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Pareto Optimal Hybrid Beamforming for Short-Packet Millimeter-Wave Integrated Sensing and Communication

IEEE Transactions on Communications 2024年第6期73卷 4570-4585页

作者： Singh, Jitendra Naveen, Banda Srivastava, Suraj Jagannatham, Aditya K. Hanzo, Lajos Indian Institute of Technology Kanpur Department of Electrical Engineering UP Kanpur208016 India Indian Institute of Technology Jodhpur Department of Electrical Engineering Rajasthan Jodhpur342030 India University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

Pareto optimal solutions are conceived for radar beamforming error (RBE) and sum rate maximization in short-packet (SP) millimeter-wave (mmWave) integrated sensing and communication (ISAC). Our ultimate goal is to realize ultra-reliable low-latency communication (uRLLC) and real-time sensing capabilities for 6G applications. The ISAC base station (BS) transmits short packets in the downlink (DL) to serve multiple communication users (CUs) and detect multiple radar targets (RTs). We investigate the performance trade-off between the sensing and communication capabilities by optimizing both the radio frequency (RF) and the baseband (BB) transmit precoder (TPC), together with the block lengths. The optimization problem considers the minimum rate requirements of the CUs, the maximum tolerable radar beamforming error (RBE) for the RTs, the unit modulus (UM) elements of the RF TPC, and the finite transmit power as the constraints for SP transmission. The resultant problem is highly non-convex due to the intractable rate expression of the SP regime coupled with the non-convex rate and UM constraints. To solve this problem, we propose an innovative two-layer bisection search (TLBS) algorithm, wherein the RF and BB TPCs are optimized in the inner layer, followed by the block length in the outer layer. Furthermore, a pair of novel methods, namely a bisection search-based majorizer and minimizer (BMM) as well as exact penalty-based manifold optimization (EPMO) are harnessed for optimizing the RF TPC in the inner layer. Subsequently, the BB TPC and the block length are derived via second-order cone programming (SOCP) and mixed integer programming methods, respectively. Finally, our exhaustive simulation results reveal the effect of system parameters for various settings on the RBE-rate region of the SP mmWave ISAC system and demonstrate a significantly enhanced performance compared to the benchmarks. © 1972-2012 IEEE.

关键词： Integer programming

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Guest Editorial Rate Splitting for Future Wireless Networks

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IEEE Journal on Selected Areas in Communications 2023年第5期41卷 1259-1264页

作者： Clerckx, Bruno Mao, Yijie Jorswieck, Eduard A. Yuan, Jinhong Love, David J. Erkip, Elza Niyato, Dusit Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom Graz8010 Austria ShanghaiTech University School of Information Science and Technology Shanghai201210 China Institute for Communications Technology TU Braunschweig Braunschweig38106 Germany University of New South Wales School of Electrical Engineering and Telecommunications SydneyNSW2052 Australia Purdue University Elmore Family School of Electrical and Computer Engineering West LafayetteIN47907 United States New York University Tandon School of Engineering Department of Electrical and Computer Engineering BrooklynNY11201 United States Nanyang Technological University School of Computer Science and Engineering Jurong West639798 Singapore

Rate splitting (RS) and rate splitting multiple access (RSMA) have emerged as a promising and powerful multiple access, interference management, and multi-user strategy for next-generation wireless systems and networks. This Special Issue is entirely dedicated to the theory, design, optimization, and applications of RS and RSMA in various network configurations. It starts with a guest editor-authored tutorial paper [A1] that delineates the basic principles and applications of RS and RSMA. The tutorial paper is then followed by 17 technical papers. © 1983-2012 IEEE.

