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Modular Extremely Large-Scale Array Communication:Near-Field Modelling and Performance analysis

China Communications 2023年第4期20卷 132-152页

作者： Xinrui Li Haiquan Lu Yong Zeng Shi Jin Rui Zhang National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and SecuritySoutheast UniversityNanjing 210096China Purple Mountain Laboratories Nanjing 211111China The Chinese University of Hong Kong Shenzhen Research Institute of Big DataShenzhen 518172China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore

This paper investigates the wireless communication with a novel architecture of antenna arrays,termed modular extremely large-scale array(XLarray),where array elements of an extremely large number/size are regularly mounted on a shared platform with both horizontally and vertically interlaced *** module consists of a moderate/flexible number of array elements with the inter-element distance typically in the order of the signal wavelength,while different modules are separated by the relatively large inter-module distance for convenience of practical *** accurately modelling the signal amplitudes and phases,as well as projected apertures across all modular elements,we analyse the near-field signal-to-noise ratio(SNR)performance for modular XL-array *** on the non-uniform spherical wave(NUSW)modelling,the closed-form SNR expression is derived in terms of key system parameters,such as the overall modular array size,distances of adjacent modules along all dimensions,and the user's three-dimensional(3D)*** addition,with the number of modules in different dimensions increasing infinitely,the asymptotic SNR scaling laws are ***,we show that our proposed near-field modelling and performance analysis include the results for existing array architectures/modelling as special cases,e.g.,the collocated XL-array architecture,the uniform plane wave(UPW)based far-field modelling,and the modular extremely large-scale uniform linear array(XL-ULA)of *** simulation results are presented to validate our findings.

关键词： modular extremely large-scale array practical deployment projected apertures non-uniform spherical wave near-field modelling

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Multi-scale cyclical similarity prototype refinement for few-shot breast ultrasound image segmentation 17

Multi-scale cyclical similarity prototype refinement for few...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Ou, Yingfeng Yang, Xing Zhang, Jian Jian, Caiqing Wang, Lihui College of Computer Science and Technology Guizhou University Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data Engineering Research Center of Text Computing Ministry of Education Guiyang China

ISBN: (纸本)9798350387384

Few-shot learning based methods can address the reliance on large-scale labeled samples in current breast tumor segmentation. However, previous methods typically rely on a few support samples to extract abstract, coarse-grained semantic prototypes, without considering the interaction between support and query data. In this study, we aim to provide each query image with relevant and information-rich support information. By integrating the difficult point selection module and the multi-scale cyclical prototype refinement module, we progressively refine coarse-grained support prototypes into more informative, fine-grained class prototypes, thereby better matching the highly variable breast ultrasound query data. Experiments on two datasets demonstrate that our proposed multi-scale cyclical similarity prototype refinement network (CSPRNet) achieves remarkable improvements compared to previous state-of-the-art methods. Specifically, using ResNet50 as the backbone, we achieve 57.93%/57.92% and 57.58%/59.24% mIoU for 1-shot and 5-shot segmentation, respectively, on public and private datasets, outperforming the suboptimal method by 8.18%/2.55% and 2.77%/4.48%. © 2024 IEEE.

关键词： Ultrasonic imaging

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Mean-field theory for the Nagel-Schreckenberg model with overtaking strategy 5

Mean-field theory for the Nagel-Schreckenberg model with ove...

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5th International Workshop on Statistical Physics and Mathematics for Complex Systems, SPMCS 2017

作者： Su, Zhu Deng, Weibing Han, Jihui Li, Wei Cai, Xu National Engineering Laboratory for Technology of Big Data Applications in Education Central China Normal University Wuhan China Complexity Science Center Institute of Particle Physics Central China Normal University Wuhan China School of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou China

Based on the Nagel-Schreckenberg (NS) model with periodic boundary conditions, a modified model considered overtaking strategy (NSOS) has been proposed [1, 2]. In this paper, we focus on the theoretical analysis of traffic flow for NSOS model by using mean-field method. In the special case of vmax = 1 where vehicles can not overtake preceding ones, the features of stationary state can be obtained exactly. However, in the case of vmax > 1 where overtaking happens, some approximative methods have to be took into account. The main results are that we find the reason why traffic flow is increased in the regime where densities exceed the maximum flow density, and the influence of traffic flow on the transition density is dominated by the braking probability p. © Published under licence by IOP Publishing Ltd.

