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Robust Transmission Design in Multiobjective RIS-Aided SWIPT IoT communications

IEEE Internet of Things Journal 2024年第10期11卷 18605-18618页

作者： Sharma, Vaibhav Allu, Raviteja Singh, Sandeep Kumar Singh, Keshav Duong, Trung Q. Tsiftsis, Theodoros A. Institute of Communications Engineering National Sun Yat-sen University Kaohsiung804 Taiwan Motilal Nehru National Institute of Technology Allahabad Electronics and Communication Engineering Department Prayagraj211004 India Memorial University of Newfoundland Faculty of Engineering and Applied Science St. John'sNLA1B 3X5 Canada Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom University of Thessaly Department of Informatics and Telecommunications Lamia35100 Greece

This work investigates the performance of simultaneous wireless information and power transfer (SWIPT) in a reconfigurable intelligent surface (RIS)-aided Internet of Things (IoT) communications under imperfect channel state information (CSI). We formulate a multiobjective optimization problem (MOOP) to design a transmit precoding vector (TPV) at the base station (BS) and phase shift matrix (PSM) at the RIS that jointly maximizes energy efficiency (EE) and harvested power (HP) under the norm-bounded CSI error model. Due to the conflicting objective functions and nonconvex nature of the above optimization problem, the MOOP is simplified using the ϵ-constraint method and subsequently adopting advanced optimization tools, such as the Dinkelbach method, S-procedure, general sign-definiteness, semidefinite programming and convex-concave procedure. Thereafter, we propose an alternating optimization-based algorithm which determines optimal TPV and PSM iteratively that jointly maximizes the EE and HP of the considered system. Through numerical simulations, we validate the robustness, optimality, convergence, accuracy, and effectiveness of our proposed algorithm. Furthermore, we assess the impact of several key parameters, such as the number of RIS elements, available transmit power at BS, and the minimum HP on the performance of the considered system. © 2014 IEEE.

关键词： Energy efficiency

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Method of Automated Dataset Collection for Microwave Filters Synthesis

Method of Automated Dataset Collection for Microwave Filters...

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Systems of Signals Generating and Processing in the Field of on Board communications

作者： O. V. Arinin D. M. Bakhmach A. I. Katsnelson A. S. Kurbeteva A. A. Yelizarov Moscow Technical University of Communications and Informatics Moscow Russia Electronic Engineering Department HSE University Moscow Russia Computer Engineering Department HSE University Moscow Russia

ISBN: (数字)9798331532635

ISBN: (纸本)9798331532642

This research discusses the method of dataset collection automatization for microwave filter synthesis by integrating machine learning techniques, thus reducing development time. Utilizing the 3D electromagnetic analysis software package, the study involves simulation and collecting geometric parameters and amplitude-frequency characteristics from three variants of passband highly selective microstrip tworesonator combined filters with stepped impedance resonators. In this paper, specific items and restrictions on filter topology creation in the simulation program are discussed, as well the review of the script used for dataset collection and its requirements is given. In addition, a dataset structure and format are described.

关键词： Microstrip filters Three-dimensional displays Network topology Resonator filters Machine learning Microwave communication Information filters Topology Microwave filters Optimization

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Proactive Congestion Management in Next-Generation Wireless Networks: Evaluating Traditional Approaches Against Machine Learning Models

Proactive Congestion Management in Next-Generation Wireless ...

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International Conference on Computational and Characterization Techniques in engineering & Sciences (CCTES)

作者： Johnjustin Thangaraj T. Mohanraj Caleb S J Srija Latha R Pushpalatha Rajendran Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Karunya University Department of Electronics and Communications Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

sensible congestion control approaches continue to be imperative in attaining the highest capability of the next-generation facilities for wireless networks. To this end, the objective of the present research is to establish the effectiveness of the study conducted with classical congestion management techniques to that of the machine models. The proposed work environments a novel and controlled setup of the next-generation wireless network and applies, assesses, and optimizes the traditional QoS and traffic-shaping methodologies, and generic AI forms of the supervised learning and reinforcement learning. Other standards that were utilized and incorporated in the efficient aspect included the latency, through put and packet loss with the view of evaluating efficiency levels of the employed methods. This simply measures that the use of ML is more superior to the conventional methods because it relieves latency and packet loss while at times enhancing through put. The analysis of the two states’ comparison proof that congestion recovery problems are more fit for the application of ML algorithms in modern transport systems due to better flexibility and online decision-making chances. Nevertheless, it discusses some problems that exist and they include; The major challenge of how to integrate the ML model in the system and the need to obtain massive training data. Therefore, one can state that the approach of applying more preventive ML models in controlling congestion also appears to be wiser when it comes to deploying lesions over wireless networks from the viewpoints of efficiency and scalability. Future work includes: working with real data; further work on the Advanced ML section; Security and Compliance.