关键词： Wireless networks

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Ultrafast magneto-optical dynamics in nickel(111)single crystal studied by the integration of ultrafast reflectivity and polarimetry probes

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Chinese Physics B 2024年第3期33卷 65-69页

作者：匡皓余军潇陈洁 H.E.Elsayed-Ali 李润泽 Peter M.Rentzepis School of Physical Science and Technology ShanghaiTech UniversityShanghai 201210China Center for Transformative Science ShanghaiTech UniversityShanghai 201210China Center for Ultrafast Science and Technology Key Laboratory for Laser Plasmas (Ministry of Education)Collaborative Innovation Center of IFSA (CICIFSA)School of Physics and AstronomyShanghai Jiao Tong UniversityShanghai 200240China Department of Electrical and Computer Engineering Old Dominion UniversityNorfolkVirginia 23529USA Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTexas 77843USA

With the integration of ultrafast reflectivity and polarimetry probes,we observed carrier relaxation and spin dynamics induced by ultrafast laser excitation of Ni(111)single *** carrier relaxation time within the linear excitation range reveals that electron-phonon coupling and dissipation of photon energy into the bulk of the crystal take tens of *** the other hand,the observed spin dynamics indicate a longer time of about 120 *** further understand how the lattice degree of freedom is coupled with these dynamics may require the integration of an ultrafast diffraction probe.

关键词： ultrafast spin dynamics non-equilibrium dynamics multi-probe

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An Efficient Stacked Ensemble Model for Heart Disease Detection and Classification

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computers, Materials & Continua 2023年第10期77卷 665-680页

作者： Sidra Abbas Gabriel Avelino Sampedro Shtwai Alsubai Ahmad Almadhor Tai-hoon Kim Department of Computer Science COMSATS UniversityIslamabadPakistan College of Computer Studies De La Salle UniversityManila1004Philippines Faculty of Information and Communication Studies University of the Philippines Open UniversityLos Banos4031Philippines College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia School of Electrical and Computer Engineering Yeosu CampusChonnam National UniversityYeosu-si59626Korea

Cardiac disease is a chronic condition that impairs the heart’s *** includes conditions such as coronary artery disease,heart failure,arrhythmias,and valvular heart *** conditions can lead to serious complications and even be life-threatening if not detected and managed in *** have utilized Machine Learning(ML)and Deep Learning(DL)to identify heart abnormalities swiftly and *** approaches have been applied to predict and treat heart disease utilizing ML and *** paper proposes a Machine and Deep Learning-based Stacked Model(MDLSM)to predict heart disease *** approaches such as eXtreme Gradient Boosting(XGB),Random Forest(RF),Naive Bayes(NB),Decision Tree(DT),and KNearest Neighbor(KNN),along with two DL models:Deep Neural Network(DNN)and Fine Tuned Deep Neural Network(FT-DNN)are used to detect heart *** models rely on electronic medical data that increases the likelihood of correctly identifying and diagnosing heart ***-known evaluation measures(i.e.,accuracy,precision,recall,F1-score,confusion matrix,and area under the Receiver Operating Characteristic(ROC)curve)are employed to check the efficacy of the proposed *** reveal that the MDLSM achieves 94.14%prediction accuracy,which is 8.30%better than the results from the baseline experiments recommending our proposed approach for identifying and diagnosing heart disease.

关键词： Deep neural network heart disease healthcare machine learning stacking

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Network Parameter Control in Cellular Networks through Graph-Based Multi-Agent Constrained Reinforcement Learning 19

Network Parameter Control in Cellular Networks through Graph...

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19th IEEE International Conference on Automation science and engineering, CASE 2023

作者： Forsberg, Albin Larsson Nikou, Alexandros Feljan, Aneta Vulgarakis Tumova, Jana Stockholm Sweden Kth Division of Robotics Perception and Learning Department of Electrical Engineering and Computer Science Stockholm Sweden Digital Futures United States

ISBN: (纸本)9798350320695

Cellular networks are growing in complexity at increasing speed and the geographical locations in which they are deployed in are getting denser. Traditional control methods fall short in providing a scalable and dynamic way of adapting the network to new conditions. Distributed multiagent reinforcement learning successfully addresses scalability problems seen in centralized approaches. The question of achieving learning with constraint satisfaction in distributed systems is still left unanswered in the state-of-the-art. In this work, we aim to perform distributed multi-agent constrained reinforcement learning in order to learn a policy online while satisfying imposed constraints. We use a coordination graph to model the interactions between agents and decompose the global value function. A conservative safety critic is trained in parallel to evaluate the safety of proposed actions. Our method allows for separate training of both the critic and the value network independently of each other, and hence offers flexibility in how and when to train the different models. The results are compared to a baseline using no safety critic. Simulations show that the agents succeed in learning a policy that can satisfy the constraints, while still maximizing the objective. © 2023 IEEE.

