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Measurement-Based Non-Stationary Markov Tapped Delay Line Channel Model for 5G-Railways

IEEE Antennas and Wireless Propagation Letters 2025年

作者： Zhang, Xuejian He, Ruisi Yang, Mi Ding, Jianwen Chen, Ruifeng Gao, Shuaiqi Qi, Ziyi Zhang, Zhengyu Ai, Bo Zhong, Zhangdui Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation The School of Electronics and Information Engineering The Frontiers Science Center for Smart High-speed Railway System Beijing100044 China Beijing Jiaotong University School of Electronics and Information Engineering Beijing100044 China Henan High Speed Railway Operation and Maintenance Engineering Research Center Zhengzhou451460 China China Academy of Railway Sciences Corporation Ltd. Institute of Computing Technology Beijing100081 China

5 G for Railways (5G-R) is globally recognized as a promising next-generation railway communication system designed to meet increasing demands. Channel modeling serves as foundation for communication system design, with tapped delay line (TDL) models widely utilized in system simulations due to their simplicity and practicality and serves as a crucial component of various standards like 3GPP. However, existing TDL models applicable to 5G-R systems are limited. Most fail to capture non-stationarity, a critical characteristic of railway communications, while others are unsuitable for the specific frequency bands and bandwidths of 5G-R. In this paper, a channel measurement campaign for 5G-R dedicated network is carried out, resulting in a measurement-based 5-tap TDL model utilizing a first-order two-state Markov chain to represent channel non-stationarity. key model parameters, including number of taps, statistical distribution of amplitude, phase and Doppler shift, and state transition probability matrix, are extracted. The correlation between tap amplitudes is also obtained. Finally, the accuracy of model is validated through comparisons with measurement data and 3GPP model. These findings are expected to offer valuable insights for design and link-level simulation of 5G-R systems. © 2025 IEEE.

关键词： Markov chains

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The Lattice Sieve Field Selection of Cado-NFS 2

The Lattice Sieve Field Selection of Cado-NFS

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2019 2nd International Conference on Computer Information Science and Artificial Intelligence, CISAI 2019

作者： Xue, Ping Chen, Shaozhen Fu, Zhixin Institute of Cyberspace Security PLA Information Engineering University Hennan450001 China State Key Laboratory of Mathematical Engineering and Advanced Computing Hennan450001 China

The software application of the discrete logarithms on the "large" finite field is studied. The most effective algorithm for the problem is the general number field sieve (GNFS). Focusing on the theory of GNFS of solving software Cado-NFS and lattice sieves, we find the selection of lattice sieve which improve efficiency of CADO-NFS. This is the first parameter modification suggest. The efficiency of our selection is better than the current by experiment. © Published under licence by IOP Publishing Ltd.

关键词： Sieves

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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

arXiv

引用

arXiv 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan The Beijing Key Laboratory of Network System Architecture and Convergence The Beijing Advanced Information Network Laboratory Beijing University of Posts and Telecommunications Beijing China Singapore The Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Department of Industrial and Systems Engineering University of Miami Coral GablesFL33146 United States The College of Information Science and Electronic Engineering Zhejiang University Zhejiang Hangzhou310027 China The Department of Computer Science North Carolina State University RaleighNC27695 United States The Beijing Key Laboratory of Network System Architecture and Convergence China The Beijing Advanced Information Network Laboratory Beijing University of Posts and Telecommunications Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025, CC BY-NC-SA.

关键词： Markov processes

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computing Power in the Sky: Digital Twin-assisted Collaborative computing with Multi-UAV Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Wang, Chao Han, Yu Zhang, Long Jia, Ziye Zhang, Hongliang Hong, Choong Seon Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Southeast University National Mobile Communications Research Laboratory Jiangsu Nanjing211111 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Kyung Hee University Department of Computer Science and Engineering Gyeonggi-do Yongin-si17104 Korea Republic of University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

The collaboration of computing powers (CPs) among unmanned aerial vehicles (UAVs)-mounted edge servers is essential to handle data-intensive tasks of user equipments (UEs). This paper presents a multi-UAV computing power network (CPN) that orchestrates the CP resources of edge servers to cooperatively process tasks through collaborative computing. Due to the frequent interactions among edge servers and the increased complexities for large-scale task executions, we propose a digital twin (DT)-driven multi-UAV CPN framework, where the aerial DT network is created as a digital replica of the multi-UAV network to assist collaborative computing. A joint optimization problem of the service association, offloading power control, task partitions, CP allocation, and UAV trajectory design is formulated to minimize the long-term average delay of UEs. We use the Lyapunov technique to tackle the long-term energy constraint, forming a tractable optimization problem, which is modeled as a Markov decision process to balance the UAV's deficit queue stability and the UE's task completion delay. A hybrid soft actor-critic based deep reinforcement learning algorithm is developed to optimize the joint design by sampling the hybrid action from the Gumbel and Gaussian distributions. Simulation results verify that the multi-UAV CPN contributes to significant performance gains when supported by DTs. © 2025 IEEE.

