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SSRN 2022年

作者： Qiao, Yaojun Sun, Zhongliang Tang, Du Cui, Han Wu, Zhen Tang, Xizi Lu, Yueming The State Key Laboratory of Information Photonics and Optical Communications School of Information and Communication Engineering Beijing University of Posts and Telecommunications Xitucheng Road NO.10 Beijing100876 China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace Security Xitucheng Road NO.10 Beijing100876 China

Probabilistic constellation shaping (PCS) reduces the required SNR compared with the uniform distribution system to achieve the same mutual information (MI). However, system with lower SNR i.e. severer amplified spontaneous emission (ASE) noise adversely degenerates the performance of the conventional blind phase search (BPS) algorithm, showing an unpleasant MI penalty. In this paper, we reveal that the MI impairment comes from the misjudgment of phase noise estimation by the conventional BPS algorithm caused by ASE noise. Therefore, we propose a Gaussian blur aided BPS (GBA-BPS) algorithm, to relieve the MI penalty in the conventional BPS scheme. Compared with the conventional BPS method, the GBA-BPS algorithm introduces an unequal weight filter based on Gaussian blur, which mitigates the impact of the ASE noise on phase noise estimation. Furthermore, we provide efficient solution of the variance option of the GBA-BPS algorithm. It is demonstrated by simulation results that with a 31-tap filter in a 50-GBaud PCS-64QAM system, the proposed method offers maximum gain of 0.5 and 0.25 bits/symbol when the SNR is 16 dB and 18 dB, respectively. Finally, we show the details about why the GBA-BPS algorithm outperforms the conventional BPS algorithm from the perspective of additive phase noise (APN) and frequency response. © 2022, The Authors. All rights reserved.

关键词： Quadrature amplitude modulation

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Learned Modified Perturbation Backpropagation for Fiber Nonlinear Equalization in High-Symbol-Rate Transmission Systems

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SSRN 2022年

作者： Wu, Zhen Tang, Du Jiang, Yingjie Lu, Yueming Qiao, Yaojun The State Key Laboratory of Information Photonics and Optical Communications School of Information and Communication Engineering Beijing University of Posts and Telecommunications Xitucheng Road NO.10 Beijing100876 China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace Security Xitucheng Road NO.10 Beijing100876 China

A novel learned modified perturbation backpropagation (L-MPBP) algorithm for high-symbol-rate (HSR) coherent optical transmission system is proposed in this paper. As an extension of perturbation backpropagation (PBP), L-MPBP utilizes deep neural network to adjust the position of nonlinear compensation (NLC) in each compensation step and jointly optimize the perturbation coefficients, which greatly improves the performance of nonlinear equalization in HSR transmission systems. The proposed approach is applied to compensate nonlinear signal distortions in a 90-GBaud, PDM16QAM coherent transmission system. The results show that the proposed L-MPBP achieves 1.75 dB effective SNR improvement compared with PBP while without inducing extra complexity. Our research indicates that the L-MPBP algorithm has great application potential for nonlinear compensation in HSR systems. © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Bag2image: A Multi-Instance Network Traffic Representation for Network Security Event Prediction

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SSRN 2024年

作者： Zhang, Jiachen Han, Daoqi Lv, Zhaoxuan Liu, Yang Lu, Yueming Duan, Junke Zhang, Xinyu School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education China School of Automation Beijing Institute of Technology Beijing China Beijing Institute of Astronautical Systems Engineering Beijing China

In practical scenarios, security events triggered by abnormal network traffic often result from the collective behavior of multiple data streams, embodying group security events with collective characteristics. Existing research methods, focusing on individual data streams, lack a macroscopic analysis and struggle with challenges of analyzing massive, imbalanced data sets. To address these challenges, this paper adopts a multi-instance learning approach, mapping multiple data streams into a bag with a coarse-grained approach, where each bag corresponds to a security event label and each data stream represents an instance. We propose a multi-instance network traffic conversion method, Bag2Image, which transforms temporal multi-instance network traffic data into image representations, preserving the spatio-temporal characteristics of instances within the bag through image channels and pixels. This strategy allows the network security event prediction task to be approached as an image classification problem, leveraging advanced image classification techniques for prediction. Our cross-experiments with six advanced multi-instance learning (MIL) algorithms and six different classification models demonstrate the superior performance of our method on both the UNSW-NB15 dataset and a private dataset. Specifically, our method achieved the highest F1 scores of 77.9% and 74.4% on these datasets, respectively, representing improvements of 4.1% and 13.5% over the second-best MIL algorithm. The recall rates also saw increases of 4.1% and 13.2%, respectively. © 2024, The Authors. All rights reserved.

