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2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Wang, Qianru Qian, Liping Wu, Yuan Yang, Xiaoniu Zhejiang University of Technology College of Information Engineering Hangzhou China University of Macau State Key Laboratory of Internet of Things for Smart City China Science and Technology on Communication Information Security Control Laboratory Jiaxing China Zhejiang University of Technology Institute of Cyberspace Security Hangzhou310023 China

ISBN: (纸本)9798350351255

With the growing demand for image transmission, there is a need for solutions that offer low energy consumption and low latency. In this paper, we present a novel relay-assisted system based on non-orthogonal multiple access (NOMA) and mobile edge computing (MEC). Our proposed system compresses images at the device end, decompresses them at either a relay or a cloud server (CS). The primary objective is to minimize system energy consumption under given task delay constraints. Considering that this is a non-convex optimization problem, we solve it by decomposing it into a continuous subproblem and a discrete subproblem. To solve the continuous subproblem, we convexify it by introducing new parameters and change variables to get the optimal the computing power of devices, relay and CS, sampling rate of devices, transmission power of devices and relay. To solve the discrete subproblem, we propose a cross-entropy (CE) algorithm to obtain the optimal decompression decision and subcarrier allocation decision. Simulation results demonstrate the accuracy and effectiveness of our algorithm in optimizing total energy consumption compared to the Linear Interactive and General Optimizer (LINGO) and frequency division multiple access (FDMA) methods. © 2024 IEEE.

关键词： Convex optimization

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Signals of Opportunity Navigation Using LTE Downlink Signals 22

Signals of Opportunity Navigation Using LTE Downlink Signals

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22nd IEEE International Conference on communication technology, ICCT 2022

作者： Wang, Yiteng Zhu, Huijie Liang, Tianyi Qian, Jiachuan The 36th Research Institute of China Electronics Technology Group Corporation Science and Technology on Communication Information Security Control Laboratory Jiaxing China

ISBN: (纸本)9781665470674

For the global navigation satellite system (GNSS) cannot meet the positioning requirements under occlusion or malicious interference, this paper proposes a receiver self-localization method using long-term evolution (LTE) downlink signals. The method is based on the multi-station time difference location mechanism. The signal acquisition of multi-base stations (BSs) is realized by signal demodulation. The transmission time difference calibration is used to solve the asynchronous problem among BSs. The synchronous signal and cell-specific reference signal are comprehensively detected to extract high-precision time difference parameters. The receiver can calculate and obtain its own position by using these timing parameters. Finally, a software-defined receiver is built to carry out experiments in the outdoor environment. Experimental results show that the positioning distance and moving direction are basically consistent with the actual situation. The root mean square error (RMSE) is less than 5m, which fully verifies the feasibility of LTE as an opportunity signal for navigation. © 2022 IEEE.

关键词： Navigation

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Congestion control of Multi-layer Cellular Networks based on Modeling of Transmit Power 3

Congestion Control of Multi-layer Cellular Networks based on...

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2020 3rd International Conference on Modeling, Simulation and Optimization Technologies and Applications, MSOTA 2020

作者： Dai, Jiahao Chen, Yongqun Chen, Yucheng Meng, Aodong Science and Technology on Communication Information Security Control Laboratory China China Electronic Group Corporation NO.36 Institute Zhejiang Jiaxing China

The explosive growth of network traffic and the limited storage node resources make the existing network architecture bear great pressure, and network congestion appears in work and life from time to time. In order to ensure the timeliness and effectiveness of the network, we propose a congestion control method based on transmission power. Different from previous work, we built a multi-layer cellular network consisting of multiple macro stations, small base stations and mobile terminals. On this basis, we establish a congestion control model based on the mixed queuing model of service window and calculate the input/output rate and congestion. Finally, we use simulation to verify the effectiveness of the congestion model. The network builder can use our model to set the appropriate power of macro stations and small base stations to ensure network stability. © Published under licence by IOP Publishing Ltd.

关键词： Base stations

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Blind identification of MIMO Space-Time Block Codes Based on Convolutional Neural Network 21

Blind identification of MIMO Space-Time Block Codes Based on...

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Proceedings of the 7th International Conference on communication and information Processing

作者： Conglin Pan Si Chen Huijie Zhu Wei Wu Jiachuan Qian Lijun Wang Science and Technology on Communication Information Security Control Laboratory No.36 Research Institute of CETC China and School of Electronic and Optical Engineering Nanjing University of Science and Technology China Science and Technology on Communication Information Security Control Laboratory No.36 Research Institute of CETC China

ISBN: (纸本)9781450385190

Aiming at the blind identification of space-time block codes (STBC) in multiple input multiple output (MIMO) system, this paper proposes a new convolutional neural network (CNN-N) to realize the blind identification. Compared to traditional algorithms using statistical features of received signal, CNN-N can reduce the computation with a higher correct identification rate. Consist of multiple layers with special functions, CNN-N has good generalization ability in complex MIMO channels. The network in this paper can recognize 6 STBC codes include spatial multiplexing signal (SM) and some OSTBC codes. The simulation result shows that this new convolutional neural network can finish STBC identification with a high correct rate even in low SNR by consuming moderate amounts of time.

