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Rapid Phase Ambiguity Elimination Methods for DOA Estimator via Hybrid Massive MIMO Receive Array

Chinese Journal of Electronics 2024年第1期33卷 175-184页

作者： Xichao ZHAN Zhongwen SUN Feng SHU Yiwen CHEN Xin CHENG Yuanyuan WU Qi ZHANG Yifan LI Peng ZHANG School of Information and Communication Engineering Hainan University School of Electronic and Optical Engineering Nanjing University of Science and Technology

For a sub-connected hybrid multiple-input multiple-output(MIMO) receiver with K subarrays and N antennas, there exists a challenging problem of how to rapidly remove phase ambiguity in only single time-slot. A direction of arrival(DOA) estimator of maximizing received power(Max-RP) is proposed to find the maximum value of K-subarray output powers, where each subarray is in charge of one sector, and the center angle of the sector corresponding to the maximum output is the estimated true DOA. To make an enhancement on precision, Max-RP plus quadratic interpolation(Max-RP-QI) method is designed. In the proposed Max-RP-QI, a quadratic interpolation scheme is adopted to interpolate the three DOA values corresponding to the largest three receive powers of *** achieve the Cramer Rao lower bound, a Root-MUSIC plus Max-RP-QI scheme is developed. Simulation results show that the proposed three methods eliminate the phase ambiguity during one time-slot and also show low computational complexities. The proposed Root-MUSIC plus Max-RP-QI scheme can reach the Cramer Rao lower bound,and the proposed Max-RP and Max-RP-QI are still some performance losses 2–4 d B compared to the Cramer Rao lower bound.

关键词： Hybrid analog and digital Direction of arrival Massive hybrid multiple-input multiple-output Phase ambiguity low computational complexity

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A low-complexity Sensing-Aided Timing Synchronization Method for UAV-Assisted OFDM Systems

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IEEE WIRELESS COMMUNICATIONS LETTERS 2025年第3期14卷 911-915页

作者： Qing, Chaojin Huang, Yuxin Zhao, Qian Hu, Qiuting Xihua Univ Sch Elect Engn & Elect Informat Chengdu 610039 Peoples R China

The timing synchronization (TS) methods based on compressed sensing (CS) and machine learning (ML) in unmanned aerial vehicle (UAV)-assisted orthogonal frequency division multiplexing (OFDM) systems need to further enhance their TS correctness while also reducing computational complexity, due to the impact of time-frequency double-selective channel. To tackle this challenge, inspired by integrated sensing and communication (ISAC), a sensing-aided TS method is proposed for UAV-assisted OFDM systems. This method leverages the received echo signals at the ground base station (gBS) to establish a metric detection threshold for determining the number of resolvable paths. By leveraging the identified number, the identification of the inter-symbol interference (ISI)-free region is iteratively refined, featuring a low computational complexity. With the reduced computational complexity, simulation results indicate that compared to CS and ML-based TS methods, the proposed method significantly reduces the TS error probability and exhibits its robustness against parameter variations.

关键词： Sensors OFDM Autonomous aerial vehicles Time-domain analysis Estimation Delays Vectors Time-frequency analysis Synchronization Signal resolution Timing synchronization (TS) low computational complexity unmanned aerial vehicle (UAV) orthogonal frequency division multiplexing (OFDM) integrated sensing and communication (ISAC)

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New efficient numerical methods for some systems of linear ordinary differential equations

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MATHEMATICS AND COMPUTERS IN SIMULATION 2025年 230卷 438-455页

作者： Boda, Livia Farago, Istvan Budapest Univ Technol & Econ Dept Anal & Operat Res Budapest Hungary Eotv Lorand Univ Dept Appl Anal Budapest Hungary ELKH ELTE Numer Anal & Large Networks Res Grp Budapest Hungary

