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Active RIS-Aided Massive MIMO With Imperfect CSI and Phase Noise

IEEE Transactions on Wireless Communications 2025年

作者： Peng, Zhangjie Zhu, Jianchen Pan, Cunhua Zhang, Zaichen Da Costa, Daniel Benevides Elkashlan, Maged Karagiannidis, George K. Shanghai Normal University College of Information Mechanical and Electrical Engineering Shanghai200234 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Interdisciplinary Research Center for Communication Systems and Sensing Department of Electrical Engineering Dhahran31261 Saudi Arabia Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Greece Artificial Intelligence and Cyber Systems Research Center Lebanon

As a recently proposed reconfigurable intelligent surface (RIS) architecture, active RIS has drawn considerable interest. The important feature of the active RIS is its ability to strengthen the impinging signals to mitigate the multiplicative fading effect inherent in passive RIS-aided systems. Herein, we explore the performance of an active RIS-aided uplink multi-user massive multiple-input multiple-output (MIMO) system, considering the phase noise at the RIS. Furthermore, a two-timescale scheme is utilized, where the base station (BS) beamforming is designed based on the instantaneous aggregated channel state information (CSI), while the statistical CSI is used for designing the phase shifts of the active RIS. In addition, the linear minimum mean square error (LMMSE) estimator is adopted to estimate the aggregated channel, which combines both the cascaded and direct channels. According to the estimated channel, a closedform expression for the lower bound of achievable rate is derived. Based on the theoretical expressions, the power scaling laws for the considered system are also investigated. Specifically, when each user’s transmit power is proportionally reduced by the quantity of BS antennas M or RIS elements N, we find that the amplified thermal noise causes the lower bound of the achievable rate to approach zero as M or N tends to infinity. Moreover, an optimization approach based on a genetic algorithm (GA) is introduced to obtain the optimal phase shifts for maximizing the achievable rate. Numerical results reveal that the active RIS can greatly enhance the performance of the massive MIMO system compared to its passive counterpart. © 2002-2012 IEEE.

关键词： Genetic algorithms

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Large-Scale Radiomics Analysis for Prostate Cancer Detection Harnessing Machine and Deep Learning Models 22

Large-Scale Radiomics Analysis for Prostate Cancer Detection...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Mylona, Eugenia Zaridis, Dimitrios I. Kalantzopoulos, Charalampos N. Pezoulas, Vasileios C. Tachos, Nikolaos S. Matsopoulos, George K. Regge, Daniele Papanikolaou, Nikolaos Tsiknakis, Manolis Marias, Kostas Fotiadis, Dimitrios I. Biomedical Research Institute Forth Ioannina GR 45110 Greece University of Ioannina Unit of Medical Technology Intelligent Information Systems Greece School of Electrical & Computer Engineering National Technical University of Athens Biomedical Engineering Laboratory Athens 15780 Greece Candiolo Cancer Institute FPO-IRCCS Department of Radiology Strada Provinciale Candiolo Turin 10060 Italy Champalimaud Foundation Computational Clinical Imaging Group Portugal Institute of Computer Science Forth Computational Biomedicine Laboratory Heraklion GR 70013 Greece Hellenic Mediterranean University Department of Electrical and Computer Engineering Heraklion GR 71004 Greece

ISBN: (纸本)9798331520526

Prostate cancer (PCa) is the second most common cancer in men, making early detection crucial for effective treatment. This study explores the efficacy of machine learning (ML) and deep learning (DL) models in detecting PCa lesions using radiomics features derived from multiparametric MRI data. Leveraging a dataset of 7621 retrospective and 2132 prospective cases from 12 clinical centers across Europe, we compared the performance of various ML (LASSO, Random Forest, SVM, XGBoost) and DL models (CNN, MLP, Transformer, CNN&MLP). Radiomic features were extracted and refined through a robust feature selection process. Models' performance was evaluated through nested cross-validation (CV) and externally. The CNN&MLP model achieved the highest ROC AUC (0.86) and PR AUC (0.90) in CV, while LASSO showed consistent generalization in external validation. Our findings indicate that DL models excel in high-dimensional data analysis but face challenges in generalizing to external datasets, highlighting the importance of external validation for clinical applicability. © 2025 IEEE.

关键词： Deep Learning Lesion Detection Machine Learning Prostate Cancer Radiomics

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Recent advances and applications of deep learning methods in materials science

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npj Computational Materials 2022年第1期8卷 548-573页