关键词： Adaptation models Computational modeling Wireless networks Packet loss Production Data models Real-time systems User experience Security Next generation networking

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TapFinger: Task Placement and Fine-Grained Resource Allocation for Edge Machine Learning

TapFinger: Task Placement and Fine-Grained Resource Allocati...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and communications Societies

作者： Yihong Li Tianyu Zeng Xiaoxi Zhang Jingpu Duan Chuan Wu School of Computer Science and Engineering Sun Yat-sen University Department of Communications Peng Cheng Laboratory Department of Computer Science The University of Hong Kong

Machine learning (ML) tasks are one of the major workloads in today's edge computing networks. Existing edge-cloud schedulers allocate the requested amounts of resources to each task, falling short of best utilizing the limited edge resources flexibly for ML task performance optimization. This paper proposes TapFinger, a distributed scheduler that minimizes the total completion time of ML tasks in a multi-cluster edge network, through co-optimizing task placement and fine-grained multi-resource allocation. To learn the tasks' uncertain resource sensitivity and enable distributed online scheduling, we adopt multi-agent reinforcement learning (MARL), and propose several techniques to make it efficient for our ML-task resource allocation. First, TapFinger uses a heterogeneous graph attention network as the MARL backbone to abstract inter-related state features into more learnable environmental patterns. Second, the actor network is augmented through a tailored task selection phase, which decomposes the actions and encodes the optimization constraints. Third, to mitigate decision conflicts among agents, we novelly combine Bayes' theorem and masking schemes to facilitate our MARL model training. Extensive experiments using synthetic and test-bed ML task traces show that TapFinger can achieve up to 28.6% reduction in the average task completion time and improve resource efficiency as compared to state-of-the- art resource schedulers.

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Deep Learning Based Corona Discharge Severity Classification for High Voltage Equipment

Deep Learning Based Corona Discharge Severity Classification...

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Annual Conference of Industrial Electronics Society

作者： Maher Messaoudi Sayed Mohammad Kameli Shady S. Refaat Haitham Abu-Rub Mohamed Trabelsi Department of Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar School of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom Electronics and Communications Engineering Department Kuwait College of Science and Technology Kuwait

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

Discharges, such as partial discharges (PDs) and corona discharges (CDs) are the most common faults that occur in insulation materials used in high voltage (HV) equipment. A high repetition rate of discharge activity indicates the severity of the defects that shorten the lifetime of electrical equipment, leading to insulation failure. To solve this, this paper proposes an efficient classification technique for corona discharge defect intensity using features obtained from statistical parameters such as the ignition voltage of CDs. The Recurrent Neural Network (RNN) is proposed to identify the intensity of corona discharges. A comprehensive experimental evaluation is conducted, to demonstrate the capabilities of the proposed solution. The exceptional predictive abilities of the long short-term memory (LSTM) method are the primary benefit of the proposed approach presented, with a potential for enhancing the performance of CD detection systems. The obtained results demonstrate the accuracy of the proposed model, indicating its potential for deployment in practical applications. The innovative approaches utilized in this paper will help engineers and operators quickly determine the severity (sharpness and curvature) of the protrusions or surface defects that cause CDs, solely based on measurements of the ignition voltage.

关键词： Partial discharges Voltage measurement Surface discharges Ignition Conductors Discharges (electric) Corona Surface roughness Rough surfaces Long short term memory

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Evaluating wall installation methods for cylindrical solar cells in urban areas by luminous flux simulation 13

Evaluating wall installation methods for cylindrical solar c...

