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Sensitivity and Distance Based Performance Analysis for Batteryless Tags with Transmit Beamforming and Ambient Backscattering

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China communications 2022年第2期19卷 109-117页

作者： Minzheng Jia Chaochao Yao Wei Liu Ruyi Ye Tutun Juhana Bo Ai Department of Information Engineering Beijing Polytechnic CollegeBeijing 100042China School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China School of Communication and Information Engineering Xi’an University of Posts and TelecommunicationsXi’an 710121China Chinese National Institute of Standalization Beijing 100088China School of Electrical Engineering and Informatics Institut Teknologi BandungIndonesia State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing 100044China

Ambient backscatter is a new green technology for Internet of Things(IoT)that utilizes surrounding wireless signals to enable batteryless devices to communicate with other *** battery-free devices first harvest energy from ambient wireless signals and then backscatter the signals for ***,sensitivity and distance are two important parameters for system ***,most existing studies on ambient backscatter communication systems do not consider the impact of the sensitivity of the energy-harvesting nodes and the distances between these *** this paper,we first provide a literature review for ambient communication technology and then take sensitivity and distance as two key parameters and investigate the sensitivity and distance based performance for ambient backscatter communication ***,we establish the mathematical model based on distances between transceivers and backscattering nodes,extract a parameter that can differentiate the direct path and the backscattering path,evaluate the effects of transmit beamforming,design an energy detector for the reader,and analyze the outage probability of energy harvesting at the tag and the bit error rate(BER)at the *** are then provided to corroborate the proposed studies.

关键词： ambient backscatter battery-free tag Internet of Things(IoT) outage probability performance analysis sensitivity transmission distance

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Reverse Time Migration of Ultra-Wideband Ground-Penetrating Radar Based on Multiple Time-Gating Algorithm

Reverse Time Migration of Ultra-Wideband Ground-Penetrating ...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Junjie Wu Haixiao Li Yuanguo Zhou Le Cao Yan Wang College of Communication and Information Engineering Xi’an University of Science and Technology Xi’an China College of Electrical and Control Engineering Xi’an University of Science and Technology Xi’an China Graduate School of Chinese Aeronautical Establishment Yangzhou China Aviation Key Laboratory of Science and Technology on Electromagnetic Environment Effects Shenyang China National Key Laboratory of Electromagnetic Information Control and Effects Shenyang China

ISBN: (数字)9798350355581

ISBN: (纸本)9798350355598

To address the issue of clutter interference in ground-penetrating radar (GPR) imaging using a VNA (Vector Network Analyzer), this paper improves the traditional reverse time migration (RTM) technique for ultra-wideband GPR imaging. The improved method retains the effective signals in the detection wavefield as the valid imaging area and suppresses signals in non-valid areas. Due to variations in the distance between the transmitting and receiving antennas and the number of anomalies in the experiment, the traditional time window cross-correlation is not ideal for this scenario. The improved method automatically applies reasonable windowing to each data channel, avoiding the simultaneous enhancement of clutter interference and effective signals through direct stacking, and preventing the filtering out of effective signals. Experimental results demonstrate that this method, when applied to real measured data and compared with the traditional cross-correlation RTM method, shows advantages in more accurately locating anomalous areas and suppressing clutter interference. This validates that the Multiple Time-Gating Reverse Time Migration (MTG-RTM) method offers better localization and higher imaging accuracy in practical engineering applications.

关键词： Location awareness Accuracy Ground penetrating radar Transmitting antennas Stacking Radar imaging Vectors Reflection Clutter Ultra wideband technology

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Joint Vehicle Pairing, Spectrum Assignment and Power control for Sum-Rate Maximization in NOMA-Based V2X Underlaid Cellular Networks

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IEEE Internet of Things Journal 2025年

作者： Xue, Tong Zhang, Haixia Ding, Hui Yuan, Dongfeng Shandong University of Science and Technology College of Electronic and Information Engineering Shandong Qingdao266590 China Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration Shandong Jinan250061 China Shandong University School of Control Science and Engineering Shandong Jinan250061 China

Vehicle-to-everything (V2X) underlaid cellular networks in underlaid mode suffers catastrophic co-channel interference caused by spectrum sharing, results in a reduced system sum-rate. To cope with this, this work studies a social-mobility-aware non-orthogonal multiple access (NOMA)-enabled V2X underlaid cellular network to mitigate the co-channel interference and improve the sum-rate. By jointly optimizing vehicle pairing and resources, a sum-rate maximization problem is formulated under the diverse quality of service requirements of both cellular and vehicular users. The formulated problem is proved to be a non-deterministic polynomial-time (NP)-hard problem, and is difficult to solve. As an alternative, we propose a NOMA-based joint vehicle pairing, spectrum assignment and power control algorithm (NOMA-JVP-SA-PCA), with which the original problem is decomposed into two disjoint subproblems, i. e., 1) joint vehicle pairing and spectrum assignment subproblem, and 2) power control subproblem. Dealing the first subproblem, we propose a heuristic social-mobility-aware vehicle pairing algorithm (HSMA-VPA) and a revised Kuhn-munkres-based spectrum assignment algorithm (KM-SAA) to acquire the vehicle pairing and spectrum assignment solutions. Then, solving the second subproblem, a closed-form power solution is obtained utilizing a three-dimensional geometric power control approach (3D-PCA). Finally, we solve the original problem through an iterative method. Simulation results show that the proposed NOMA-JVP-SA-PCA effectively enhances the sum-rate and outperforms the baseline algorithms around 24%-53% within a specific range. © 2014 IEEE.