作者： Kamal Choudhary Brian DeCost Chi Chen Anubhav Jain Francesca Tavazza Ryan Cohn Cheol Woo Park Alok Choudhary Ankit Agrawal Simon J.L.Billinge Elizabeth Holm Shyue Ping Ong Chris Wolverton Materials Science and Engineering Division National Institute of Standards and TechnologyGaithersburgMD20899USA Theiss Research La JollaCA92037USA DeepMaterials LLC Silver SpringMD20906USA Material Measurement Science Division National Institute of Standards and TechnologyGaithersburgMD20899USA Department of NanoEngineering University of California San DiegoSan DiegoCA92093USA Energy Technologies Area Lawrence Berkeley National LaboratoryBerkeleyCAUSA Department of Materials Science and Engineering Carnegie Mellon UniversityPittsburghPA15213USA Department of Materials Science and Engineering Northwestern UniversityEvanstonIL60208USA Department of Electrical and Computer Engineering Northwestern UniversityEvanstonIL60208USA Department of Applied Physics and Applied Mathematics and the Data Science Institute Fu Foundation School of Engineering and Applied SciencesColumbia UniversityNew YorkNY10027USA

Deep learning(DL)is one of the fastest-growing topics in materials data science,with rapidly emerging applications spanning atomistic,image-based,spectral,and textual data *** allows analysis of unstructured data and automated identification of *** recent development of large materials databases has fueled the application of DL methods in atomistic prediction in *** contrast,advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL *** this article,we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation,materials imaging,spectral analysis,and natural language *** each modality we discuss applications involving both theoretical and experimental data,typical modeling approaches with their strengths and limitations,and relevant publicly available software and *** conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations,challenges,and potential growth areas for DL methods in materials science.

关键词： learning limitations textual

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Hypercomplex Generative Adversarial Networks for Lightweight Semantic Labeling 3rd

Hypercomplex Generative Adversarial Networks for Lightweigh...

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3rd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2022

作者： Sfikas, Giorgos Retsinas, George Gatos, Basilis Nikou, Christophoros Computational Intelligence Laboratory Institute of Informatics and Telecommunications National Center for Scientific Research "Demokritos" Athens Greece School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Department of Surveying and Geoinformatics Engineering University of West Attica Athens Greece Department of Computer Science and Engineering University of Ioannina Ioannina Greece

ISBN: (纸本)9783031090363

Following recent advances on parameterized hypercomplex multiplication [21], we explore the usefulness of hypercomplex convolutions and deconvolutions in a document labeling task. We show that the proposed Hypercomplex Generative Adversarial Networks achieve excellent results while requiring significantly less independent parameters than real-valued models. © 2022, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Retraction Note: An efficient quantum based D2D computation and communication approach for the internet of things

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Optical and Quantum Electronics 2024年第11期56卷 1-2页

作者： Mekala, M. S. Srivastava, Gautam Chun-Wei Lin, Jerry Dhiman, Gaurav Park, Ju H. Jung, Ho-Youl Department of Information and Communication Engineering Yeungnam University Gyeongsan Korea RLRC for Autonomous Vehicle Parts and Materials Innovation Yeungnam University Gyeongsan Korea Department of Math and Computer Science Brandon University Brandon Canada Western Norway University of Applied Sciences Gaurav Dhiman Bergen Norway Department of Computer Science Government Bikram College of Commerce Patiala India Department of Electrical Engineering Yeungnam University Gyeongsan Korea Research Centre for Interneural Computing China Medical University Taichung Taiwan University Centre for Research and Development Department of Computer Science and Engineering Chandigarh University Gharuan India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

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Deep Learning Techniques in Mobile Edge computing for Internet of Medical Things 3

Deep Learning Techniques in Mobile Edge Computing for Intern...

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Alawadhi, Abdulwadood Ahmad, R. Badlishah Almogahed, Abdullah Abrar, Ahmad Department of Computers and Information Technology Yemen Universiti Malaysia Perlis Faculty of Electronic Engineering and Technology Perlis Malaysia Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Parit Raja Malaysia School of Computing Universiti Utara Malaysia InterNetWorks Research Laboratory Sintok-Kedah Malaysia

ISBN: (纸本)9798350305333

The fast expansion of the Internet of Medical Things (IoMT) has resulted in a ubiquitous home health diagnostic network. High patient demand results in high costs, short latency, and communication overload. As a result, 6G is the next generation of IoT, IoMT, and cellular networks, intending to considerably improve the quality of smart healthcare services through high throughput and decreased latency. So far, adopting cloud computing for time-critical applications and decreasing access delays to resources is difficult. Deep learning has been extensively employed to extract characteristics from complicated networks as artificial intelligence technology has advanced. On the other hand, deep learning models are often run in cloud computing data centres with tremendous processing resources. Conventional cloud computing systems primarily rely on the network, which has significant latency and security and privacy issues. One of the successful solutions is the Mobile Edge computing (MEC) paradigm, which brings cloud computing services closer to the edge network and uses available resources. Mobile edge computing places computer and storage nodes near mobile devices at the Internet's edge, leading to considerable savings in system operating time, memory cost, and power usage. Deep learning is used in mobile edge computing to forecast changes in demand based on daily patient behaviours. It also prepares the network by scaling up network resources as required. This study aims to discuss IoMT and identfy the problems in deep learning for mobile edge computing technology and its applications. © 2023 IEEE.

