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A Novel Access Point Deployment Framework for mmWave Cell-Free Massive MIMO Networks

IEEE Transactions on Wireless communications 2025年第6期24卷 4581-4597页

作者： Topal, Ozan Alp Demir, Ozlem Tugfe Bjornson, Emil Cavdar, Cicek School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Electrical and Electronics Engineering TOBB University of Economics and Technology Ankara Turkey

Millimeter-wave network deployment is an essential and ongoing problem due to the limited coverage and expensive network infrastructure. In this work, we solve a joint network deployment and resource allocation optimization problem for a mmWave cell-free massive MIMO network considering indoor environments. The objective is to minimize the number of deployed access points (APs) for a given environment, bandwidth, AP cooperation, and precoding scheme while guaranteeing the rate requirements of the user equipments (UEs). Considering coherent joint transmission (C-JT) and non-coherent joint transmission (NC-JT), we solve the problem of AP placement, UE-AP association, and power allocation among the UEs and resource blocks jointly. For numerical analysis, we model a mid-sized airplane cabin in ray-tracing as an exemplary case for IDS. Results demonstrate that a minimum data rate of 1 Gbps can be guaranteed with less than 10 APs with C-JT. From a holistic network design perspective, we analyze the trade-off between the required fronthaul capacity and the processing capacity per AP, under different network functional split options. We observe an above 600 Gbps fronthaul rate requirement, once all network operations are centralized, which can be reduced to 200 Gbps under physical layer functional splits. 2002-2012 IEEE.

关键词： Integer programming

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Utilization of Two Cross Two Switch for Bitonic Sorting Circuit

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Micro and Nanosystems 2025年第1期17卷 45-57页

作者： Bhattacharjee, Subhasree Sarkar, Soumyadip Das, Kunal Sadhu, Arindam Sarkar, Bikramjit Department of Computer Application Narula Institute of Technology Kolkata India Department of Computer Science Acharya Prafulla Chandra College Kolkata India Department of Electronics & Communication Engineering Greater Kolkata College of Engineering and Management West Bengal India Department of Computer Science & Engineering JIS College of Engineering West Bengal India

Background: The synthesis of reversible logic has gained prominence as a crucial research area, particularly in the context of post-CMOS computing devices, notably quantum computing. Objective: To implement the bitonic sorting circuit, 2x2 switch, is used. The overarching objective of implementing a reversible sorting circuit is to mitigate power consumption while promising secure communication. The successful implementation of the bitonic sorting circuit utilizing the proposed 2x2 switch is validated through simulations conducted in IBM Qiskit. Methods: This study begins by employing the control swap gate to implement a 2x2 switch, aimed at enabling concurrent computing operations within multiprocessor systems to enhance throughput. Subsequently, following the successful implementation of the 2x2 switch, the study proceeds to design a bitonic sorting circuit. Results: Bitonic sort using a 2x2 switch on the IBM Quantum Experience (IBMQ) platform via the QISKit SDK in Python 3.6. Specifically, Qiskit v0.29.0 is utilized, with OpenQASM. OpenQASM serves as the quantum assembly and instruction language. Conclusion: This work presents a methodology for constructing a 2x2 switch, utilizing a reversible control swap gate as its core component. Additionally, it demonstrates the intricate development and implementation of a bitonic sorting circuit, capitalizing on this innovative switch architecture. © 2025 Bentham Science Publishers.

