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2021 International Conference on networking and network Applications, NaNA 2021

作者： He, Yue Li, Hui Li, Yiming Wu, Qiang Ji, Yuefeng Gu, Rentao Fan, Shaobin Beijing Laboratory of Advanced Information Networks Beijing100876 China BUPT Beijing Key Laboratory of Network System Architecture and Convergence Beijing100876 China BUPT School of Information and Communication Engineering Beijing100876 China China Unicom China

ISBN: (纸本)9781665441582

With the rapid development of information technology, the society's demand for information technology talents has become particularly high;how colleges and universities devote themselves to cultivating professional information technology talents is an important direction at present. However, the teacher's evaluation method stays at the exam-oriented education. In the university education, in order to help students with basic professional knowledge to learn more professional knowledge and skills, and to help teachers understand the characteristics of students more objectively, this article discusses how to improve the existing teaching environment and simulation teaching mode. A simulation teaching platform for the "Intelligent Information network"course is designed with multiple modules for complex network construction. Through the online network simulation teaching platform and the feedback obtained from the platform's use, different students' different problems can be found objectively, and the combination of theory and engineering, the combination of new and old knowledge, and the simplicity of complex software can be realized. © 2021 IEEE.

关键词： Students

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EM-Mamba: An Edge-Mix Enhanced Long-Range Sequential Modeling Mamba for Kidney Segmentation in CT Scans

EM-Mamba: An Edge-Mix Enhanced Long-Range Sequential Modelin...

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Smart World Congress (SWC), IEEE

作者： Shaowei Feng Zheng Li Mengmeng Zheng Yang Yang Xin Wang Caili Guo Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China China Telecom Digital Intelligence Technology Co. Ltd Beijing China Department of Urology Beijing Friendship hospital Capital Medical University Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Manual delineation of the kidney region in CT scans is a laborious process. Although existing models have achieved remarkable success in the realm of automated segmentation, they still encounter challenges in effectively modeling both the global and local information. To address this, we introduce Edge-Mix enhanced Mamba (EM-Mamba) for kidney segmentation, which is designed to capture global and local information from multi-scales. EM-Mamba leverages SegMamba as its backbone, utilizing Mamba’s efficiency in extracting long-range dependencies. To further enhance the model’s integration of local and global information, we introduce the Edge-Mix module, which captures edge information from the shallow layers of the network during the encoding phase and merges it with multi-scale features during the decoding phase. Additionally, a Multi-Scale Convolution (MSC) module is designed to enhance the capability of extracting effective information from 3D medical images, achieving a fusion of rich local and global information across multiple scales. The innovation of the EM-Mamba model lies in its integration of detailed and global information, as well as its robust feature extraction ability in 3D medical image analysis, offering a new solution for precise kidney segmentation. The experimental results show that our framework achieving a Dice score of $\mathbf{9 1 . 9 5}$ % on the KiTS21 dataset and 86.35 % Dice score on our proprietary dataset called Kidney Parenchyma Segmentation (KPS).

关键词： Measurement Image segmentation Solid modeling Technological innovation Three-dimensional displays Image edge detection Computed tomography Feature extraction Kidney Biomedical imaging

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RIS-Enhanced LEO Satellite Communication: Joint Passive Beamforming and Orientation Optimization

RIS-Enhanced LEO Satellite Communication: Joint Passive Beam...

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IEEE Globecom Workshops

作者： Ziyuan Zheng Wenpeng Jing Zhaoming Lu Xiangming Wen Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665459761

Low Earth orbit (LEO) satellite communication (SatCom) systems have emerged as a key enabler to provide ubiquitous connectivity in the upcoming generation networks. Yet the conflict between power-limited link budget and long-distance path loss restricts the universal application of LEO SatCom. Fortunately, the reconfigurable intelligent surface (RIS) technique can be an efficient solution to address this issue. In this paper, we incorporate a RIS into the LEO SatCom system to enhance its downlink transmission by introducing a controllable reflection link. A joint RIS passive beamforming and orientation optimization problem is formulated to maximize average throughput at the ground terminal (GT) during the service. The formulated non-convex problem has the unit-modulus constraint on the phase shifters and the piecewise RIS radiation coefficient function related to the predictable satellite trajectory. A two-stage optimization framework is proposed to equivalently solve two sequential subproblems. Specifically, for the passive beamforming subproblem, a closed-form solution is derived at first in compliance with the phase alignment principle. Then, an iterative algorithm is proposed to approximately solve the orientation optimization subproblem. Simulation results show the substantial performance gains on link budget and throughput achieved by the proposed RIS-assisted LEO SatCom scheme.

关键词： Satellites Array signal processing Simulation Low earth orbit satellites Performance gain Throughput Downlink

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MIMO Radar Aided Beam Selection in Millimeter Wave V2I Communications

MIMO Radar Aided Beam Selection in Millimeter Wave V2I Commu...