In mathematics there are several problems arise that can be described by differential equations with particular, highly complex structure. Most of the time, we cannot produce the exact (analytical) solution of these problems, therefore we have to approximate them numerically by using some approximating method. The main aim of this paper is to create numerical methods, based on operator splitting, that well approximate the exact solution of the original ODE systems while having low computational complexity. Starting from an example, based on the relationship between the Lie-Trotter (sequential) and Strang-Marchuk splitting methods, we examine the properties of processed integrator methods. Then we generalize these methods and introduce the new extended processed methods. By examining the consistency and stability of these methods, we establish the one order higher convergence. However, these methods have a higher computational complexity, which we aim to reduce by introducing economic extended processed methods. In this case we show the lower computational complexity and prove the second-order convergence. In the end, we test the analyzed methods in three models: a large-scale linear model, a piecewise-linear model of flutter and the heat conduction equation. Runtimes and errors are also compared.

关键词： Differential equation Operator splitting Higher order method low computational complexity Large-scale model Aerodynamical model Heat conduction equation

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Fast Computation of Zero-Forcing Precoding for Massive MIMO-OFDM Systems

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 912-927页

作者： Liu, Junkai Zhang, Wei Jiang, Yi Fudan Univ Sch Informat Sci & Technol Key Lab Informat Sci Electromagnet Waves MoE Shanghai 200433 Peoples R China Fudan Univ Engn Sch Informat Sci & Technol Dept Commun Sci Shanghai 200438 Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Shanghai 200050 Peoples R China

As a simple and popular transmission scheme, zero-forcing (ZF) precoding can effectively reap the great benefits of a multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) wireless system. But as the wireless technology evolves to massive MIMO-OFDM, even the ZF precoding may incur too cumbersome computational complexity. In this paper, we first derive the ZF precoder in the time domain. By exploiting the block-Toeplitzness of the time-domain channel matrix, we propose a novel approximate solution to the original time-domain ZF precoding problem for fast computation. We then provide an approximation analysis to show the convergence to the exact solution. To compute the approximate scheme efficiently, we propose two novel low-complexity algorithms: a fast Fourier transform (FFT) based conjugate gradient algorithm, which can obtain the MIMO-OFDM ZF precoder as a time-domain MIMO finite impulse response (FIR) filter, and a flexible block Toeplitz QR decomposition algorithm exploiting the special structure of the time-domain channel matrix. We also extend the proposed approximate scheme and two low-complexity algorithms to the regularized ZF precoding scenario. Simulation results and complexity analysis show that our methods can achieve favorable performance but with significantly lower computational complexity compared with the state-of-the-art methods.

关键词： Massive MIMO-OFDM zero-forcing precoding low computational complexity time domain precoding conjugate gradient QR decomposition

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low-complexity frequency-invariant beampattern synthesis using accurate response control for speech extraction

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APPLIED ACOUSTICS 2024年 224卷

作者： Xu, Jiayi Li, Jian Meng, Weixin Li, Xiaodong Zheng, Chengshi Chinese Acad Sci Inst Acoust Key Lab Noise & Vibrat Res Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Beampattern synthesis generally designs weight parameters to form a main beam towards the direction of interest while suppressing interference and environmental noise simultaneously, playing a critical role in fixed beamforming techniques. In practical applications, when the position of the interference source changes, the filter design requirements will correspondingly change, thus necessitating accurate response control and rapid update capabilities. A low-complexity method is also highly desired in broadband beampattern synthesis, especially for time-domain beamformers with a relatively large number of weight parameters. To achieve rapid update and reduce the complexity for time-domain beamformers, this paper proposes a low-complexity frequencyinvariant beampattern synthesis method using accurate response control. By introducing the null-forming scheme of adaptive beamformers, a virtual interference-plus-noise covariance matrix is constructed to precisely control the beampattern. Additionally, the spatial response variation is considered in the optimization problem for a constant mainlobe pattern, avoiding the complexity of determining a desired beampattern. Compared with the existing algorithms based on the interior-point method and the alternating direction method of multipliers, the proposed method reduces the computational complexity of each iteration, showing higher efficiency in broadband beampattern synthesis. Furthermore, the proposed method adjusts the beamformer flexibly on the basis of a pre-designed beampattern, achieving fast beampattern update when the acoustic environment changes. Numerical simulations on beam patterns and experimental results on speech extraction demonstrate better interference suppression performance of the beamformers designed by the proposed method.