关键词： Deep learning

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Dubins Vehicle Intercept of a Brownian Target on a Sphere

Dubins Vehicle Intercept of a Brownian Target on a Sphere

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IEEE Conference on Decision and Control

作者： Dejan Milutinović David W. Casbeer Department of Electrical and Computer Engineering University of California Santa Cruz CA USA Control Science Center Air Force Research Laboratory Wright-Patterson AFB OH USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper focuses on a navigation of a Dubins vehicle (DV) to intercept a moving target on a sphere. The uncertainty in the target motion is described by a Brownian motion model. An Itô-type stochastic differential equation for the relative motion between DV and the Brownian moving target is derived. This stochastic differential equation is used to formulate an intercept controller as a minimum time optimal controller. The properties of the controller are incorporated into the numerical computations of the controller, which by its construction can intercept a Brownian target. Our numerical simulation results show that the computed controller can intercept both stationary and moving targets on the sphere.

关键词： Space vehicles Uncertainty Planets Space debris Stochastic processes Optimal control Differential equations Satellite navigation systems Numerical simulation Numerical models

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Modeling and Optimization of a Through-Metal Acoustic Wireless Power Transfer System

Modeling and Optimization of a Through-Metal Acoustic Wirele...

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IEEE Workshop on computers in Power Electronics

作者： Kody Froehle Daniel Costinett Sarah S. Bedair Victor Farm-Guoo Tseng Department of Electrical Engineering and Computer Science The University of Tennessee Knoxville TN USA U.S. Army Combat Capabilities Development Command Army Research Laboratory USA

ISBN: (数字)9798350307238

ISBN: (纸本)9798350307245

This paper presents the design of a through-metal acoustic wireless power transfer (AWPT) system using piezoelectric transducers (PT) as transmitter (TX) and receiver (RX). An equivalent circuit model using measured impedance parameters is used to model the complete PT-to-PT electromechanical conversion. The system is designed by sweeping frequency, load resistance, and compensation design to maximize transferred power. Boundary constraints are presented based on temperature and voltage limits the PTs can withstand. An optimal design staying within the operating bounds is then chosen and tested in hardware. The designed system is fabricated and verified experimentally for varying voltage levels. The prototype system has been tested up to 160 V dc input and 41 W output.

关键词： Temperature measurement Voltage measurement Transmitters Prototypes Wireless power transfer Thermal management Acoustics

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Analysis and Treatment of Missing Data in Happiness Score Using Machine Learning

Analysis and Treatment of Missing Data in Happiness Score Us...

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International Symposium on High Capacity Optical Networks and Enabling Technologies, HONET

作者： Wassim Touati Yamen El Touati Nejib Ben Hadj-Alouane Abderrazek Jemai SERCOM Laboratory Polytechnic School of Tunisia Carthage University Tunis Tunisia Department of Computer Science Faculty of Computing and Information Technology Northern Border University Kingdom of Saudi Arabia OASIS Laboratory ENIT University of Tunis EI Manar Tunisia Electrical Engineering and Computer Engineering Department American University in Dubai Dubai UAE

ISBN: (数字)9798350378078

ISBN: (纸本)9798350378085

Machine learning, a vital part of artificial intelli-gence, improves our ability to make predictions from complex data. The success of these predictions relies heavily on the model's fit with its data and the data's consistency. Accurate predictions depend on closely matching the model to the data patterns. However, missing data can seriously disrupt the accuracy of predictions, causing errors. To address this, we recommend adopting a proposed framework that enhances predictions by effectively filling in missing data. Our objective is to refine the accuracy of our predictions, especially in scenarios where the data might be incomplete and features exhibit patterns of dependency. In the context of missing data, our approach concentrates on enhancing regression performance to minimize the effects of missing data as effectively as possible.

关键词： Accuracy Correlation Statistical analysis Smart cities Predictive models Data models Imputation Reliability Random forests Robots

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Inverse Design of Multiple-Input Multiple-Output Perfectly-Matched Metamaterials

Inverse Design of Multiple-Input Multiple-Output Perfectly-M...

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International Review of Progress in Applied Computational Electromagnetics (ACES)

作者： Shrey Thakkar Jorge Ruiz-Garcia Luke Szymanski Gurkan Gok Anthony Grbic Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor USA MIT Lincoln Laboratory Lexington USA RTX Technology Research Centre East Hartford USA

ISBN: (数字)9781733509671

ISBN: (纸本)9798350362978

Inverse design has been applied to a variety of complex electromagnetic structures including multifunctional and multiple-input multiple-output (MIMO) devices, where analytical design methods are limiting. We have previously reported on the inverse design of single-input, single-output Perfectly-Matched Metamaterials (PMMs). PMMs are inhomogeneous, anisotropic 2D metamaterials composed of unit cells that remain impedance-matched to each other and the surroundings under all excitations. PMMs rely primarily on refractive effects and exhibit broadband operation. This work presents the inverse design of a compact MIMO device based on PMMs: a 2D beamformer with prescribed amplitude and phase profiles that exhibits zero scan loss.

关键词： Limiting Design methodology Anisotropic Nonhomogeneous media Computational electromagnetics Metamaterials Broadband communication

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