关键词： Mean field theory

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Model Degradation Hinders Deep Graph Neural Networks

arXiv

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arXiv 2022年

作者： Zhang, Wentao Sheng, Zeang Yin, Ziqi Jiang, Yuezihan Xia, Yikuan Gao, Jun Yang, Zhi Cui, Bin School of CS & Key Laboratory of High Confidence Software Technologies Peking University China Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications China Beijing Institute of Technology China

Graph Neural Networks (GNNs) have achieved great success in various graph mining tasks. However, drastic performance degradation is always observed when a GNN is stacked with many layers. As a result, most GNNs only have shallow architectures, which limits their expressive power and exploitation of deep neighborhoods. Most recent studies attribute the performance degradation of deep GNNs to the over-smoothing issue. In this paper, we disentangle the conventional graph convolution operation into two independent operations: Propagation (P) and Transformation (T). Following this, the depth of a GNN can be split into the propagation depth (Dp ) and the transformation depth (Dt ). Through extensive experiments, we find that the major cause for the performance degradation of deep GNNs is the model degradation issue caused by large Dt rather than the over-smoothing issue mainly caused by large Dp. Further, we present Adaptive Initial Residual (AIR), a plug-and-play module compatible with all kinds of GNN architectures, to alleviate the model degradation issue and the over-smoothing issue simultaneously. Experimental results on six real-world datasets demonstrate that GNNs equipped with AIR outperform most GNNs with shallow architectures owing to the benefits of both large Dp and Dt, while the time costs associated with AIR can be ignored. Copyright © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

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RIM: reliable influence-based active learning on graphs 21

RIM: reliable influence-based active learning on graphs

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Wentao Zhang Yexin Wang Zhenbang You Meng Cao Ping Huang Jiulong Shan Zhi Yang Bin Cui School of CS Peking University and Apple School of CS Peking University Apple School of CS Peking University and National Engineering Laboratory for Big Data Analysis and Applications School of CS Peking University and National Engineering Laboratory for Big Data Analysis and Applications and Institute of Computational Social Science Peking University (Qingdao) China

ISBN: (纸本)9781713845393

Message passing is the core of most graph models such as Graph Convolutional Network (GCN) and Label Propagation (LP), which usually require a large number of clean labeled data to smooth out the neighborhood over the graph. However, the labeling process can be tedious, costly, and error-prone in practice. In this paper, we propose to unify active learning (AL) and message passing towards minimizing labeling costs, e.g., making use of few and unreliable labels that can be obtained cheaply. We make two contributions towards that end. First, we open up a perspective by drawing a connection between AL enforcing message passing and social influence maximization, ensuring that the selected samples effectively improve the model performance. Second, we propose an extension to the influence model that incorporates an explicit quality factor to model label noise. In this way, we derive a fundamentally new AL selection criterion for GCN and LP–reliable influence maximization (RIM)–by considering quantity and quality of influence simultaneously. Empirical studies on public datasets show that RIM significantly outperforms current AL methods in terms of accuracy and efficiency.