关键词： Frequency-invariant beampattern Time-domain beamforming Microphone array low computational complexity Speech extraction

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Data Dissemination Framework Using low-Rank Approximation in Edge Networks

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IEEE ACCESS 2024年 12卷 1266-1279页

作者： Kwon, Jungmin Park, Hyunggon Ewha Womans Univ Dept Elect & Elect Engn Seoul 03760 South Korea Ewha Womans Univ Dept Elect & Elect Engn Smart Factory Multidisciplinary Program Seoul 03760 South Korea

This paper proposes a reliable data dissemination framework for edge networks, leveraging network coding combined with low-rank approximation. We consider an edge network that consists of a server and power-limited mobile devices, where the data is broadcasted by the server. In such networks, broadcasted data may be lost due to poor channel conditions or the interference caused by the mobility of edge mobile devices, particularly without a retransmission mechanism. This can cause application errors in edge devices, lower the Quality of Service (QoS), and compromise network stability. To overcome these challenges, we propose a framework for reliable edge networks in broadcasting without retransmissions. The edge network reliability can be achieved by the approximate decoding of broadcasted data. In the proposed framework, the edge server employs matrix factorization to encode data with principal components, ensuring a lower decoding error rate even with potential packet losses. Furthermore, the proposed framework can shift the computational complexity from mobile edge devices to the edge server using the low-rank approximation at the decoding stage, effectively mitigating power limitations on mobile devices. Through theoretical analysis, we demonstrate that the proposed algorithm outperforms typical broadcasting in terms of decoding accuracy, and present an upper bound error rate for the proposed algorithm. The simulation results confirm that the proposed algorithm outperforms other state-of-the-art algorithms in terms of decoding accuracy, time delay, and complexity.

关键词： Data dissemination edge computing edge network network coding low-rank approximation decoding accuracy low computational complexity complexity shift

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A computationally Efficient Learning-Based Control of a Three-Phase AC/DC Converter for DC Microgrids

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ENERGIES 2025年第9期18卷 2383-2383页

作者： Li, Ran Feng, Wendong Qie, Tianhao Liu, Yulin Fernando, Tyrone Iu, Herbert HoChing Zhang, Xinan Univ Western Australia Dept Elect Elect & Comp Engn Perth WA 6009 Australia

This paper presents a novel learning-based control algorithm for three-phase AC/DC converters, which are key components in DC microgrids, for reliable power conversion. In contrast with conventional model-based nonlinear controllers that rely on detailed system modeling and manual gain tuning, the proposed method is model-free and eliminates such dependencies. By integrating a recurrent equilibrium network (REN), the controller achieves an enhanced dynamic response and robust steady-state performance, while maintaining a low computational complexity. Moreover, its closed-loop stability can be rigorously verified based on contraction theory and incremental quadratic constraints. To facilitate practical implementation, a design guideline is provided. Experimental results confirm that the proposed method outperforms conventional PI and model predictive controllers in terms of response speed, harmonic suppression, and robustness under parameter variations. Additionally, the algorithm is lightweight enough for real-time execution on embedded platforms, such as a TI DSP.

关键词： three-phase AC/DC converter learning-based control low computational complexity stability

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Nonnegative graph embedding induced unsupervised feature selection

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 282卷

作者： Mi, Yong Chen, Hongmei Yuan, Zhong Luo, Chuan Horng, Shi-Jinn Li, Tianrui Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Peoples R China Minist Educ Engn Res Ctr Sustainable Urban Intelligent Transpo Chengdu 611756 Peoples R China Southwest Jiaotong Univ Natl Engn Lab Integrated Transportat Big Data Appl Chengdu 611756 Peoples R China Southwest Jiaotong Univ Mfg Ind Chains Collaborat & Informat Support Techn Chengdu 611756 Peoples R China Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China Asia Univ Dept Comp Sci & Informat Engn Taichung 41354 Taiwan

