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Model-Driven Deep Learning-Based Optimization of Downlink Precoding and Fronthaul Compression in Cell-Free MIMO systems

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IEEE Transactions on Network Science and engineering 2025年第3期12卷 1804-1817页

作者： Chen, Yijie Xia, Wenchao Cai, Shu Zheng, Gan Zhu, Hongbo Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Wireless Communications and Internet of Things Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education Nanjing210003 China Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Intelligent Information Processing and Communication Technology Nanjing210003 China

Cell-free multiple-input multiple-output (MIMO) system is a promising solution for wireless communications. However, the potential in improving system performance is restricted due to the capacity-limited fronthaul links. To tackle this problem, we utilize the compress-and forward approach, which leverages various compression and encoding techniques to reduce the fronthaul overhead. Thus, jointly optimizing precoding and fronthaul compression presents a significant challenge. However, current optimization algorithms frequently entail high computational complexity because of their iterative processes, rendering impractical for real-time use. To overcome this challenge, we present a model-driven deep learning-based framework to characterize the structure of precoding vectors and the compression matrix via low-dimensional parameters, leveraging the uplink-downlink duality as the expert knowledge. A deep neural network can learn these low-dimensional "key features" from channel state information. Simulation results indicate that our approach achieves performance comparable to the optimal algorithm and substantially reduces complexity. © 2013 IEEE.

关键词： Channel state information

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Fast resistivity imaging of transient electromagnetic using an extreme learning machine

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Soft Computing 2025年第2期29卷 1121-1131页

作者： Li, Ruiyou Zhang, Yong Li, Guang Li, Ruiheng Hu, Jia Li, Min School of Software and Internet of Things Engineering Jiangxi University of Finance and Economics NanChang330013 China School of Geophysics and Measurement Control Technology East China University of Technology NanChang330013 China School of Information Engineering Hubei University of Economics Wuhan430205 China The State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing University Chongqing400044 China

The transient electromagnetic method (TEM) is widely used in geophysical exploration. In TEM data interpretation, nonlinear inversion plays an important role. However, traditional TEM nonlinear inversion adopts the OCCAM imaging method, which merely presents the approximate shape of the stratum model, with poor inversion accuracy and much iteration time. To solve the above problems, a novel nonlinear inversion approach based on extreme learning machine (ELM) is proposed in this paper. The ELM is required to establish the input–output mapping relationship of the inversion network only once through the analytical method, which is different from the traditional neural network method that demands iterative gradient learning and is prone to fall into the local optimum. Moreover, the ELM inversion network by randomly assigning the hidden layer parameters is capable of mapping the observed TEM data and quickly producing resistivity images, which avoids time-consuming iteration and inversion calculations. The presented approach is applied to both synthetic and field examples. The results show that compared with the traditional nonlinear inversion algorithms (BP and OCCAM), the proposed method achieves better inversion accuracy and significantly reduces the calculation time, which verifies the effectiveness of the ELM algorithm for TEM data interpretation. Additionally, the research provides a new method and technology for TEM data inversion. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Electromagnetic transients

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An active power control of DFIG-based wind turbine generators for frequency regulation with expected dynamic performance

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Applied Energy 2025年 391卷

作者： Zhao, Teyang Liu, Hui Wang, Ni Su, Jinshuo Luo, Zhiqiang Ma, Siyu School of Electrical Engineering Guangxi University Nanning530004 China Guangxi Key Laboratory of Power System Optimization and Energy Technology Guangxi University Nanning530004 China The State Key Laboratory of Internet of Things for Smart City and Department of Electrical and Computer Engineering University of Macau 519000 China

The escalating wind power penetration presents a substantial challenge in ensuring the system frequency stability. Although wind turbine generators (WTGs) can offer rapid frequency support by the presetting power trajectory response (PPTR), it will result in a secondary frequency dip (SFD) while facing the demand of restoring rotor speed after frequency regulation. Moreover, the frequency overshoot will occur due to the fixed power increment provided by PPTR under minor disturbances. In this paper, an active power control strategy of DFIG-based WTGs is proposed for eliminating frequency overshoot and mitigating SFD while providing power support for the system frequency. A rotor-speed-based power increment and power attenuation trajectory are designed to enhance the primary frequency nadir while ensuring the operation stability of WTGs. After that, SFD will be mitigated by the proposed parabolic-formed compensation power. Moreover, a power increment control mode is proposed to eliminate the frequency overshoot by a membership-function-based global controller, which can smoothly switch the combined inertial response and the improved PPTR to provide a flexible power increment. Finally, the validity of the proposed strategy in improving frequency nadir, eliminating frequency overshoot, and mitigating SFD are verified by simulations under different wind power penetration levels, disturbances, and wind conditions. © 2025

关键词： Frequency stability

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Solo Adaptation for Distributed Synchronization With Application to Event-Triggered Linear Multi-Agent systems