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International Conference on Computer and Communications (ICCC)

作者： Dianang Li Xinxin He Jie Lv Tao Wang Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

The multiple-in-multiple-out (MIMO) radar is deployed to assist the millimeter wave (mmWave) communication with low beam training overhead in vehicle-to-infrastructure (V2I), which has attracted increasing attention. This paper proposes a structured weighted radar aided beam selection. To be specific, since the spatial information provided by radar involves inevitable inaccuracy due to the angle deviation, we calculate the probability distribution of the codewords based on the deviation angle and use it for designing the training codebook. Furthermore, beam selection is formulated as prior weighted sparse signal recovery problem, where the prior structured-weight for communication is determined by the spatial congruence with radar channel, and the difference between two frequency bands is derived to correct the weight. The resulting prior structured weight is further deployed to the orthogonal matching pursuit algorithm to enhance its performance. Simulation results demonstrate that the proposed method provides the efficient and reliable beam selection performance of the mobility mmWave channel with fewer training overhead.

关键词： Training MIMO radar Vehicle-to-infrastructure Simulation Matching pursuit algorithms Millimeter wave radar Radar tracking

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Task-Oriented Multi-user Semantic Communication based on Deep Over-the-Air Computation

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IEEE Transactions on Vehicular Technology 2025年

作者： Wei, Shuangying Feng, Chunyan Guo, Caili Zhang, Biling Chen, Jiujiu Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Nanchang Hangkong University Key Laboratory of Processing and Pattern Recognition of Jiangxi Province School of Information Engineering Nanchang330063 China

The presence of large-scale heterogeneous IoT devices and AI-based applications has brought significant transmission and computation pressure on the wireless communication system. In this paper, we propose a novel multi-user semantic communication framework based on deep over-the-air computation called SC-DAC to tackle this challenge. By integrating communication and computation through a deep over-the-air computation (AirComp) encoder, SC-DAC is able to utilize the superposition of multiple access channels, facilitating multi-user information fusion during transmission and eliminating the need for multi-user detection at the receiver. Further, to achieve the optimal task results, the semantic encoders, decoders, and AirComp encoder are jointly optimized, where stochastic gradient descent (SGD) algorithm and deep double-Q network (DDQN) are employed to optimize the encoder/decoder parameters. The simulation results demonstrate that as the number of users increased, the performance of the system remains relatively stable and shows potential in resource utilization. What's more, when applied to the AI-based tasks such as multi-user multimodal action recognition and semantic segmentation, SC-DAC enhances the accuracy of action recognition by 26% compared with traditional methods and by about 30%-50% compared with other multi-user semantic communication methods under conditions of low signal-to-noise ratio (SNR), while it enhances the accuracy of semantic segmentation by 0.22 to 0.32 compared to other methods © 1967-2012 IEEE.

关键词： Deep reinforcement learning

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Communication network Selection for Distribution network Based on Analytic Hierarchy Process

Communication Network Selection for Distribution Network Bas...

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Power Engineering Application (ICPEA), IEEE International Conference in

作者： Wei Ge Qi Wei Weijun Hong Yuchen Wu Cui Li Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China State Grid Economic and Technological Research Institute CO. LTD Beijing China Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China State Grid Fujian Economic Research Institute Fuzhou China

The access of large-scale distributed new energy has high requirements for the protection of new distribution network and communication network, but the selection of the communication network has no definite standard. In this paper, a communication network selection mechanism based on Analytic Hierarchy Process (AHP) is proposed to solve the problem of smart grid communication network selection. This method is based on the principle of decision-making analysis combining qualitative and quantitative analysis of multi- objective complex problems. The weight of indexes at all levels are calculated by the judgment matrix. After the consistency test, the overall judgment matrix is calculated to select the best scheme, which achieves the effect of decomposing and analyzing the communication network of distribution network. Through the analysis of 5 kinds of communication networks, namely optical fiber communication network, 4G network, 5G network, wireless private network and power line carrier, this research constructs the AHP model with accessibility, coverage, economy, reliability and security as the main parameter indexes, and obtains that 5G is the optimal scheme with 26.0178% of the comprehensive weight. Therefore, 5G network is more suitable for the application in the distribution network under the current new energy access.