关键词： Generalization Ability Convolutional Neural Network Blind Identification STBC MIMO

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Target-Before-Detect Algorithm for Passive MIMO Radar Based on Improved Particle Filter

Target-Before-Detect Algorithm for Passive MIMO Radar Based ...

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2022 Global Conference on Robotics, Artificial Intelligence and information technology, GCRAIT 2022

作者： Zhou, Yawen Zhu, Jiawei Song, Tingsong Yao, Siyi Lai, Rui Guo, Yuning School of Electronic Science and Technology University Chengdu China Information Security Control Laboratory Science and Technology on Communication Jiaxing China

ISBN: (数字)9781665481922

ISBN: (纸本)9781665481922

In order to improve the target detection performance of passive multiple-input-multiple-output (PMR) radar system, a Target-Before-Detect (TBD) algorithm based on improved particle filter is proposed in this paper. As a classical TBD algorithm, particle filter algorithm can realize the gain accumulation of target signal in time domain and effectively improve the performance of target detection. Although this algorithm has performed well in passive single-input-single-output (SISO) radar (PSR) systems, it cannot be directly applied to PMR systems. Therefore, an improved particle filtering algorithm based on passive MIMO radar is proposed in this paper. This method takes advantage of the PMR spatial accumulation and proposes a novel particle weight calculation method, which uses the false alarm probability distribution of detector to calculate the weight of the particle, so as to obtain the gain accumulation of the target in time and space. The simulation results show that the detection probability of the proposed algorithm can reach 90% even when the target-path signal-To-noise ratio (SNR) is low. Meanwhile, the good detection performance of the proposed algorithm is verified by comparing with the single frame detection results. © 2022 IEEE.

关键词： Probability distributions

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General Simultaneous Localization and Mapping Scheme for mmWave communication Systems

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IEEE Internet of Things Journal 2024年第12期11卷 22521-22536页

作者： Du, Tao Yang, Jie Wen, Chao-Kai Xia, Shuqiang Jin, Shi Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Southeast University Key Laboratory Of Measurement And Control Of Complex Systems Of Engineering Ministry Of Education The Frontiers Science Center For Mobile Information Communication And Security Nanjing210096 China Institute Of Communications Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan Zte Corporation The State Key Laboratory Of Mobile Network And Mobile Multimedia Technology Shenzhen518055 China Southeast University National Mobile Communications Research Laboratory The Frontiers Science Center For Mobile Information Communication And Security Nanjing210096 China

Utilizing high-resolution antenna arrays and wide bandwidth of the millimeter-wave (mmWave) spectrum in 5G new radio (NR) mmWave communication systems holds the potential for high-throughput data transmission while enabling user localization and environmental mapping. However, the majority of existing simultaneous localization and mapping (SLAM) algorithms rely on methods akin to the extended Kalman filter for generating initial map features. These methods prove ineffective when the measurement dimension is insufficient. Furthermore, there is a notable absence of research exploring mmWave prototype systems to evaluate and compare the performance and viability of various SLAM algorithms. To address these challenges, we propose an innovative probability hypothesis density (PHD) generation scheme for birth events and have developed a prototype system. Our approach, referred to as PHD-SLAM, exhibits remarkable effectiveness even in scenarios where the measurement dimension falls short of map features. This means it can function seamlessly with only delay or angle information available. Additionally, we have designed a 28GHz mmWave beam scanning prototype system that leverages the 5G NR frame for accomplishing SLAM algorithms. Following this, we conducted extensive simulations and experimental evaluations to gauge the performance of several leading-edge SLAM algorithms under diverse mmWave circumstances, encompassing PHD-based and belief propagation (BP) SLAM algorithms. Our analysis reveals that both PHD and BP SLAM can achieve agent localization precision within a decimeter and mapping precision within a meter, capitalizing on the angle parameters of mmWave signals. While BP SLAM showcases reduced computational demand, its estimation precision is marginally inferior to that of PHD-SLAM. © 2014 IEEE.

关键词： Belief propagation

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Geomagnetic Matching Combined Navigation Based on Improving Operation Efficiency of DTW Algorithm 15

Geomagnetic Matching Combined Navigation Based on Improving ...