关键词： Quantum computers

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Multi-Channel Multi-Frame Integration Detection Method for Weak Target with Coherent MIMO Radar

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IEEE Transactions on Aerospace and Electronic Systems 2025年第3期61卷 6518-6536页

作者： Gao, Longji Li, Xiaolong Wang, Mingxing Sun, Zhi Cui, Guolong Yeo, Tat Soon University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China National University of Singapore Department of Electrical and Computer Engineering Singapore119077 Singapore

Coherent multiple-input multiple-output (MIMO) radar could significantly improve the weak moving target detection ability by accumulating multi-channel and multi-frame echo signal. However, due to the target motion and the geometric configuration differences of each node relative to the target, the multi-channel multi-frame accumulation of target echoes by coherent MIMO radar faces several difficulties, i.e., range migration (RM) and Doppler frequency migration (DFM) issues within each channel, spatial variations of target signal envelope and phase across different channels, time variations of target signal envelope and phase across different frames. To tackle these problems, two contributions are made in this paper. Firstly, by applying the generalized Radon Fourier transform (GRFT) to correct the RM/DFM and obtain the signal integration within each channel, we propose a multi-channel accumulation algorithm to compensate the spatial variations of target signal and realize the target energy accumulation across different channels, which takes full advantage of the GRFT output characteristics and the radar system topological structure. Secondly, a multi-frame accumulation algorithm is developed to compensate the time variations of target signal and achieve the further integration of target energy distributed in different frames. We also provide explicit expressions and analysis for performance metrics, including the output response, SNR improvement performance. The proposed method's effectiveness is confirmed through simulation experiments. © 1965-2011 IEEE.

关键词： Topology

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Adaptive Age of Information Optimization in Rateless Coding-Based Multicast-Enabled Sensor Networks

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2024年 1卷 73-92页

作者： Lin, Hung-Chun Lin, Kuang-Hsun Wei, Hung-Yu National Taiwan University Graduate Institute of Communication Engineering Taipei City106319 Taiwan National Taiwan University Graduate Institute of Communication Engineering The Department of Electrical Engineering Taipei City106319 Taiwan

As the demand for real-time information in Internet of Things and wireless sensor networks (WSN) scenarios grows with the evolution of bandwidth-intensive 5G applications, multicast transmission becomes increasingly vital. This article delves into the significant role of multicast in WSN, exploring a novel perspective of using rateless codes over the traditionally employed hybrid automatic repeat request for eliminating retransmissions in a wireless multicast system. We aim to optimize data freshness, quantified by the age of information (AoI), and analyze strategies to minimize time-average AoI in a multicast environment with diverse channel conditions. Specifically, our policy focuses on optimizing the time-average AoI based on sensor devices' feedback. We transform the problem into a Markov decision process to locate optimal and low-complexity suboptimal policies. We present the first age-minimum scheme for rateless code-based wireless multicast systems. Our numerical simulations reveal that the proposed policies, developed considering unique system structural properties, consistently surpass baseline strategies. We are thus able to preempt updates at the most beneficial time, thereby addressing the issue of the bottleneck device's adverse impact on overall performance. © 2024 IEEE.

关键词： Wireless sensor networks

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Early detection of stroke disease using patients previous medical data instil with deep learning

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Multimedia Tools and Applications 2025年第16期84卷 16853-16881页

作者： Diwan, Tausif Gajbhiye, Saurav M. Goydani, Purva R. Gannarpwar, Vedant R. Khandait, Harshal R. Tembhurne, Jitendra V. Sahare, Parul Department of Computer Science and Engineering Indian Institute of Information Technology Maharashtra Nagpur441108 India Department of Electronics and Communication Engineering Indian Institute of Information Technology Maharashtra Nagpur441108 India

Early detection of any disease and starting its treatment in this early stage are the most important steps in case of any life-threatening disease. Stroke is not an exception in this regard which is one of the leading causes of death and disability worldwide. We develop a simple but efficient deep neural network for the stroke prediction that accurately evaluates the probability of occurrence of stroke disease by treating this as a binary classification problem on one of the standard datasets named as Stroke Prediction Dataset available on Kaggle. With the help of effective pre-processing techniques such as SMOTE and FastICA on the noisy and imbalanced dataset, we could achieve improved performance for the binary classification of stroke prediction by employing a deep dense neural network. The architectural hyper-parameters are critically designed with the help of Keras Tuner and consisting of just ten layers in the deep dense neural network. With the help of this light weight deep dense model, we report an accuracy of 95%, AUC Score of 100% and F1-Score of 95% on a testing set. Moreover, we also present comparative illustration of various machine learning models for the aforesaid task along with the comparative illustration of various state of the arts with our proposed model for the stroke prediction. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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Noise robust automatic speech recognition: review and analysis