关键词： Wireless communication 5G mobile communication Distribution networks Optical fiber communication Smart grids Communication networks Security

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On the Impact of Uncertainty and Calibration on Likelihood-Ratio Membership Inference Attacks

arXiv

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arXiv 2024年

作者： Zhu, Meiyi Guo, Caili Feng, Chunyan Simeone, Osvaldo Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China lab Department of Engineering King’s College London LondonWC2R 2LS United Kingdom

In a membership inference attack (MIA), an attacker exploits the overconfidence exhibited by typical machine learning models to determine whether a specific data point was used to train a target model. In this paper, we analyze the performance of the likelihood ratio attack (LiRA) within an information-theoretical framework that allows the investigation of the impact of the aleatoric uncertainty in the true data generation process, of the epistemic uncertainty caused by a limited training data set, and of the calibration level of the target model. We compare three different settings, in which the attacker receives decreasingly informative feedback from the target model: confidence vector (CV) disclosure, in which the output probability vector is released;true label confidence (TLC) disclosure, in which only the probability assigned to the true label is made available by the model;and decision set (DS) disclosure, in which an adaptive prediction set is produced as in conformal prediction. We derive bounds on the advantage of an MIA adversary with the aim of offering insights into the impact of uncertainty and calibration on the effectiveness of MIAs. Simulation results demonstrate that the derived analytical bounds predict well the effectiveness of MIAs. Copyright © 2024, The Authors. All rights reserved.

关键词： Prediction models

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Machine Learning Inspired Binocular Vision Indoor Positioning system Based on QR Code Beacon 2020

Machine Learning Inspired Binocular Vision Indoor Positionin...

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4th International Conference on Innovation in Artificial Intelligence, ICIAI 2020

作者： Deng, Xinyi He, Dazhong Zhou, Wenli Yang, Yang Zhang, Yue Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781450376587

With the development of the Internet of Things, indoor positioning technology has a wider application prospect. Binocular vision positioning technology can effectively meet the needs of high positioning accuracy and low hardware costs. Based on the traditional binocular vision ranging process, we aims to improve the ranging and positioning accuracy of the binocular system by using machine learning (ML) methods and Quick Response (QR) code beacons. First, this article will use the binarization technology to locate the QR code area. Second, using the fully convolutional network (FCN) structure, we design a convolutional neural network (CNN) model and train it on the KITTI dataset, and convolve the QR code region to get the disparity map of the region. Third, in order to accurately locate the coordinate position of the beacon center, our proposed system uses a density-based clustering algorithm to cluster 3D point clouds, and uses binary linear regression to fit the plane equations of the QR code beacon. Finally, the positioning experiments are carried out in practical applications. The experimental results show that compared with the traditional binocular vision algorithm, the system has higher positioning accuracy, and has performance close to the theoretical accuracy of binocular vision. © 2020 ACM.

关键词： Convolutional neural networks

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A Low Profile Wideband Absorption Structure based on Metasurface Embed with Lumped Resistance 3

A Low Profile Wideband Absorption Structure based on Metasur...

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3rd IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2020

作者： Jin, Yaping Hong, Weijun Cai, Jiaqi Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781728190457

Metamaterials exhibit more and more novel electromagnetic properties. Common ones are zero refractive index materials and negative refractive index materials. Therefore, metamaterials are widely used in antennas and antenna arrays designed today. In this paper, a metasurface embedded with lumped resistance is designed for broad band microwave absorption. The proposed metasurface is a periodic structure whose unit is consisted of four patches who are separated by a cross patch at center and surrounded by L shape patches at corner. The dimension of the proposed absorption structure is 16.5mm. The simulations show that the proposed absorption structure has a wide absorption band of 5.7-12.3 GHz with 90% absorptivity. Due to the symmetry, the proposed metasurface show great tolerance on oblique incident angle, simulations show that it can keep a wide band absorption above 90% with oblique incident angle to 60°. © 2020 IEEE.

关键词： Metamaterials

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Machine Learning Based Joint Offloading and Trajectory Design in UAV Based MEC system for IoT Devices 6

Machine Learning Based Joint Offloading and Trajectory Desig...

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6th IEEE International Conference on Computer and Communications, ICCC 2020

作者： Ma, Xingjie Yin, Changchuan Liu, Xuanlin Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781728186351

In this paper, we consider an unmanned aerial vehicle (UAV)-based mobile edge computing (MEC) system for Internet of Things devices (IoTDs), in which the mobile UAV equipped with computing resources provides service for all IoTDs in the system and each IoTD also has a certain amount of computing power. Each IoTD has a computing task to be completed and the computing task will be offloaded to the UAV or implemented locally. After the UAV completes the service for any IoTD, it proceeds to the next IoTD, until that all IoTDs in the system have been served. The UAV can collect all information related to all IoTDs before taking off, such as the data size of the computing task and the coordinate of the locations of all IoTDs. Our goal is to minimize the total delay of the system (i.e., flying delay, transmission delay, local computing delay and UAV-aided edge computing delay) through joint optimization of the flying trajectory of the UAV and the ratio of the offloading tasks. Aiming at this problem, a machine learning framework based on Q-learning algorithm is proposed so as to minimize the total delay of the system. Simulation results show that the proposed algorithm can achieve up to 15.7% and 38.0% gain compared to the random algorithm with dynamic selection of task offloading mode and the Q-learning algorithm with fixed selection of task offloading mode. © 2020 IEEE.

关键词： Internet of things

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