关键词： Network security

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The hot topics evaluation and key issues in online news about emergency events

The hot topics evaluation and key issues in online news abou...

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World Congress on Intelligent Control and Automation (WCICA)

作者： Liping Chen Junping Du Katsunori Shimohara Maoqiang Song Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer Science Beijing University of Posts and Telecommunications Beijing China Department of Information Systems Design Doshisha University Kyotanabe Kyoto Japan Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing University of Posts and Telecommunications Beijing China

This paper concentrates on the evaluation system of hot topics in the online news about the emergency events. First, the overall technology framework of the system was established and description was formulated on the key issues need to be resolved prior to the evaluation of the hot topics. Focusing on the vector representation of the news documents and clustering algorithm of the news topics, a modified TF-IDF text representation model was formulated and an improved selection method of initial topic clustering center was established. The evaluation model of the hot topics was then established to evaluate the heat value of the clustered topics based on extracting websites feature parameters, such as the time properties of the news reports, reporting properties, and user attention. Finally, the paper takes news of 2011 Japan Earthquake as data source for algorism and model evaluation. The results showed that the identification and evaluation system of hot topics in online news about the emergency events was valid, and the evaluation result of hot topics by the systems agreed with the expected results. It lays a foundation for the subsequent studying about the tracking and evolution of the hot topics.

关键词： Vectors Heating Feature extraction Media Educational institutions Earthquakes Time frequency analysis

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Gaussian Blur Aided Blind Phase Search Algorithm in Low Snr Qam Transmission System

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SSRN 2022年

作者： Sun, Zhongliang Tang, Du Cui, Han Wu, Zhen Tang, Xizi Lu, Yueming Qiao, Yaojun The State Key Laboratory of Information Photonics and Optical Communications School of Information and Communication Engineering Beijing University of Posts and Telecommunications Xitucheng Road NO.10 Beijing100876 China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace Security Xitucheng Road NO.10 Beijing100876 China

Probabilistic constellation shaping (PCS) reduces the required SNR compared with the uniform distribution system to achieve the same mutual information (MI). However, severe amplified spontaneous emission (ASE) noise degenerates the performance of the conventional blind phase search (BPS) algorithm, showing an unpleasant MI penalty and reducing the shaping gain. In this paper, we reveal that the MI impairment comes from the misjudgment of phase noise estimation by the conventional BPS algorithm interfered by severe ASE noise. To cope with this problem, we propose a Gaussian blur-aided BPS (GBA-BPS) algorithm by introducing an unequal weight filter with an efficient solution of variance configuration into the conventional BPS. Details about why the GBA-BPS algorithm outperforms the conventional BPS algorithm are elaborated from the perspective of phase noise estimation error (PNEE) and frequency response. Evaluated on a 50-GBaud PCS-64QAM system, the proposed GBA-BPS algorithm with a 31-tap filter effectively mitigates the misjudgment of phase noise estimation, achieving a maximum MI gain of 0.5 and 0.25 bits/symbol compared with conventional BPS when the SNR is 16 dB and 18 dB respectively. The proposed GBA-BPS is a significant improvement over conventional BPS and is more suitable for carrier phase recovery in low SNR systems. © 2022, The Authors. All rights reserved.

关键词： Phase noise

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Sensing-Assisted Neighbor Discovery for Vehicular Ad Hoc Networks

Sensing-Assisted Neighbor Discovery for Vehicular Ad Hoc Net...

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IEEE Conference on Wireless Communications and Networking

作者： Yuyang Liu Songlin Sun Ronghui Zhang School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Engineering Research Center of Blockchain and Network Convergence Technology Ministry of Education State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

In this paper, we propose a sensing-assisted neighbor discovery algorithm that utilizes the sensing capability of radar to improve the efficiency of neighbor discovery for vehicular ad hoc networks (VANETs). To store and manage the sensing information of radar, we design the sensing neighbor list (SNL) by analogy with the communication neighbor list (CNL). For vehicle mobility, we build a vehicle-to-vehicle (V2V) state evolution model and use extended Kalman filtering (EKF) to predict, track, and update the kinematic parameters of nodes, which are stored in the SNL. Specifically, the conversion relationship between CNL and SNL is implemented by the designed SNL based neighbor discovery (SBND) algorithm. Numerical simulation results show that the performance of the proposed algorithm is significant in terms of vehicle tracking and communication overhead reduction.

关键词： Filtering Vehicular ad hoc networks Radar Predictive models Prediction algorithms Radar tracking Numerical simulation

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Reinforcement Learning Based Antenna Selection in User-Centric Massive MIMO

Reinforcement Learning Based Antenna Selection in User-Centr...