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International Journal of Speech Technology 2023年第2期26卷 475-519页

作者： Dua, Mohit Akanksha Dua, Shelza Department of Computer Engineering National Institute of Technology Kurukshetra India Department of Electronics and Communication Engineering National Institute of Technology Kurukshetra India

Automatic Speech Recognition (ASR) system is an emerging technology used in various fields such as robotics, traffic controls, and healthcare, etc. The leading cause of ASR performance degradation is mismatch between the training and testing environments. The main reason for this mismatch is the presence of noise during the testing phase of an ASR system. Various techniques have been used by different researchers in front and backend phases of ASR, to detect and handle the noise. However, a very few review papers have considered noise as a criterion to present the comparison among the existing research works. Hence, the objective of this survey is to analyze and review all the effective methods proposed by different scientists and researchers to boost the noise robustness of an ASR system. Initially, the paper discusses the basic architecture of an ASR system, the factors affecting the its performance, and noise problem formulation. Secondly, the work analysis existing state of the art noise robust ASR methods in terms of front end feature extraction techniques and backend classification model. Then, a detailed review in terms of various speech databases, that are used by these methods, is given. Finally, an analysis in terms of performance metrics of all these noise-resistant ASR techniques is presented. Also, the paper discusses various feature extraction techniques, backend classification methods, different speech databases and performance metrics in detail, while presenting the analysis. The paper also discusses the existing challenges, and describes future research directions in the area of building noise-resistant ASR systems. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Feature extraction

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Pseudoinvertible Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 602-612页

作者： Bolluyt, Elijah D. Comaniciu, Cristina Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States

This work introduces novel architecture components and training procedures to create augmented neural networks with the ability to process data bidirectionally via an end-to-end approximate inverse. We develop pseudoinvertible neural network (PsI-NN) layers which function as drop-in replacements for corresponding convolutional and fully connected layers;by using these, existing architectures gain a pseudoinverse function which, with training, approximately reverses the forward function. For cases where learning both a task and its inverse are necessary or desirable, we show that PsI-NN enabled models match or exceed the quality of results generated by systems that use two separate models while drastically reducing system parameter count. We demonstrate this on two tasks: unpaired image translation and semisupervised classification. In both, PsI-NN greatly reduces parameter count without any loss of output quality;in semisupervised image classification, PsI-NN improves classification performance beyond the baseline autoencoder method. Our approach creates pseudoinvertible versions of existing architectures, circumventing the stringent constraints required to create a true inverse while allowing a single neural network to learn a task in two directions simultaneously. © 2023 IEEE.

关键词： Supervised learning

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Priority-based Residential Demand Response for Alleviating Crowding in Distribution Systems

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Journal of Modern Power Systems and Clean Energy 2023年第2期11卷 502-510页

作者： Venkateswarlu Gundu Sishaj P.Simon Vemalaiah Kasi Narayana Prasad Padhy Dheeraj Kumar Khatod Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation GunturGunturIndia Department of Electrical and Electronics Engineering National Institute of Technology TiruchirappalliTiruchirappalliIndia Department of Electrical Engineering(EED) Indian Institute of Technology Roorkee(IITR)RoorkeeIndia

The dynamic pricing environment offers flexibility to the consumers to reschedule their switching *** the dynamic pricing environment results in several benefits to the utilities and consumers,it also poses some *** crowding among residential customers is one of such *** scheduling of loads at low-cost intervals causes crowding among residential customers,which leads to a fall in voltage of the distribution system below its prescribed *** order to prevent crowding phenomena,this paper proposes a priority-based demand response program for local energy *** the program,past contributions made by residential houses and demand are considered as essential parameters while calculating the priority *** non-linear programming(NLP)model proposed in this study seeks to reschedule loads at low-cost intervals to alleviate crowding *** the NLP model does not guarantee global optima due to its non-convex nature,a second-order cone programming model is proposed,which captures power flow characteristics and guarantees global *** proposed formulation is solved using General Algebraic Modeling System(GAMS)software and is tested on a 12.66 kV IEEE 33-bus distribution system,which demonstrates its applicability and efficacy.