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IEEE Transactions on Automatic Control 2025年第6期70卷 4225-4232页

作者： Chen, Ci Zheng, Zhiyang Lewis, Frank L. Xie, Kan Xie, Shengli Guangdong University of Technology Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education School of Automation Guangzhou510006 China Guangdong University of Technology School of Automation Guangdong Guangzhou510006 China The University of Texas at Arlington UTA Research Institute Fort Worth76118 United States Guangdong University of Technology 111 Center for Intelligent Batch Manufacturing Based on IoT Technology School of Automation Guangzhou China Guangdong University of Technology Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing Ministry of Education School of Automation Guangzhou510006 China

We study a solo adaptation-based protocol design problem for distributed synchronization of event-triggered linear multi-agent systems (MAS) over general directed graphs. We show that a solo adaptive gain, a locally static control gain matrix, and a distributed state measurement are sufficient for obtaining adaptive distributed synchronization of linear MAS over directed graphs without any prior knowledge of the global communication network. We then extend the solo adaptation structure to formulate an event-triggered synchronization control protocol. The main feature of our result is that the time for triggering a subsequent event is now computed using the distributed event-triggered state measurement, rather than the continuous-time state measurement. © 1963-2012 IEEE.

关键词： Continuous time systems

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Kernel Bayesian tensor ring decomposition for multiway data recovery

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Neural Networks 2025年 189卷 107500页

作者： Huang, Zhenhao Zhou, Guoxu Qiu, Yuning Chen, Xinqi Zhao, Qibin School of Automation Guangdong University of Technology Guangzhou510006 China Key Laboratory of Intelligent Detection and the Internet of Things in Manufacturing Ministry of Education Guangdong University of Technology Guangzhou510006 China RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangdong University of Technology Guangzhou510006 China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China

Tensor ring (TR) decomposition has emerged as the prevailing method for tensor completion. Earlier approaches have situated TR decomposition within a probabilistic framework, yielding satisfactory outcomes. However, these methods ignore side information or are inherently incapable of leveraging it. In response to this challenge, we propose a variational inference-based kernel Bayesian TR (VKBTR) method that integrates side information, low-rankness, and sparse learning. By incorporating kernel matrices into the TR factors, we can effectively leverage the intrinsic properties of the data (e.g., the smoothness in images and videos) to improve performance across different tasks. Additionally, by introducing a sparsity-inducing hierarchical prior on the latent factors, the proposed method enables automatic selection of the TR rank. Leveraging the variational inference algorithm enables us to achieve the update of posterior parameters effectively. Extensive experiments conducted on synthetic data, color images, face images, and color video data have shown that, with the assistance of side information, VKBTR significantly improves performance in completion tasks compared to other state-of-the-art methods. © 2025 Elsevier Ltd

关键词： Variational techniques

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Temporal-Spatial Scheduling of Energy and Computation Resources for Charging and Computing Service Vehicles

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IEEE internet of things Journal 2025年

作者： Rong, Shichu Deng, Xiongtian Zhong, Weifeng Huang, Xumin Kang, Jiawen Xie, Shengli Yuen, Chau Guangdong University of Technology School of Automation Key Laboratory of Intelligent Detection and Manufacturing Internet of Things Ministry of Education Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration Guangzhou510006 China Guangdong University of Technology School of Automation 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Nanyang Technological University School of Electrical and Electronic Engineering Singapore639798 Singapore

The growing adoption of electric vehicles (EVs) and expansion of internet-of-things (IoT) in-vehicle applications enhance vehicle intelligence and connectivity but also drive higher demand for both charging and computing services. Charging and Computing Stations (CCSs), integrating bidirectional chargers and edge computing servers and allowing optimal joint energy-computation management, has been taken as an effective solution to address this demand. This paper introduces a new concept called charging and computing service vehicle (CCSV) fleets, which are equipped with high-capacity batteries and edge servers, serving as mobile resources to support the stationary CCSs at different locations in a wide area. We propose a two-timescale model integrating temporal-spatial scheduling, charging/discharging management, and computation task offloading of the CCSV fleets. Our goal is to minimize the total system cost by optimizing the energy-computation coordination between the mobile CCSV fleets and the stationary CCSs. We construct an extended time-space network (TSN) with congestion nodes, providing a clearer depiction of the time-varying congestion conditions in the traffic network. For practical implementation, we develop a heuristic based on the convex-concave procedure (CCP) and penalty alternating direction method (PADM) to solve the problem quickly. Simulation results in a traffic network based on Guangzhou city demonstrate that the proposed model effectively leverages the mobility and multi-dimensional resources of the CCSV fleets to reduce the system cost significantly. © 2014 IEEE.