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13th IEEE Power and Energy Society: Innovative Smart Grid Technologies - Asia, ISGT Asia 2024

作者： Tamura, Fumiki Yokogawa, Shinji Hayase, Shuzi Nakamura, Masaki Hirami, Tomoyuki Hirotani, Daisuke Sono, Yuya Takenoya, Toshihiro Nomura, Daishiro Nomura, Takatoshi Hayashi, Masahiro Honma, Kento The University of Electro-Communications Graduate School of Informatics and Engineering Department of Informatics Tokyo Japan The University of Electro-Communications Info-Power Energy System Research Center Tokyo Japan Ushio Inc Marketing Division Climate Solution Department Japan Marketing Division Ushio Inc Global Warming Solution Group Climate Solution Department Japan Fujico CO. Ltd Technology Development Center Japan Ckd Corporation New Business Development Office Japan The University of Electro-Communications Informatics and Computer Engineering Tokyo Japan

ISBN: (纸本)9798350373929

In urban areas of Japan, such as Tokyo, overcrowding in condominiums and office buildings leaves limited flat land for installing solar panels, resulting in inadequate installation of renewable energy. To advance photovoltaic power generation and support Japan's goal of achieving carbon neutrality by 2050, we are exploring the use of cylindrical solar cells in these dense urban settings. Cylindrical solar cells are ideal for metropolitan installations due to their lightweight design and wind resistance. However, it is crucial to consider how installation configurations influence electricity production, especially the need to minimize shadowing from nearby buildings and other urban elements. This study utilized lighting simulation software to model the light flux based on actual installation conditions of cylindrical solar cells to identify optimal placement strategies. Additionally, this configuration was compared with traditional flat-plate solar cells under similar conditions. Findings indicate that cylindrical solar cells offer flexible installation options. Notably, employing a horizontal grid layout for cylindrical cells addresses the challenges associated with wall-mounted flat-plate cells, which suffer reduced luminous flux at higher solar altitudes. These results demonstrate that cylindrical solar cells can effectively overcome the challenges of photovoltaic installation in densely populated urban areas. © 2024 IEEE.

关键词： Apartment houses

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A Machine Learning Method for Detecting Behavior-Based Malware 5

A Machine Learning Method for Detecting Behavior-Based Malwa...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Venkatarao, Javvaji Deeban Chakravarthy, V. Ramya, Singaraju Siva Prasad, V. Cherukuvada, Srikanth Soujanya, Annaram Sree Vidyanikethan Engineering College Dept. of Computer Science and Engineering Tirupati517 102 India Srm Institute of Science and Technology Department of Computing Technologies Chennai India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Chennai India Mohan Babu University Dept. of Ai&ml Tirupati India School of Computing Srm Institute of Scienceand Technology Department of Networking and Communications Chennai India Cvr College of Engineering Department of Computer Science and Engineering Telangana India

ISBN: (纸本)9798350321425

While malware has posed a danger to businesses for years, advances in malware detection have lagged. Malware can cause damage to a system by starting unneeded services, which increases the system's workload and prevents it from operating smoothly. Malware detection can be done in one of two ways: the traditional signature-based approach or the more modern behavior-based approach. When malware is activated on a system, it conducts specific actions, such as launching malicious OS services or downloading malicious files from the web, that characterize its behaviour. The described technique detects malicious software based on its actions. The suggested model in this paper combines Support Vector Machine and Principal Component Analysis. © 2023 IEEE.

关键词： Support vector machines

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Text Analysis Based on Natural Language Processing (NLP) 2

Text Analysis Based on Natural Language Processing (NLP)

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2nd International Conference on Advances in engineering Science and Technology, AEST 2022

作者： Flayeh, Azhar Kassem Hamodi, Yaser Issam Zaki, Nashwan Dheyaa Ministry of Higher Education & Scientific Research Department of Computer Engineering & Information Technology Baghdad Iraq Ministry of Higher Education & Scientific Research Computer Engineering College of Electrical and Electronic Techniques Baghdad Iraq University of Information Technology and Communications College of Engineering Media Technology Engineering Baghdad Iraq

ISBN: (纸本)9798350334906

In the modern era of information explosion, where there is enormous information including thousands and millions of texts loaded on the Internet of over 1.2 million terabytes, there are no accurate statistics on the size of texts within this huge amount of data. Given the multiplicity of websites on the Internet and the spread of social networking services like Twitter, for example, it is stated that about 6000 tweets are posted per second in the year 2019. This work aims to analyze these texts through resorting to artificial intelligence to help solve this problem. In this article, a review is presented on the latest concepts in the field of word processing, information extraction, and implementing an interface for text analysis using the C++ programming language and Visual Studio 2015 edition. A comparison is made between two articles in terms of the number of verbs, prepositions, article strength, number of letters, with the possibility of sorting the text into verbs, nouns, object, and adjectives, as will be displayed in the program interface. © 2022 IEEE.