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15th IEEE International Conference on Electronic Measurement and Instruments, ICEMI 2021

作者： Huo, Gang Shang, Junna Zhang, Dongli Wang, Yiteng Institute of China Electronics Technology Group Corporation Hangzhou Dianzi University Science and Technology on Communication Information Security Control Laboratory No.36 Research Zhejiang HangZhou China

ISBN: (纸本)9781665444910

Aiming at the problem of 'singularity' generated by the traditional DTW matching algorithm, the DDTW matching algorithm is used to match the geomagnetic sequence, and the first derivative of the sequence is compared for matching, and the Itakura window is used as a constraint to restrict the search range. In order to improve the overall positioning effect, a combination of inertial navigation and geomagnetism is used for navigation, and Kalman filtering is used to fuse the positioning results. Experimental results show that when the matching window is 60, the average positioning error of the system using the improved DDTW matching algorithm is within 1.37m. Compared with the system using the DTW matching algorithm, the average positioning error is reduced by 0.59m and effectively improved Computing efficiency. © 2021 IEEE.

关键词： Navigation

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Multi-scale network for single image deblurring based on ensemble learning module

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Multimedia Tools and Applications 2025年第11期84卷 9045-9064页

作者： Wu, WenBo Pan, Yun Su, Na Wang, JingTao Wu, ShaoChuan Xu, ZeKun Yu, YouJian Liu, YaPeng State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China School of Computer and Cyberspace Security Communication University of China Beijing100024 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China College of Computer and Control Engineering Northeast Forestry University Harbin150040 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China School of Computer Science and Technology Anhui University Hefei230601 China

In this paper, we have identified two primary issues with current multi-scale image deblurring methods. On the one hand, the blurring scale is ignored. On the other hand, the context information of images is not fully utilized, and the spatial detail information of images is lost. To better solve the above problems, on top of that, this paper proposes a multi-scale network for single image deblurring. The designed network contains two parts: 1) Ensemble Learning Model: The Weak Learning Model in this module connects multiple weak learners in series to increase network depth. Moreover, through multi-scale feature ensemble module, the output features and intermediate features of different weak learner are integrated, and finally strong learner is used to enhance the features with semantic information at different scales;2) Spatial Feature Enhancement Module: This module can preserve spatial detail information of images and compensate for the disadvantages of losing image spatial details due to continuous downsampling in the Ensemble Learning Model structure. Our proposed deblurring model has been extensively evaluated on several benchmark datasets, and the model achieves superior performance compared to the most advanced deblurring methods. In particular, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values are 33.57 dB and 0.965 on the GoPro dataset, respectively, with PSNR and SSIM improving by 0.49 dB and 0.002 over NAFNet, and 0.65 dB and 0.004 over Restormer. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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Linearized microwave downconversion link based on fast and intelligent impairment equalization for noncooperative systems

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Chinese Optics Letters 2023年第2期21卷 143-147页

作者：陈智宇钟欣蒋林徐嘉鑫刘静娴盘艳周涛 Key Laboratory of Science and Technology on Electronic Information Control Chengdu 610029China Center for Information Photonics&Communications School of Information Science&TechnologySouthwest Jiaotong UniversityChengdu 610031China Science and Technology on Communication Security Laboratory Institute of Southwestern CommunicationChengdu 610041China

We experimentally demonstrated the use of intelligent impairment equalization(IIE)for microwave downconversion link linearization in noncooperative *** an equalizer is realized based on an artificial neural network(ANN).Once the training process is completed,the inverse link transfer function can be *** the inverse transformation for the detected signal after transmission,the third-order intermodulation distortion components are suppressed significantly without requiring any prior information from an input RF ***,fast training speed is achieved,since the configuration of ANN-based equalizer is *** results show that the spurious-free dynamic range of the proposed link is improved to 106.5 dB·Hz^(2/3),which is 11.3 dB higher than that of a link without ***,the training epochs reduce to only five,which has the potential to meet the practical engineering requirement.

关键词： microwave photonics linearized downconversion link intelligent impairment equalization artificial neural network

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Ultra-Wideband Spectrum Monitoring Using Software Defined Radio 22

Ultra-Wideband Spectrum Monitoring Using Software Defined Ra...

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22nd IEEE International Conference on communication technology, ICCT 2022

作者： Zhu, Huijie Chen, Binhui Wang, Yiteng Wang, Lijun Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China The 36th Research Institute of China Electronics Technology Group Corporation Jiaxing314033 China

ISBN: (纸本)9781665470674

This paper presents an ultra-wideband spectrum monitoring system using a software defined radio platform. The system has been developed by using an Ettus X310 universal software radio peripheral (USRP) for data acquisition and a laptop for displaying the graphical user interface. The system can operate in two modes with two different daughter boards installed. One of the operation modes allows simultaneously monitoring up to two different channels, each with a maximum sampling bandwidth of 200 MHz, or simultaneously monitoring up to four different channels, each with a maximum sampling bandwidth of 100 MHz. The other operation mode allows monitoring a single channel with a maximum sampling bandwidth exceeding 300 MHz. Furthermore, unlike the widely adopted GNU Radio software, the proposed system software is friendly to the Microsoft Windows operating system. © 2022 IEEE.

关键词： Bandwidth

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