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Application of machine learning in ocean data

Application of machine learning in ocean data

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作者： Lou, Ranran Lv, Zhihan Dang, Shuping Su, Tianyun Li, Xinfang Qingdao China Laboratory for Regional Oceanography and Numerical Modeling Pilot National Laboratory for Marine Science and Technology Qingdao China Marine Data and Information Center The First Institute of Oceanography MNR Qingdao China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Qingdao China Thuwal Saudi Arabia

In recent years, machine learning has become a hot research method in various fields and has been applied to every aspect of our life, providing an intelligent solution to problems that could not be solved or difficult to be solved before. Machine learning is driven by data. It learns from a part of the input data and builds a model. The model is used to predict and analyze another part of the data to get the results people want. With the continuous advancement of ocean observation technology, the amount of ocean data and data dimensions are rising sharply. The use of traditional data analysis methods to analyze massive amounts of data has revealed many shortcomings. The development of machine learning has solved these shortcomings. Nowadays, the use of machine learning technology to analyze and apply ocean data becomes the focus of scientific research. This method has important practical and long-term significance for protecting the ocean environment, predicting ocean elements, exploring the unknown, and responding to extreme weather. This paper focuses on the analysis of the state of the art and specific practices of machine learning in ocean data, review the application examples of machine learning in various fields such as ocean sound source identification and positioning, ocean element prediction, ocean biodiversity monitoring, and deep-sea resource monitoring. We also point out some constraints that still exist in the research and put forward the future development direction and application prospects. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.

关键词： Biodiversity

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FedLGMatch: Federated semi-supervised learning via joint local and global pseudo labeling

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Knowledge-Based Systems 2025年 320卷

作者： Zhao, Qing Chu, Jielei Li, Zhaoyu Huang, Wei Luo, Zhipeng Li, Tianrui School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China China Railway Engineering Group Limited Beijing100039 China China Railway Eryuan Engineering Group Co. Ltd. Sichuan Chengdu610031 China College of Computer and Data Science Fuzhou University China

The bulk of existing Federated Learning (FL) algorithms pay attention to supervised setting and assume that clients have fully labeled data. However, it may be impractical for all clients to obtain plenty of labels due to high annotation costs. Hence, the Federated Semi-Supervised Learning (FSSL) as a promising paradigm has better prospect in many realistic applications (e.g. medical scenario). Under Labels-at-Server scenario, most pseudo labeling based FSSL approaches use only the global model to generate pseudo-labels for unlabeled data, while the local models are ignored. When the local data distribution is much more different from the central server (e.g., Non-IID setting), the generated pseudo-labels may contain much noise, thus, resulting in more serious confirmation bias. To tackle the crucial issue, a novel Federated Semi-Supervised Learning via Joint Local and Global Pseudo Labeling (FedLGMatch) framework is proposed in this paper. The prominent advantage of the proposed FedLGMatch is that it allows local models trained in the last communication round to assist global model in generating pseudo-labels, which successfully emphasizes more clean pseudo-label learning at each client. Experimental results also show that FedLGMatch achieves significant performance improvements than other state-of-the-art models on the standard benchmark datasets. © 2025 Elsevier B.V.

关键词： Semi-supervised learning

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Bayesian Optimization of Double-SAC Robotic Arm Path Planning Using Recent Prioritized Experience Replay 5

Bayesian Optimization of Double-SAC Robotic Arm Path Plannin...

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5th World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2023

作者： Yuan, Shuai Wen, Ke Zhang, Feng Xiong, Jianqiang Shenyang Jianzhu University Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Branch of National Special Computer Engineering Technology Research Center School of Computer Science and Engineering Shengyang110168 China Shenyang Jianzhu University School of Electrical and Control Engineering Shengyang110168 China

