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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Qizheng Sun Caili Guo Rui Tang Chuanhong Liu Yufeng Zhang Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing China Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing China China Telecom Dict Application Capability Center Beijing University of Posts and Telecommunications

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

Semantic image transmission is promising for both image reconstruction or downstream task execution. However, it is still challenging to simultaneously achieve pleasing visual clarity and superior downstream tasks. To tackle this issue, we propose a semantic-preserving image coding and compression method based on the semantics of pixels (SP). Specifically, we first project the semantic importance onto pixels, which is quantified by a gradient-based mechanism. Then, we design an SP-based loss function to train the image coding and compression network. This loss function not only preserves the semantic information to boost the performance of intelligent tasks but also preserves the pixel information to achieve pleasing visual effects. Experimental results confirm improvements in both aspects of performance. Especially for intelligent task performance, the proposed method achieves up to 22.22 % and 78.40 % performance gain ratio compared with the state of art deep learning-based and the traditional method, respectively.

关键词： Training Fading channels Image coding Image communication Semantics Performance gain Visual effects Generative adversarial networks Encoding Image reconstruction

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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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Reinforcement-Learning-Enabled Beam Alignment for Water-Air Direct Optical Wireless Communications

arXiv

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arXiv 2024年

作者： Liu, Jiayue Mao, Tianqi He, Dongxuan Yang, Yang Gao, Zhen Zheng, Dezhi Zhang, Jun State Key Laboratory of CNS/ATM Beijing Institute of Technology Beijing100081 China MIIT Key Laboratory of Complex-Field Intelligent Sensing Beijing Institute of Technology Beijing100081 China Jiaxing314019 China School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

The escalating interests on underwater exploration/ reconnaissance applications have motivated high-rate data transmission from underwater to airborne relaying platforms, especially under high-sea scenarios. Thanks to its broad bandwidth and superior confidentiality, Optical wireless communication has become one promising candidate for water-air transmission. However, the optical signals inevitably suffer from deviations when crossing the highly-dynamic water-air interfaces in the absence of relaying ships/buoys. To address the issue, this article proposes one novel beam alignment strategy based on deep reinforcement learning (DRL) for water-air direct optical wireless communications. Specifically, the dynamic water-air interface is mathematically modeled using sea-wave spectrum analysis, followed by characterization of the propagation channel with ray-tracing techniques. Then the deep deterministic policy gradient (DDPG) scheme is introduced for DRL-based transceiving beam alignment. A logarithm-exponential (LE) nonlinear reward function with respect to the received signal strength is designed for high-resolution rewarding between different actions. Simulation results validate the superiority of the proposed DRL-based beam alignment scheme. Copyright © 2024, The Authors. All rights reserved.

关键词： Ray tracing

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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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A Deep Learning-Based Algorithm for Energy and Performance Optimization of Computational Offloading in Mobile Edge Computing

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Khan, Israr Raza, Salman Rehman, Waheed Ur Khan, Razaullah Nahida, Kiran Tao, Xiaofeng National Engineering Laboratory for Mobile Network Technologies Beijing University of Posts and Telecommunications Beijing100876 China Department of Computer Science National Textile University Faisalabad Pakistan Department of Computer Science University of Peshawar Peshawar Pakistan Department of Computer Science University of Engineering and Technology Mardan Pakistan Beijing Laboratory of Advanced Information Network Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications China

Mobile edge computing (MEC) has produced incredible outcomes in the context of computationally intensive mobile applications by offloading computation to a neighboring server to limit the energy usage of user equipment (UE). However, choosing a pool of application components to offload in addition to the volume of data transfer along with the latency in communication is an intricate issue. In this article, we introduce a novel energy-efficient offloading scheme based on deep neural networks. The proposed scheme trains an intelligent decision-making model that picks a robust pool of application components. The selection is based on factors such as the remaining UE battery power, network conditions, the volume of data transfer, required energy by the application components, postponements in communication, and computational load. We have designed the cost function taking all the mentioned factors, get the cost for all conceivable combinations of component offloading decisions, pick the robust decisions over an extensive dataset, and train a deep neural network as a substitute for the exhaustive computations associated. Model outcomes illustrate that our proposed scheme is proficient in the context of accuracy, root mean square error (RMSE), mean absolute error (MAE), and energy usage of UE. © 2023 Israr Khan et al.

关键词： Data transfer

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Reinforcement-Learning-Enabled Beam Alignment for Water-Air Direct Optical Wireless Communications

Reinforcement-Learning-Enabled Beam Alignment for Water-Air ...

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IEEE International Conference on Communications in China (ICCC)

作者： Jiayue Liu Tianqi Mao Dongxuan He Yang Yang Zhen Gao Dezhi Zheng Jun Zhang State Key Laboratory of CNS/ATM Beijing Institute of Technology Beijing China MIIT Key Laboratory of Complex-Field Intelligent Sensing Beijing Institute of Technology Beijing China Yangtze Delta Region Academy Beijing Institute of Technology (Jiaxing) Jiaxing China School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

关键词： Wireless communication Integrated optics Water Sea surface Analytical models Simulation Ray tracing

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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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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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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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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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