关键词： Sentiment analysis

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Optimizing Wireless Sensor Networks in E-Commerce with Message Passing Neural Networks 2

Optimizing Wireless Sensor Networks in E-Commerce with Messa...

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2nd IEEE Global Conference on communications and Information Technologies, GCCIT 2024

作者： Jeyaselvi, M. Saravanan, B. Boobalan, M. Ganesha, M. Lamba, Anil Kumar Suganthi, D. Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur India Sri Eshwar College of Engineering Department of Cse Tamilnadu Coimbatore India Vivekanandha College of Engineering for Women Tiruchengode Namakkal India Ajiet Department of Cse Karnataka Mangaluru India Geeta University School of Computer Science and Engineering Haryana India Saveetha College of Liberal Arts and Sciences Simats Department of Computational Intelligence Thandalam Chennai India

ISBN: (纸本)9798350388916

In today's e-commerce landscape, successful logistics management is essential for achieving success. In this post, we will go over how wireless sensor networks (WSNs) can help online stores streamline their logistics. Data collection and real-time monitoring made possible by wireless sensor networks (WSNs) increase supply chain visibility and control. The proposed approach includes both preprocessing and training the model. Part of the preprocessing phase is principal component analysis (PCA), which entails cleaning and transforming raw data into an appropriate format for analysis or model training. Throughout the training, CNN-BiGRU-BiLSTM was continuously utilized. With a 93.46% average accuracy, this innovative method surpasses the BiGRU and BiLSTM. © 2024 IEEE.

关键词： Long short-term memory

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Dual-Propagation-Feature Fusion Enhanced Neural CSI Compression for Massive MIMO

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IEEE Transactions on communications 2023年第9期71卷 5182-5198页

作者： Zhang, Shaoqing Xu, Wei Jin, Shi You, Xiaohu Ng, Derrick Wing Kwan Wang, Li-Chun Southeast University The National Mobile Communications Research Laboratory Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China University of New South Wales The School of Electrical Engineering and Telecommunications SydneyNSW2052 Australia National Yang Ming Chiao Tung University The Department of Electrical Computer Engineering Hsinchu30010 Taiwan

Due to the ability of feature extraction, deep learning (DL)-based methods have been recently applied to channel state information (CSI) compression feedback in massive multiple-input multiple-output (MIMO) systems. Existing DL-based CSI compression methods are usually effective in extracting a certain type of features in the CSI. However, the CSI usually contains two types of propagation features, i.g., non-line-of-sight (NLOS) propagation-path feature and dominant propagation-path feature, especially in channel environments with rich scatterers. To fully extract the both propagation features and learn a dual-feature representation for CSI, this paper proposes a dual-feature-fusion neural network (NN), referred to as DuffinNet. The proposed DuffinNet adopts a parallel structure with a convolutional neural network (CNN) and an attention-empowered neural network (ANN) to respectively extract different features in the CSI, and then explores their interplay by a fusion NN. Built upon this proposed DuffinNet, a new encoder-decoder framework is developed, referred to as Duffin-CsiNet, for improving the end-to-end performance of CSI compression and reconstruction. To facilitate the application of Duffin-CsiNet in practice, this paper also presents a two-stage approach for codeword quantization of the CSI feedback. Besides, a transfer learning-based strategy is introduced to improve the generalization of Duffin-CsiNet, which enables the network to be applied to new propagation environments. Simulation results illustrate that the proposed Duffin-CsiNet noticeably outperforms the existing DL-based methods in terms of reconstruction performance, encoder complexity, and network convergence, validating the effectiveness of the proposed dual-feature fusion design. © 1972-2012 IEEE.

关键词： Feature extraction

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