关键词： Markov processes

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Rate-Overhead Tradeoff in Beam Training for RRS-Assisted Multi-User Communications

Rate-Overhead Tradeoff in Beam Training for RRS-Assisted Mul...

引用

IEEE Conference on Vehicular Technology (VTC)

作者： Shupei Zhang Yutong Zhang Boya Di Hongliang Zhang School of Electronics Engineering and Computing Science Peking University Beijing China State Key Laboratory of Advanced Optical Communication Systems and Networks Peking University Beijing China Department of Electrical and Computer Engineering Princeton University NJ USA

ISBN: (纸本)9781665454698

Holographic multiple-input multiple-output (HMIMO) with a spatially continuous aperture is a promising solution for future radio access to handle the explosively increasing data demands. As a key enabler of HMIMO, the reconfigurable refractive surface (RRS) can serve as an antenna array with numerous programmable radiation elements. In this paper, we consider a multi-user system with an RRS-aided base station (BS) where the transmit signal is refracted by the RRS towards the users. A beamforming scheme is developed via codebook design and beam training. A larger codebook size implies more codewords, each corresponding to a directional beam. When the codebook size increases, the directivity of the refracted beam is enhanced, bringing a higher data rate. However, it also leads to an exponential growth of the training overhead. To achieve the critical tradeoff between the data rate and overhead, we evaluate the system throughput and model the relation between the codebook size of the RRS and the throughput mathematically. The optimal codebook size is then derived given different user distributions. Simulation results verify our theoretical analysis and show the influence of both codebook size and RRS size on the throughput.

关键词： Training Vehicular and wireless technologies Array signal processing Simulation System performance Transmitting antennas Throughput

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CATH: an effective method for detecting denial-of-service attacks in software defined networks

引用

Science China(Information Sciences) 2019年第3期62卷 75-89页

作者： Yi GUO Fu MIAO Liancheng ZHANG Yu WANG State Key Laboratory of Mathematical Engineering and Advanced Computing Institute for Network Science and Cyberspace Tsinghua University School of Computer Science Henan University of Engineering

Software defined networks(SDNs) are innovative network frameworks that have recently received wide attention. Their programming flexibility facilitates automatic network management and control, thus mitigating existing issues in the traditional network architecture. However, SDNs face several security risks,in particular denial-of-service(DoS) attacks, the most common and serious network attacks. To address such a threat, an SDN-DoS attack detection method is proposed based on fusing multiple flow features for describing the network catastrophe between the normal and the attack state. Several statistic attributes of SDN flow information are first chosen as detection features; subsequently, the cusp model is used to establish a catastrophe equilibrium surface for SDN states. After being trained, the cusp catastrophe model can be utilized to infer whether an SDN is under DoS attack. The experimental results demonstrate that the method can effectively and timely perceive SDN-DoS attacks, not only in simple networks but also in larger enterprise networks.

关键词： DoS attacks software defined network flow features cusp model equilibrium surface

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Simultaneous Q-Space Sampling Optimization and Reconstruction for Fast and High-Fidelity Diffusion Magnetic Resonance Imaging

Simultaneous Q-Space Sampling Optimization and Reconstructio...