Recently, many unsupervised feature selection (UFS) methods have been developed due to their effectiveness in selecting valuable features to improve and accelerate the subsequent learning tasks. However, most existing UFS methods suffer from the following three drawbacks: (1) They usually ignore the nonnegative attribute of feature when conducting feature selection, which inevitably loses partial information;(2) Most adopt a separate strategy to rank all features and then select the first k features, which introduces an additional parameter and often obtains suboptimal results;(3) Most generally confront the problem of high time-consuming. To tackle the previously mentioned shortage, we present a novel UFS method, i.e., Nonnegative Graph Embedding Induced Unsupervised Feature Selection, which considers nonnegative feature attributes and selects informative feature subsets in a one-step way. Specifically, the raw data are projected into a low-dimensional subspace, where the learned low-dimensional representation keeps a nonnegative attribute. Then, a novel scheme is designed to preserve the local geometric structure of the original data, and l(2,0) norm is introduced to guide feature selection without ranking and selecting processes. Finally, we design a high-efficiency solution strategy with low computational complexity, and experiments on real-life datasets verify the efficiency and advancement compared with advanced UFS methods.

关键词： Unsupervised feature selection Nonnegative attribute One-step way l(2,0) norm low computational complexity

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The Complex Convolutional Neural Network for Adaptive Spatio-Temporal Broadband Beamforming

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第7期73卷 10778-10782页

作者： Wu, Xun Xue, Cong Zhang, Shurui Zhu, Hairui Han, Yubing Sheng, Weixing Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Peoples R China

Broadband beamforming technology is an effective solution in millimeter-wave (mmWave) massive multiple-input-multiple-output (MIMO) systems to counteract severe path loss via beamforming gains. One classic approach to implementing adaptive broadband beamforming is through spatio-temporal structure. In this paper, a method based on the complex convolutional neural network (CCNN) is proposed, namely, broadband beamforming prediction complex-valued network (BBPCNet), which can solve the performance degradation problem of traditional broadband beamforming methods under circumstance of limited snapshots. The BBPCNet leverages the CCNN to maintain the inherent amplitude and phase relationships of the complex-valued signals received by the array. A significant advantage of the proposed method is the elimination of the need for covariance matrix inversion to produce beamforming weights, resulting in lower computational complexity. Numerical simulations demonstrate effectiveness and superiority of the proposed method.

关键词： Array signal processing Broadband communication Feature extraction Delays Covariance matrices Convolution Task analysis Massive MIMO spatio-temporal broadband beamforming complex convolutional neural network low computational complexity low snapshots

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Sparse and robust support vector machine with capped squared loss for large-scale pattern classification

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PATTERN RECOGNITION 2024年 153卷

作者： Wang, Huajun Zhang, Hongwei Li, Wenqian Changsha Univ Sci & Technol Dept Math & Stat Changsha Peoples R China Hunan Normal Univ Coll Life Sci Changsha Peoples R China

Support vector machine (SVM), being considered one of the most efficient tools for classification, has received widespread attention in various fields. However, its performance is hindered when dealing with large-scale pattern classification tasks due to high memory requirements and running very slow. To address this challenge, we construct a novel sparse and robust SVM based on our newly proposed capped squared loss (named as L-csl-SVM). To solve L-csl-SVM, we first focus on establishing optimality theory of L-csl-SVM via our defined proximal stationary point, which is convenient for us to efficiently characterize the L-csl support vectors of L-csl-SVM. We subsequently demonstrate that the L-csl support vectors comprise merely a minor fraction of entire training data. This observation leads us to introduce the concept of the working set. Furthermore, we design a novel subspace fast algorithm with working set (named as L-csl-ADMM) for solving L-csl-SVM, which is proven that L-csl-ADMM has both global convergence and relatively low computational complexity. Finally, numerical experiments show that L-csl-ADMM has excellent performances in terms of getting the best classification accuracy, using the shortest time and presenting the smallest numbers of support vectors when solving large-scale pattern classification problems.

关键词： Capped squared loss Fast algorithm Support vectors low computational complexity Working set

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