关键词： Traffic congestion

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Dynamic event-triggered mechanism-based design of H_∞ reliable state estimator for discrete-time neural networks

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International Journal of systems Science 2025年

作者： Guo, Siyu Luo, Yuqiang Zhu, Kaiqun Lin, Hong Department of Control Science and Engineering University of Shanghai for Science and Technology Shanghai China Informatization Office University of Shanghai for Science and Technology Shanghai China Shanghai Key Lab of Modern Optical System Department of Control Science and Engineering University of Shanghai for Science and Technology Shanghai China State Key Laboratory of Internet of Things for Smart City University of Macau Macao Institute of Intelligence Science and Engineering Shenzhen Polytechnic University Shenzhen China

In this paper, we investigate the reliable (Formula presented.) state estimation problem for discrete-time neural networks, where the activation function is assumed to satisfy a prescribed nonlinearity condition. To alleviate communication burden and save transmission energy, a dynamic event-triggered mechanism (DETM) is employed in the communication procedure. Considering the possible failures experienced by the data receiving register in engineering practice, a failure matrix is introduced to characterise the real received measurements. Initially, based on the obtained measurements, we design a full-order state estimator for the target neural networks. Through introducing new vectors of error and augmentation, the state estimation error dynamics is established. Then, by applying Lyapunov stability theory, matrix theory, nonlinearity analysis, and the relevant lemmas, both the asymptotical stability and the (Formula presented.) performance of the augmented error dynamics are thoroughly analysed, and the corresponding criteria are derived. Next, the target estimator parameter is determined based on the analysis of stability and disturbance attenuation. The effectiveness of the proposed estimation approach is demonstrated through a numerical example. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： DETM failure matrix H∞ performance measurement failures Neural network nonlinearity function reliable estimation stability analysis state estimation

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Research on wireless sensor privacy data measurement and classification model based on IoT technology

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International Journal of Information and Communication technology 2023年第2期22卷 185-198页

作者： Wang, Jianye Zhuang, Chunsheng Liu, Xin Wu, Shunli Wang, Ding An, Haoping Institute of Applied Physics Henan Academy of Sciences Zhengzhou450008 China Henan Key Laboratory of Sensing Technology and System for Internet of Things Zhengzhou450008 China Henan Automation Engineering and Technology Research Centre Zhengzhou450008 China

Traditional wireless sensor privacy data measurement and classification process has the problems of low accuracy and very time-consuming. A new wireless sensor privacy data measurement and classification model is proposed. The measurement module of the model establishes two levels of privacy measurement elements from three dimensions, calculates the weight of level privacy elements by using Shannon information entropy, and obtains the privacy quantity of each data privacy measurement element in the data set. With the help of internet of things technology, the privacy data measurement and classification model of wireless sensor is designed. The experimental results show that the misjudgement rate of this model is very low, less than 5%, and the misjudgement error is kept below 10%. Using this model to protect the wireless sensor privacy data, the data running cost and time cost show a low level, has a certain application value. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Data privacy

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A Survey on Deep Learning in Crop Planting 2

A Survey on Deep Learning in Crop Planting

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2nd International Symposium on Application of Materials Science and Energy Materials, SAMSE 2018

作者： Yang, Xiaofen Sun, Ming College of Information and Electrical Engineering China Agricultural University Key Laboratory of Modern Precision Agriculture System Integration Research Ministry of Education Key Laboratory of Agricultural Information Acquisition Technology Ministry of Agriculture Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture Beijing100083 China

In recent years, with the explosive growth of data, deep learning has become one of the hottest research areas in artificial intelligence. Deep learning has been widely used in many fields such as medical field, industry, transportation system, agriculture is no exception. Crop planting is a vital part of agriculture. Here, we review deep learning applications in crop planting. In addition, we discuss the challenges and future trend of deep learning in crop planting. We hope that this review could promote more researchers to apply deep learning methods in crop planting field. © Published under licence by IOP Publishing Ltd.

关键词： Crops

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Intelligent Crop Planting Management and Quality Traceability system 2

Intelligent Crop Planting Management and Quality Traceabilit...

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2nd International Symposium on Application of Materials Science and Energy Materials, SAMSE 2018

Aiming at the farmland management problems, soil heavy metal pollutions problems and the agricultural product quality security problems, the system develops a link planting, acquisition, processing, logistics and distribution each link of intelligent crop planting management and achieves quality tracing based centralized service data gateway. This system extracts the information in real time from crop planting to agricultural product sales. It is conducive to the implementation of scientific planting and the sustainable development of agriculture, and it enables consumers to trace the quality of agricultural products. The main work of this research is as follow. The first is the construction of crop planting information management subsystem. It contains the information about field information, seeding, fertilizing, irrigation, weeding, pest controlling, spraying insecticide and harvest. The second is the construction of crop planting environmental monitoring subsystem based on sensor technology. It contains real-time environmental information in the process of crop growth, such as soil temperature and humidity, air temperatures and humidity, light intensity and video information, and it realizes remote monitoring of crops. The third is agricultural product quality traceability subsystem oriented to consumers. © 2019 Institute of Physics Publishing. All rights reserved.

关键词： Crops

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