关键词： Graphic methods

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Enhancing IoT Networking Through Predictive Big Data Processing 2

Enhancing IoT Networking Through Predictive Big Data Process...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Jain, Sandeep Kumar Hannah Jessie Rani, R. Paikaray, Divya Vivekananda Global University Department of Electrical Engineering Jaipur India School of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India ARKA JAIN University Department of Computer Science & IT Jharkhand Jamshedpur India

ISBN: (纸本)9798350349221

the net of things (IoT) promises to revolutionize the way humans interact with their surroundings. Notwithstanding this promise, the deployment of IoT networks is hindered by using several challenges, particularly related to communication, scalability, and electricity performance. This paper proposes a technique to address those demanding situations through predictive big statistics processing blended with the use of new network technology. In particular, the use of prediction fashions, system studying, and analytics algorithms will permit IoT networks to investigate past and present facts in order to forecast destiny traits. Additionally, the use of latest and existing network technologies, which include extremely-low strength mesh networks, 5G, and electricity-conscious network routing protocols will help to improve the overall performance of IoT networks. In conclusion, this paper gives a method to decorate the characteristic of IoT networks through predictive big statistics processing as a way to permit more effective, efficient, and reliable communique for all related devices. © 2024 IEEE.

关键词： Scalability

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Ultra-wideband Frequency Selective Surface for Communication Applications

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computers, Materials & Continua 2022年第3期70卷 6177-6187页

作者： Shahid Habib Ghaffer Iqbal Kiani Muhammad Fasih Uddin Butt Syed Muzahir Abbas Abdulah Jeza Aljohani Soon Xin Ng Department of Electrical and Computer Engineering COMSATS University IslamabadIslamabad45550Pakistan Department of Electrical and Computer Engineering King Abdulaziz UniversityJeddahKingdom of Saudi Arabia School of Electronics and Computer Science University of SouthamptonSouthamptonSO171BJUK School of Engineering Faculty of Science and EngineeringMacquarie UniversitySydneyNSW2109Australia BENELEC BotanySydneyNSW2019Australia

A low-profile ultra-wideband(UWB)band-stop frequency selective surface(FSS)is designed for S-,C-,X-and Ku-bands communication *** FSS is constructed by using square and circular loop elements printed on the top and bottom sides of the RO3210 *** FSS has been designed to reduce the electromagnetic interference(EMI)as well as to mitigate the harmful effects of electromagnetic radiation on the human body caused by different radio *** dimension and size of the UWB FSS have been reduced to 0.12λ×0.12λand 90%,respectively,as compared to the reported *** other advantages of the proposed FSS are that it is low profile,it has a simplified geometry and it ensures better angular and polarization stability of up to 85◦.The-10 and-20 dB bandwidths of the proposed FSS are 146%(2.0-13.0 GHz)and 80%(4.87-11.42 GHz),*** results have been obtained using ANSYS HFSS and verified through measured results.

关键词： Electromagnetic radiation fractional bandwidth polarization transverse electric transverse magnetic

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Large Language Models for Data Annotation and Synthesis: A Survey

Large Language Models for Data Annotation and Synthesis: A S...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Tan, Zhen Li, Dawei Wang, Song Beigi, Alimohammad Jiang, Bohan Bhattacharjee, Amrita Karami, Mansooreh Li, Jundong Cheng, Lu Liu, Huan School of Computing and Augmented Intelligence Arizona State University United States Department of Electrical and Computer Engineering the University of Virginia United States Department of Computer Science University of Illinois Chicago United States

ISBN: (纸本)9798891761643

Data annotation and synthesis generally refers to the labeling or generating of raw data with relevant information, which could be used for improving the efficacy of machine learning models. The process, however, is labor-intensive and costly. The emergence of advanced Large Language Models (LLMs), exemplified by GPT-4, presents an unprecedented opportunity to automate the complicated process of data annotation and synthesis. While existing surveys have extensively covered LLM architecture, training, and general applications, we uniquely focus on their specific utility for data annotation. This survey contributes to three core aspects: LLM-Based Annotation Generation, LLM-Generated Annotations Assessment, and LLM-Generated Annotations Utilization. Furthermore, this survey includes an in-depth taxonomy of data types that LLMs can annotate, a comprehensive review of learning strategies for models utilizing LLM-generated annotations, and a detailed discussion of the primary challenges and limitations associated with using LLMs for data annotation and synthesis. Serving as a key guide, this survey aims to assist researchers and practitioners in exploring the potential of the latest LLMs for data annotation, thereby fostering future advancements in this critical field. © 2024 Association for Computational Linguistics.

