reduces energy consumption by up to 37.24% compared to the state-of-the-art scheme. © 1982-2012 IEEE.

关键词： Frequency modulation

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Minimizing CNOT-count in quantum circuit of the extended Shor’s algorithm for ECDLP

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Cybersecurity 2025年第1期8卷 153-179页

作者： Xia Liu Huan Yang Li Yang State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of SciencesBeijingChina School of Cyber Security University of Chinese Academy of SciencesBeijingChina Beijing Youzhuju Network Technology Co. Ltd.BeijingChina

The elliptic curve discrete logarithm problem(ECDLP)is a popular choice for cryptosystems due to its high level of ***,with the advent of the extended Shor’s algorithm,there is concern that ECDLP may soon be *** the algorithm does ofer hope in solving ECDLP,it is still uncertain whether it can pose a real threat in *** the perspective of the quantum circuits of the algorithm,this paper analyzes the feasibility of cracking ECDLP using an ion trap quantum computer with improved quantum circuits for the extended Shor’s *** give precise quantum circuits for extended Shor’s algorithm to calculate discrete logarithms on elliptic curves over prime felds,including modular subtraction,three diferent modular multiplication,and modular ***,we incorporate and improve upon windowed arithmetic in the circuits to reduce the *** previous studies mostly focused on minimizing the number of qubits or the depth of the circuit,we focus on minimizing the number of CNOT gates in the circuit,which greatly afects the running time of the algorithm on an ion trap quantum ***,we begin by presenting implementations of basic arithmetic operations with the lowest known CNOT-counts,along with improved constructions for modular inverse,point addition,and windowed ***,we precisely estimate that,to execute the extended Shor’s algorithm with the improved circuits to factor an n-bit integer,the CNOT-count required is1237n^(3)/log n+2n^(2)+***,we analyze the running time and feasibility of the extended Shor’s algorithm on an ion trap quantum computer.

关键词： Elliptic curve discrete logarithm problem Extended Shor’s algorithm Quantum circuits Ion trap quantum computer

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Unsupervised Outlier Detection with Reinforced Noise Discriminator

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ACM Transactions on Intelligent systems and technology 2025年第2期16卷 1-26页

作者： Zhang, Zhongping Liu, Daoheng Zhu, Jinwei Wu, Youxi Hebei Key Laboratory of Computer Virtual Technology and System Integration School of Information Science and Engineering Yanshan University Qinhuangdao China School of Information Science and Engineering Yanshan University Qinhuangdao China School of Artificial Intelligence Hebei University of Technology Tianjin China

Outlier detection is one of the hot topics in the field of machine learning and data mining. At present, there are many kinds of outlier detection algorithms. The accuracies of traditional outlier detection algorithms are often affected by unique parameters, and an increase in the amount of data and the dimensions of the data can seriously affect their efficiency and effectiveness. Methods based on generative adversarial networks (GANs) can solve the above problems, but they are unacceptable since the model often collapses during the training period. In this article, to solve the problems of curse of dimensionality and model collapse, we propose a novel reinforced noise discriminator (RND) method for unsupervised outlier detection in tabular data. We consider outlier detection as a binary classification problem. Thus, we apply a learnable reinforced discriminator and generate a large number of potential outliers with a uniform distribution and potential outliers that are close to the original data that are used as a negative sample to train the discriminator, which learns the distribution of the original data to detect outliers. We empirically compare the proposed approach with ten state-of-the-art outlier detection methods on both synthetic and real-world tabular datasets. The experimental results show that RND outperforms its competitors in the majority of cases. The codes used to perform the experiments described in this article are available at https://***/urlhearts/r-n-d. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Generative adversarial networks

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RF-Sauron:Enabling Contact-Free Interaction on Eyeglass Using Conformal RFID Tag

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IEEE Internet of Things Journal 2025年第10期12卷 14320-14332页

作者： Yang, Baizhou Chen, Ling Peng, Xiaopeng Chen, Jiashen Tang, Yani Wang, Wei Fang, Dingyi Feng, Chao Northwest University Shaanxi Key Laboratory of Passive Internet of Things and Neural Computing School of Information Science and Technology Xi'an710127 China Northwest University Xi'an Key Laboratory of Advanced Computing and Software Security School of Information Science and Technology Xi'an710127 China Rochester Institute of Technology RochesterNY14623 United States Northwest University Xi'an Key Laboratory of Advanced Computing and System Security School of Information Science and Technology Xi'an710127 China Northwest University Shaanxi International Joint Research Centre for the Battery-Free Internet of Things School of Information Science and Technology Xi'an710127 China

Smart eyeglasses are emerging as a new medium for human-computer interaction. Existing solutions typically rely on cameras or touchpads, raising privacy invasion concerns or requiring users to physically interact with the glass frames. Here, we present, a novel mid-air gesture interaction system for eyeglasses, based on radio frequency identification (RFID). The design of our system involves embedding a conformal RFID tag into the eyeglass frame, where the received signals change with user gestures. We optimize the radiation gain of the tag to avoid view blockage while preserving a long working range. To discriminate different gestures, we propose an adaptively weighted multi-channel fusion network to extract their respective distinctive features. To allow efficient adaptation of the pre-trained network to new users, we also introduce a novel diagonal dot-product attention in our contrastive learning framework to uncover the feature similarities of different users. The proposed system was evaluated through extensive experiments, demonstrating an average recognition accuracy of 98.86% across twenty users and a cross-user accuracy of 98.75%. © 2014 IEEE.

关键词： Contrastive Learning

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A pipelining task offloading strategy via delay-aware multi-agent reinforcement learning in Cybertwin-enabled 6G network

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Digital Communications and networks 2025年第1期11卷 92-105页

作者： Haiwen Niu Luhan Wang Keliang Du Zhaoming Lu Xiangming Wen Yu Liu Beijing Laboratory of Advanced Information Networks Beijing Univ.of Posts&TelecomBeijing 100876China Beijing Key Lab of Network System Architecture and Convergence Beijing Univ.of Posts&TelecomBeijing 100876China School of Information and Communication Engineering Beijing Univ.of Posts&TelecomBeijing100876China

Cybertwin-enabled 6th Generation(6G)network is envisioned to support artificial intelligence-native management to meet changing demands of 6G ***-Agent Deep Reinforcement Learning(MADRL)technologies driven by Cybertwins have been proposed for adaptive task offloading ***,the existence of random transmission delay between Cybertwin-driven agents and underlying networks is not considered in related works,which destroys the standard Markov property and increases the decision reaction time to reduce the task offloading strategy *** order to address this problem,we propose a pipelining task offloading method to lower the decision reaction time and model it as a delay-aware Markov Decision Process(MDP).Then,we design a delay-aware MADRL algorithm to minimize the weighted sum of task execution latency and energy ***,the state space is augmented using the lastly-received state and historical actions to rebuild the Markov ***,Gate Transformer-XL is introduced to capture historical actions'importance and maintain the consistent input dimension dynamically changed due to random transmission ***,a sampling method and a new loss function with the difference between the current and target state value and the difference between real state-action value and augmented state-action value are designed to obtain state transition trajectories close to the real *** results demonstrate that the proposed methods are effective in reducing reaction time and improving the task offloading performance in the random-delay Cybertwin-enabled 6G networks.

关键词： Cybertwin Multi-Agent Deep Reinforcement Learning(MADRL) Task offloading Pipelining Delay-aware

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