引用

IEEE International Symposium on Biomedical Imaging

作者： Jing Yang Jian Cheng Cheng Li Wenxin Fan Juan Zou Ruoyou Wu Qiegen Liu Hairong Zheng Shanshan Wang Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Hangzhou China Department of Electronic Information Engineering Nanchang University Nanchang China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Diffusion Magnetic Resonance Imaging (dMRI) plays a crucial role in the noninvasive investigation of tissue microstructural properties and structural connectivity in the in vivo human brain. However, to effectively capture the intricate characteristics of water diffusion at various directions and scales, it is important to employ comprehensive q-space sampling. Unfortunately, this requirement leads to long scan times, limiting the clinical applicability of dMRI. To address this challenge, we propose SSOR, a Simultaneous q-Space sampling Optimization and Reconstruction framework. We jointly optimize a subset of q-space samples using a continuous representation of spherical harmonic functions and a reconstruction network. Additionally, we integrate the unique properties of diffusion magnetic resonance imaging (dMRI) in both the q-space and image domains by applying l1-norm and total-variation *** experiments conducted on HCP data demonstrate that SSOR has promising strengths both quantitatively and qualitatively and exhibits robustness to noise.

关键词： Water In vivo Limiting Magnetic resonance imaging Harmonic analysis Robustness Image reconstruction

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A Novel Method for Detection of ECG with Deep Learning 7

A Novel Method for Detection of ECG with Deep Learning

引用

7th International Conference on Computer and Communications, ICCC 2021

作者： Wang, Haiyan Zhou, Yanjie Zhou, Bing Wang, Zongmin State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Simulation Experiment Centre Zhengzhou University of Aeronautics Zhengzhou China Collaborative Innovation Centre For Internet Healthcare Zhengzhou University Zhengzhou China School of Management Engineering Zhengzhou University Zhengzhou China School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9781665409506

With the rapid development of machine learning and deep learning, ECG intelligent detection models lean more heavily on the labeled data. However, the development of ECG annotation cannot satisfy the development of an intelligent detection model because of its high complexity and heavy workload. Developing a model with low dependence on labeled data becomes the core for solving the ECG intelligent detection. To overcome this limitation, this paper proposes a deep active learning (DAL) model for ECG intelligent detection, which combines high-performance deep learning (DL) with active learning (AL) that is less dependent on labeled data. Experimental results show that the proposed method can achieve high performance with the support of a small amount of labeled data. The proposed method could be adopted for data annotation, which achieves a high-quality data annotation and avoids useless annotation. © 2021 IEEE.

关键词： Electrocardiograms

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A Review of Field Text Analysis

A Review of Field Text Analysis

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Telecommunications, Optics and Computer Science (TOCS), IEEE Conference on

作者： Zhuolun Li Fei Kang Pu Yu Hui Shu School of Software Zhengzhou University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing MEAC Zhengzhou China

ISBN: (纸本)9781665424998

Field text analysis is the process of using natural language processing technology to understand the Text and extract information from unstructured text data. In recent years, due to the increasing maturity of machine learning technology, higher requirements have been put forward for field text analysis. This review first introduces the process of Text preprocessing and the development of text representation. Secondly, it summarizes the four typical techniques of text analysis, including named entity recognition, relation extraction, extractive text summarization, and text classification. Finally, for each typical technology, it introduces the commonly used methods and part of the researcher’s exploration of this technology.

关键词： Computer science Text analysis Text recognition Conferences Text categorization Machine learning Optics

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A quantum-classical hybrid algorithm with Ising model for the learning with errors problem

arXiv

引用

arXiv 2024年

作者： Zheng, Muxi Zeng, Jinfeng Yang, Wentao Chang, Pei-Jie Yan, Bao Zhang, Haoran Wang, Min Wei, Shijie Long, Gui-Lu Beijing Academy of Quantum Information Sciences Beijing100193 China State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore Singapore Frontier Science Center for Quantum Information Beijing100084 China Beijing National Research Center for Information Science and Technology Beijing100084 China

The Learning-With-Errors (LWE) problem is a crucial computational challenge with significant implications for post-quantum cryptography and computational learning theory. Here we propose a quantum-classical hybrid algorithm with Ising model (HAWI) to address the LWE problem. Our approach involves transforming the LWE problem into the Shortest Vector Problem (SVP), using variable qubits to encode lattice vectors into an Ising Hamiltonian. We then identify the low-energy levels of the Hamiltonian to extract the solution, making it suitable for implementation on current noisy intermediate-scale quantum (NISQ) devices. We prove that the number of qubits required is less than m(3m -1)/2, where m is the number of samples in the algorithm. Our algorithm is heuristic, and its time complexity depends on the specific quantum algorithm employed to find the Hamiltonian's low-energy levels. If the Quantum Approximate Optimization Algorithm (QAOA) is used to solve the Ising Hamiltonian problem, and the number of iterations satisfies y © 2024, CC BY-NC-ND.

关键词： Qubits

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