关键词： Data assimilation

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Enhancing Image Clarity through a Comparative Analysis of Polarizing Filters and Homomorphic Filtering Algorithms

Enhancing Image Clarity through a Comparative Analysis of Po...

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2023 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2023

作者： Cha, Sung Keun Lee, Si Woo Jeon, Jae Wook Sungkyunkwan University School of Electronic and Electrical Engineering Suwon Korea Republic of Sungkyunkwan University Department of Computer Science and Engineering Suwon Korea Republic of Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798350344318

In the context of autonomous vehicles, camera-based lane recognition can be compromised by external factors like sunlight. This study evaluated two denoising methods: software-based denoising via the homomorphic filter algorithm and hardware-based denoising by attaching a polarizing filter in front of the camera lens. Images were analyzed under four conditions based on the combination of using the homomorphic filter and the presence of a polarizing filter. A comparative analysis revealed that a combined application of both filters achieved the highest Structural Similarity Index Measure (SSIM) and the lowest Mean Square Error (MSE), establishing it as the most effective denoising approach. © 2023 IEEE.

关键词： Mean square error

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A novel deep learning approach for investigating liquid fuel injection in combustion system

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Discover Artificial Intelligence 2025年第1期5卷 1-14页

作者： Inam, Syed Azeem Khan, Abdullah Ayub Ahmed, Noor Mazhar, Tehseen Shahzad, Tariq Khan, Sunawar Saeed, Mamoon M. Hamam, Habib Department of Artificial Intelligence and Mathematical Science Sindh Madressatul Islam University Sindh Karachi Pakistan Department of Computer Science and Information Technology Shaheed Benazir Bhutto University Lyari Karachi Pakistan Department of Computer Science Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology Karachi Pakistan Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar62300 Pakistan Sana’a00967 Yemen Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Electrical and Electronic Engineering Sciences University of Johannesburg Johannesburg South Africa Av. Grandes Ecoles BP 1989 Libreville Gabon Bridges for Academic Excellence Spectrum Center-ville Tunis Tunisia

The intricacies and instability of introducing cryogenic propellants into the combustion system have piqued the curiosity of scientists studying the procedure. The latest innovation is utilizing data-driven machine learning and deep learning approaches to gain deeper insights into the related difficulties. However, the current work serves as a baseline for future research because relatively few studies have used data-driven methodologies to assess the temperature of liquid fuel injections in combustion systems. The performance of Linear Regression (LR), Random Forest (RF), Extra Trees Regressor (ETR), Polynomial Regression (PR), Support Vector Regressor (SVR), Decision Tree Regressor (DTR), Gradient Boost Regressor (GBR), XGB Regressor (XGBoost), AdaBoost Regressor (ABR), K-Neighbors Regressor (KNR), Long-Short Term Memory (LSTM), Bi-LSTM (Bi-directional Long-Short Term Memory) has all been investigated in this study. The study also suggested a Fully Connected Neural Network (FCNN) to examine its performance and paired it with an Extra Tree Regressor (ETR). The coupled FCNN and Extra Tree Regressor outperform the other algorithms with a Mean Square Error (MSE) of 0.0000005062, Root Mean Square Error (RMSE) of 0.00071148, Mean Absolute Error (MAE) of 0.00020672, and R-squared (R2) value of 0.99998689. Linear Regression, Polynomial Regression, and Support Vector Regressor are found to be the least-performing algorithms. The current work uses machine learning and deep learning methods to make data-driven decisions for liquid fuel injection in the combustion system. © The Author(s) 2025.

关键词： Deep learning

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