关键词： Crowding strategy demand response distribution system General Algebraic Modeling System(GAMS) local energy community second-order cone programming

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SiC JFETs Modular Cascaded Method with Active Clamp Control Strategy for DC Solid-state Circuit Breaker

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CSEE Journal of Power and Energy Systems 2023年第6期9卷 2435-2446页

作者： Hong Duan Wei Wang Zhikang Shuai Dong He Daixin Chen Z.John Shen the College of Electrical and Information Engineering Hunan UniversityChangsha 410006China the Department of Electrical and Computer Engineering Illinois Institute of TechnologyChicagoIL 60616 USA.

Power semiconductor devices cascaded topology is one of the most common solutions for solid-state circuit breakers(SSCBs)working in medium-voltage DC ***,when the number of cascaded devices is large,current series technologies of power semiconductor devices will be difficult to be applied since with uneven voltage sharing ***,this paper proposes a novel cascade method of multi-SiC JFETs based on modules *** method consists of two parts:one is a single-gate driver cascaded SiC JFETs topology which is used to form the module,the other one is an active clamp control strategy which ensures that each module is protected from overvoltage when modules are *** proposed cascade method can effectively suppress voltage overshoot of cascading power devices in the switching *** on the proposed cascade method,a 5kV/63A SSCB prototype is ***,an experiment is conducted based on the designed *** results validate the effectiveness of the proposed cascaded method for SSCB.

关键词： Active control clamp DC solid-state circuit breaker modular SiC JFETs series connection

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Joint Trajectory, User-Association, and Power Control for Green UAV-Assisted Data Collection using Deep Reinforcement Learning

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-12页

作者： Mondal, Abhishek Mishra, Deepak Prasad, Ganesh Johansson, Hakan Department of Electronics and Communication Engineering National Institute of Technology Calicut Kozhikode India School of Electrical Engineering and Telecommunications University of New South Wales Sydney Australia Division of Communication Systems Department of Electrical Engineering Linköping University 581 83 Linköping Sweden

Because of the numerous benefits, unmanned aerial vehicles (UAVs) assist ground users in maintaining a satisfactory quality of service (QoS) even when they are far from terrestrial base stations (BSs) or outside the cellular coverage area. However, the limited energy capacity of UAVs restricts their operational duration. This study, therefore, investigates how to maximize energy efficiency (EE) in a UAV-enabled data collection system to prolong the network's lifespan, taking into account variations in UAV propulsion and data reception energy. The study focuses on optimizing user associations, their instantaneous transmit power allocation (PA), and UAV's trajectory while meeting users' minimum data rate requirements. This optimization problem is challenging due to its non-convex and combinatorial nature, making analytical solutions difficult. To address this, the study uses the Markov decision process (MDP) to split the problem into two sub-problems: user association with PA and UAV's optimal successive locations. These sub-problems are then solved alternately, first deriving optimal instantaneous transmit powers for each user analytically and then employing a deep reinforcement learning (DRL) framework based on the soft actor-critic (SAC) algorithm to acquire the UAV's optimal flying path. The proposed adaptive SAC algorithm allows the UAV to adjust its speed, heading direction, and altitude while efficiently shaping rewards to adhere to practical constraints. Numerical results validate the analysis and demonstrate significant improvements in total EE compared to benchmark deep deterministic policy gradient (DDPG), twin-delayed DDPG, and particle swarm optimization techniques, with increases of $19.29\%$, $7.53\%$, and $53.96\%$, respectively. IEEE

关键词： Quality of service

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