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IEEE Conference on Vehicular Technology (VTC)

作者： Xinxin Chai Hui Gao Ji Sun Xin Su Tiejun Lv Jie Zeng Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China Ministry of Industry and Information Technology Institute of Telecommunications Beijing China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China

ISBN: (数字)9781728152073

ISBN: (纸本)9781728140537

In this paper, we consider a user-centric massive multiple-input multiple-output (UC-MMIMO) system, wherein the optimal antenna selection (AS) is very complicated, because of the huge number of deployed antennas. Traditional AS algorithms rely heavily on full and perfect channel state information (CSI). Thus, we propose a novel AS algorithm to achieve low-complexity and less CSI reliance for UC-MMIMO. The proposed AS algorithm consists of the selection stage and the adjustment stage. In the selection stage, antennas are selected by a reinforcement learning (RL) based algorithm in which input data are the locations of users. In the adjustment stage, an adjustment mechanism is designed to further improve the performance. Numerical results show that our algorithm achieves better performance with lower complexity compared with related traditional algorithms.

关键词： Loaded antennas Machine learning algorithms Reinforcement learning Antenna arrays Massive MIMO

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EFM: A Negative Network Public Opinion Early Warning Model Based on Evolutionary Feature Mining

EFM: A Negative Network Public Opinion Early Warning Model B...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Fanfeixue Meng Jingchen Wu Feifan Song Xu Wu Lan Hu Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace Security BUPT Beijing University of Posts and Telecommunications Beijing China School of Computer Science (National Pilot Software Engineering School) BUPT School of Economics and Finance Changchun Finance College

With the popularity of the Internet and the increase in the number of netizens, it is of great significance to effectively monitor and predict the negative network public opinion on social media platforms. Most mainstream negative network public opinion early warning models mainly focus on mining textual features, while neglecting the response and dissemination among netizens during the process of negative network public opinion fermentation. This is one of the important reasons for the accuracy bottleneck of them. This article proposes a negative network public opinion early warning model based on evolutionary feature mining. Firstly, by using entity linking techniques based on knowledge graphs, domain prior knowledge is supplemented for the text need to be analyzed for negative network public opinion. Then, the text sentiment features extracted based on long short-term memory networks and the evolutionary features extracted based on graph convolutional networks are fused through an enhanced-feature fusion method. Finally, the features are used for classification and early warning. Experimental results based on the Weibo public dataset show that the EFM model has higher early warning performance for negative network public opinion compared to other baseline models. The accuracy of this model is 0.959.

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A twin-multi-ring channel model for Massive MIMO system

A twin-multi-ring channel model for Massive MIMO system

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International Symposium on Communications and Information Technologies (ISCIT)

作者： Ying Chen Yue Li Songlin Sun Xinzhou Cheng Xuqi Chen School of Information and Communication Engineering Beijing University of Posts and Telecommunications China Ministry of Education Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) China Unicom Network Technology Research Institute China Posts & Telecommunications Planning and Designing Institute Co. Ltd China

ISBN: (纸本)9781509041008

A Massive MIMO system is equipped with more antennas than traditional MIMO system. Channel modeling for MIMO system has been well studied. However, study for Massive MIMO system channel modeling has just begun. In this paper, we prove the correctness of the spherical wave assumption and the non-stationary properties on both time and array axes. Then, we propose a novel twin-multi-ring channel model for massive MIMO. This model is based on the extension of a multi-ring model for MIMO. Furthermore, we consider the appearance and disappearance of clusters by “Cluster generation algorithm”. Finally, we make the simple numerical analysis for the model, and we find the characteristic is well matching the measurements.

关键词： MIMO Antenna arrays Receiving antennas Channel models Fading channels Transmitting antennas Wireless communication

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Modulation Recognition based on Incremental Deep Learning

Modulation Recognition based on Incremental Deep Learning

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International Conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Yong Yang Menghan Chen XiaoYa Wang Piming Ma The 54th Research Institute of CETC Shijiazhuang China School of Information Science and Engineering Shandong University Qingdao China School of Information and Communication Engineering National Engineering Laboratory for Mobile Network Security Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications line Beijing China

Incremental learning has emerged to solve the problem of incrementally updating the classification model as the number of data classes grows. There are many challenges in incremental learning such as catastrophic forgetting and learning efficiency. In this paper, we present a method of the modulation recognition based on incremental learning, that allows learning continuously with a class-incremental way. The new classes can be added into the existing model progressively from a sequential data stream. We conduct experiments on the modulation signal dataset characterized by the constellation diagram, the experimental results prove the feasibility of our incremental learning system. Our method performs similarly in classification accuracy compared to common multi-task joint training, but performs better in training efficiency.

关键词： Training Learning systems Deep learning Constellation diagram Memory management Modulation Aerospace electronics

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