ISBN: (纸本)9798350307320

As for the path planning of robotic arms, Deep Reinforcement Learning (DRL) algorithms have recently been developed. However, the high-dimensional action space and state space will result in reward sparsity and low training efficiency of robotic arm path planning. We propose a Bayesian optimization of Double-SAC algorithm using recent priority experience replay to solve these issues. We first propose the sampling method of recent prioritized experience replay (RPER), which combines recent sampling and prioritized experience replay (PER). The interval of experience pool is divided according to the recent sampling method, and sampling is performed according to the sample priority of the interval. Then we adopt a Bayesian optimization method that reduces the variance of the random strategy and improve the quality of the sampled actions. The larger variance is more beneficial for exploring the environment. Smaller variance is advantageous for exploiting good experiences to obtain higher returns. Our approach finds a good balance between exploration and exploitation. We train two Soft Actor Critic (SAC) agents to obtain two independent probability distributions, and a Bayesian optimizer is adopted to combine the two distributions to design a hybrid strategy. The hybrid strategy can output better actions according to the uncertainty estimation of the distribution, and further enhance training efficiency and stability. Finally, simulation tests are performed on the ROS-Gazebo platform, and the simulation results illustrate that the algorithm proposed in this paper converges faster and has a shorter training time than the original algorithm in terms of path planning performance. © 2023 IEEE.

关键词： Motion planning

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A hybrid modeling approach incorporating physical modeling and deep learning for multiphase flow simulation 84

A hybrid modeling approach incorporating physical modeling a...

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84th EAGE Annual Conference and Exhibition

作者： Wang, Y. Zhang, J. Du, S. Yan, B. Song, H. Wang, J. Xie, C. China Texas A&M University United States University of Science and Technology Beijing China National & Local Joint Engineering Laboratory for Big-Data Analysis and Computing Technology King Abdullah University of Science and Technology Saudi Arabia Computer Network Information Center Chinese Academy of Sciences China

ISBN: (纸本)9781713884156

Multiphase flows are of great importance for the sustainable utilization of geological resources and ecological protection. Numerical simulation, as a general and powerful approach for multiphase flow modeling, faces great computational challenge for large scale and heterogeneous problems. This study explores the possibility of a hybrid modeling architecture for nonlinear multiphase flow, which integrates physical modeling and deep learning approaches. By breaking down the coupled system into pressure and saturation equations, we construct novel solvers using a physics-guided U-net and Fourier Neural Operator instead of the traditional time-consuming implicit pressure solver. The coupling of our carefully designed solver for pressure and explicit solver for saturation provides a new perspective for modeling large scale complex multiphase flows. Our results demonstrate that this hybrid modeling architecture exhibits superior predictive performance for pressure and saturation, with relative error about 0.2% and 0.6%, respectively. More importantly, our hybrid solver can improve computational efficiency by several orders of magnitude compared to traditional numerical simulation workflows. Our work showcases that the proposed hybrid modeling approach incorporating physical modeling and deep learning is promising to provide a more flexible and powerful alternative for multiphase flow modeling in geological formations. © 2023 84th EAGE Annual Conference and Exhibition. All rights reserved.

关键词： Multiphase flow

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A Cooperative Transmission Strategy with Relay-Assisted for Frequency Regulation Service in Virtual Power Plants

A Cooperative Transmission Strategy with Relay-Assisted for ...

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IEEE General Meeting Power& Energy Society

作者： Pei Liu Jianxiao Wang Qinglai Guo Cheng Feng Yuqian Zhang Qi Wang Department of Electrical Engineering Tsinghua University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

Virtual power plant (VPP) responds to dynamic changes in the system by aggregating distributed energy resources (DERs). The massive DERs rely on a reliable communication network system to realize high-frequency information collection and real-time monitoring data generation. However, due to the geographic dispersion of DERs with strong uncertainty and heterogeneity, the information of DERs faces problems such as data transmission congestion, complicated types and huge storage capacity, etc. For the frequency regulation service with strict latency requirement, the limited basic communication facilities results in information that cannot ensure the timeliness and reliability of the uplink and downlink data transmission, which negatively affects the actual responsiveness and dynamic performance and makes the individual DER and the VPP response credibility is reduced. Therefore, in this paper, we first evaluate the impact of communication delay and the packet loss rate on the VPP's capability to provide frequency regulation services, and then adopt the cooperative relaying strategy to reduce the communication latency and the packet loss rate to guarantee VPP's performance.

关键词： Uncertainty Simulation Packet loss Virtual power plants Delays Reliability data communication Communication networks Uplink Frequency control

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