关键词： Polynomial approximation

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Obstacle Avoidance Based on Deep Reinforcement Learning and Artificial Potential Field

Obstacle Avoidance Based on Deep Reinforcement Learning and ...

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International Conference on control, Automation and Robotics (ICCAR)

作者： Haoran Han Zhilong Xi Jian Cheng Maolong Lv School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Air Traffic Control and Navigation College Air Force Engineering University Xian China

Obstacle avoidance is an essential part of mobile robot path planning, since it ensures the safety of automatic control. This paper proposes an obstacle avoidance algorithm that combines artificial potential field with deep reinforcement learning (DRL). State regulation is presented so that the pre-defined velocity constraint could be satisfied. To guarantee the isotropy of the robot controller as well as reduce training complexity, coordinate transformation into normal direction and tangent direction is introduced, making it possible to use one-dimension controllers to work in a two-dimension task. Artificial potential field (APF) is modified such that the obstacle directly affects the intermediate target positions instead of the control commands, which can well be used to guide the previously trained one-dimension DRL controller. Experiment results show that the proposed algorithm successfully achieved obstacle avoidance tasks in single-agent and multi-agent scenarios.

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Potential Transmission Choice for Internet of Things(IoT):Wireless and Batteryless communications and Open Problems

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China communications 2021年第2期18卷 241-249页

作者： Zhan Xu Guanjie Hu Minzheng Jia Lan Dong School of Information and Communication Engineering and with Key Laboratory of Modern Measurement&Control Technology Ministry of EducationBeijing Information Science&Technology UniversityChina Beijing Key Lab of Transportation Data Analysis and Mining School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijingChina Department of Information Engineering Beijing Polytechnic CollegeBeijingChina

The 5th generation mobile communications aims at connecting everything and future Internet of Things(IoT)will get everything smartly *** realize it,there exist many *** key challenge is the battery problem for small devices,such as sensors or *** backscatter,also referred to as or battery-free backscatter,is a new potential technology to address this *** early and typical type of batteryless backscatter is ambient ***,batteryless backscatter utilizes environmental wireless signals to enable battery-free devices to communicate with each *** devices first harvest energy from ambient wireless signals and then backscatter these signals so as to transmit their own *** paper reviews the current studies about batteryless backscatter,including various backscatter schemes and theoretical works,and then introduces open problems for future research.

关键词： batteryless backscatter battery-free channel state information(CSI) channel estimation multiple antennas signal detection symbiotic communication

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Intelligent Mobile Power Sharing and Rental System Based on BeiDou Navigation 2

Intelligent Mobile Power Sharing and Rental System Based on ...

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2nd International Conference on Electronic Information technology and Smart Agriculture, ICEITSA 2022

作者： Hu, Hui Li, Xinhu Wu, Jiaxin Zeng, Qingli Zhang, Renmin College of Communication and Electronic Engineering Jishou University Hunan Jishou416000 China Hunan Provincial Key Lab. of Intelligent Control Technology for Ecological Agriculture in Wuling Mountain Area Huaihua University Hunan Huaihua418000 China

ISBN: (纸本)9781665464017

Aiming at the problems that the mobile power supply in the market cannot output AC voltage and manually adjustable DC voltage at the same time, expensive and cannot be accurately located, an intelligent mobile power sharing and rental system based on BeiDou navigation is designed. The device mainly consists of three major parts: power circuit module, controller module and mobile APP, which solves a series of problems such as location finding, rental use, visual power management and positioning return of shared power by users. The power circuit module converts the battery voltage into 220V AC output and adjustable DC precision output. The controller module realizes the functions of power supply DC output voltage collection, Battery power Detection, Beidou positioning and data uploading to the server. The mobile APP realizes the functions such as displaying the location, voltage, power and other data of multiple power supply units and sharing the rental under the shared rental mode by the APP. The system realized in this paper can greatly promote the development of China's shared energy storage industry and effectively change the current dilemma that people have difficulties in using electricity outdoors. © 2022 IEEE.

关键词： Digital storage

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PreAdaptFWI: Pretrained-Based Adaptive Residual Learning for Full-Waveform Inversion Without Dataset Dependency

arXiv

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arXiv 2025年

作者： Dong, Xintong Yuan, Zhengyi Lin, Jun Dong, Shiqi Tong, Xunqian Li, Yue College of Instrumentation and Electrical Engineering Jilin University Changchun130026 China Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Northeast Electric Power University Jilin132012 China College of Communication Engineering Jilin University Changchun130012 China

Full-waveform inversion (FWI) is a method that utilizes seismic data to invert the physical parameters of subsurface media by minimizing the difference between simulated and observed waveforms. Due to its ill-posed nature, FWI is susceptible to getting trapped in local minima. Consequently, various research efforts have attempted to combine neural networks with FWI to stabilize the inversion process. This study presents a simple yet effective training framework that is independent of dataset reliance and requires only moderate pre-training on a simple initial model to stabilize network outputs. During the transfer learning phase, the conventional FWI gradients will simultaneously update both the neural network and the proposed adaptive residual learning module, which learns the residual mapping of large-scale distribution features in the network’s output, rather than directly fitting the target mapping. Through this synergistic training paradigm, the proposed algorithm effectively infers the physically-informed prior knowledge into a global representation of stratigraphic distribution, as well as capturing subtle variations in inter-layer velocities within local details, thereby escaping local optima. Evaluating the method on two benchmark models under various conditions, including absent low-frequency data, noise interference, and differing initial models, along with corresponding ablation experiments, consistently demonstrates the superiority of the proposed approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Stratigraphy

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Fault Classification Method of Information System Based on Cascaded Convolutional Neural Network Model 5

Fault Classification Method of Information System Based on C...

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5th IEEE International Conference on Information Systems and Computer Aided Education, ICISCAE 2022

作者： Ou, Ruiting Chen, Shuo Qiao, Lin Liu, Ziang Cheng, Xu Qiao, Yongfeng State Grid Liaoning Information and Communication Company Shenyang China College of Computer Science and Engineering Northeastern University Shenyang China College of Economics and Management Shenyang Agricultural University Shenyang China Dandong Dongfang Measurement & Control Technology Co. Ltd Dandong China

ISBN: (数字)9781665481229

ISBN: (纸本)9781665481229

Artificial intelligence plays an important role in data anomaly detection, which can significantly improve the speed and accuracy of fault diagnosis and detection in power information system. With its powerful fitting ability, deep learning begins to emerge in various fields, especially in the field of da-ta anomaly detection, which can also use deep learning methods. Due to various types and similar features of power data sets, fault classification cannot be carried out accurately. At present, frequent detection of text information needs to be performed manually. To solve this problem, this paper proposes a fault classification method for information system based on cascaded convolutional neural network model. BERT deep learning network is used for model training, and a cascaded classification network is constructed. Multiple BERT models are called hierarchically to achieve the fault detection effect of hierarchical classification. The method presented in this paper improves the accuracy of fault prediction and classification, which also provides fast, accurate and reliable decision information basis for improving fault diagnosis. © 2022 IEEE.

关键词： Failure analysis

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A data-driven control method for vehicle lateral stability in snow and ice-rutted conditions under aperiodic sampling

A data-driven control method for vehicle lateral stability i...

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Conference on Vehicle control and Intelligence (CVCI)

作者： Xu Zhao Jingzheng Guo Lianbing Wang Hongyan Guo Zhenze Liu Yiming Zhang College of Communication Engineering Jilin University Changchun China Department of Aircraft Control and Information Engineering Jilin Institute of Chemical Technology Changchun China State Key Laboratory of Automotive Simulation and Control and the College of Communication Engineering Jilin University Changchun China SAIC Motor R&D Innovation Headquarter Jilin University Shanghai China

Designing a controller to ensure vehicle stability under snow and ice-rutted road conditions, in the absence of accurate model knowledge and with potential sampling data delay, poses a significant challenge. To address this, this paper presents a novel data-driven parametric approach that integrates linear matrix inequalities (LMIs) methods. This approach allows for the determination of an appropriate sampling time interval. By iteratively solving LMIs, a state-feedback controller is derived, which guarantees both system stability and robustness. The effectiveness of the proposed approach is demonstrated through numerical simulations conducted on the MATLAB/CarSim platform. The results highlight the good control performance and robustness achieved by the proposed method.

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Artificial Intelligence-Aid OTFS Receiver: Design and Performance Evaluation

Artificial Intelligence-Aid OTFS Receiver: Design and Perfor...

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Ubiquitous communication (Ucom), International Conference on

作者： Chenxi Wang Li Ping Qian Huijie Zhu College of Information Engineering Zhejiang University of Technology Hangzhou China Science and Technology on Communication Information Security Control Laboratory The 36th Research Institute of China Electronics Technology Jiaxing China

OTFS technology is attracting much attention due to its strong adaptability to high-speed mobility and high spectrum efficiency. Studies show that applying deep learning (DL) to traditional communication systems significantly improves their performance. To this end, this paper proposes an AI-aid OTFS receiver to enhance the performance of OTFS systems in high-speed mobile communication scenarios. The proposed receiver employs two networks - a channel estimation network called TFNet and a decoding network called RecovNet. In the channel estimation module, TFNet performs more accurate channel estimation based on the results of traditional channel estimation methods. In the decoding module, RecovNet decodes the signal based on the equalized signals. To validate the superiority of our proposed receiver, we conduct comparative testing with a traditional receiver under the integer Doppler channel. The experimental results demonstrate that integrating DL technology in OTFS systems effectively enhances their performance.

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