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检索条件"任意字段=Sensor Array and Multichannel Signal Processing Workshop"

共 2593 条记录，以下是101-110 订阅

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ADMM-Based Outage Constrained MIMO-ISAC Hybrid Beamforming Design

ADMM-Based Outage Constrained MIMO-ISAC Hybrid Beamforming D...

引用

sensor array and multichannel signal processing workshop

作者： Hao Liang Bin Liao College of Electronics and Information Engineering Shenzhen University Shenzhen China State Key Laboratory of Radio Frequency Heterogeneous Integration Shenzhen University Shenzhen China

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

This paper considers the problem of robust hybrid beamforming design for multiple-input multiple-output (MIMO) integrated sensing and communications (ISAC) systems in the presence of channel state information (CSI) error. Specifically, the analog and digital hybrid beamformer at the transmitter is designed with imperfect CSI, through synthesizing a desired transmit power pattern for radar sensing under the outage probability constraints on the signal-to-interference-plus-noise ratio (SINR) of each user. The formulated nonconvex problem is addressed by taking advantage of the Cantelli's Inequality and the Alternating Direction Method of Multipliers (ADMM) frame- work, and deriving the solution of each subproblem involved in the algorithm. Numerical experiments are carried out to show the effectiveness and robustness of the proposed MIMO-ISAC hybrid beamforming design.

关键词： array signal processing Simulation signal processing algorithms Interference Probability Integrated sensing and communication Approximation algorithms

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A Preconditioned Fast Iterative Hard Thresholding Algorithm for Spectrally Sparse signal Reconstruction

A Preconditioned Fast Iterative Hard Thresholding Algorithm ...

引用

sensor array and multichannel signal processing workshop

作者： Fengmiao Bian Jian-Feng Cai Xueyang Quan Yang Wang Department of Mathematics Hong Kong University of Science and Technology Hong Kong China

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Spectrally sparse signals arise from various applications, including seismic imaging, autoregression, and magnetic resonance spectroscopy. Reconstructing a spectrally sparse signal from sub-samples on the uniform grid in the time domain can be reformulated as a low-rank Hankel matrix completion problem. Although the fast iterative hard thresholding (FIHT) algorithm is an efficient non-convex solver for this problem, it relies on the canonical metric in the matrix space. To speed up FIHT, we introduce a novel algorithm, preconditioned fast iterative hard thresholding (PFIHT), which adopts a data-driven metric by employing preconditioning techniques. Numerical experiments demonstrate that PFIHT outperforms FIHT and projected gradient descent (PGD) in terms of the number of iterations required for convergence and overall computational time.

关键词： Spectroscopy Thresholding (Imaging) signal processing algorithms Magnetic resonance Iterative algorithms signal reconstruction Sparse matrices

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On Detecting Low-Pass Graph signals Under Partial Observations

On Detecting Low-Pass Graph Signals Under Partial Observatio...

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sensor array and multichannel signal processing workshop

作者： Hoang-Son Nguyen Hoi-To Wai Dept. of Computer Science & Engineering The Chinese University of Hong Kong Dept. of Systems Engineering & Engineering Management The Chinese University of Hong Kong

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

The application of graph signal processing (GSP) on partially observed graph signals with missing nodes has gained attention recently. This is because processing data from large graphs are difficult, if not impossible due to the lack of availability of full observations. Many prior works have been developed using the assumption that the generated graph signals are smooth or low pass filtered. This paper treats a blind graph filter detection problem under this context. We propose a detector that certifies whether the partially observed graph signals are low pass filtered, without requiring the graph topology knowledge. As an example application, our detector leads to a pre-screening method to filter out non low pass signals and thus robustify the prior GSP algorithms. We also bound the sample complexity of our detector in terms of the class of filters, number of observed nodes, etc. Numerical experiments verify the efficacy of our method.

关键词： array signal processing Conferences Low-pass filters signal processing algorithms Detectors Filtering algorithms Complexity theory

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Near-Field ISAC: Performance Analysis and Rate Region Characterization

Near-Field ISAC: Performance Analysis and Rate Region Charac...

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sensor array and multichannel signal processing workshop

作者： Boqun Zhao Chongjun Ouyang Jiaqi Xu Xingqi Zhang Yuanwei Liu University of Alberta Edmonton Canada University College Dublin Dublin Ireland Queen Mary University of London London UK University of California Irvine USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

A near-field integrated sensing and communications (ISAC) framework is proposed with an accurate channel model, where the effective aperture loss is considered. Based on the proposed framework, sensing and communication performance are analyzed in terms of three different beamforming designs: a communications-centric design, a sensing-centric design, and a Pareto optimal design. Within each design, the closed-form expressions of sensing rates (SRs) and communication rates (CRs) are derived. Moreover, the attainable SR-CR regions of the near-field ISAC are characterized. Numerical results reveal that the performance of the proposed near-field ISAC framework converges to upper bounds as the number of BS antennas increases.

关键词： Accuracy Upper bound Closed-form solutions array signal processing Conferences Pareto optimization Integrated sensing and communication

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A Federated Learning Approach for Graph Convolutional Neural Networks

A Federated Learning Approach for Graph Convolutional Neural...

引用

sensor array and multichannel signal processing workshop

作者： Andrew Campbell Hang Liu Anna Scaglione Tong Wu Department of Electrical and Computer Engineering Cornell Tech Cornell University NY USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

In this paper we cast the problem of training a graph neural network based on labeled graph data in a “federated learning” scenario where different agents have access to data from a subset of the network nodes. The learning problem is not decomposable, therefore it does not lend itself to a straightforward mapping onto a distributed multi-agent protocol. We propose a multi-agent federated learning scheme which leverages the local and sparse structure of graph filters to limit the information sharing while emulating the performance of centralized training. Even though we preserve data locality and agent communication is restricted to the neighborhood level, the proposed method still converges in simulation.

关键词： Training Federated learning Computational modeling Distributed databases signal processing algorithms Data models Numerical models

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Experimental Evaluation of a Null-Steered Performance Weighted Blended Beamformer

Experimental Evaluation of a Null-Steered Performance Weight...

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sensor array and multichannel signal processing workshop

作者： Jeff Tucker Kathleen E. Wage ECE Department George Mason University Fairfax USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

In array signal processing beamformers are used to improve the signal to interference and noise ratio (SINR). Dominant mode rejection (DMR) is a common adaptive beamforming algorithm designed to detect quiet sources in the presence of a few loud interferers. Buck and Singer proposed the Universal DMR (UDMR) beamformer that automates the selection of the subspace rank by blending fixed-rank DMR beamformers based on their performance. While UDMR requires only slightly more computation than a single fixed-rank DMR, it still requires a computationally expensive eigendecomposition. This paper proposes to reduce the computational complexity of the UDMR by replacing the DMR beamformers with a set of conventional beamformers. The null-steered performance weighted blended (NS-PWB) beamformer combines beamformers with nulls steered to interlocking locations so that if a plane-wave interferer is present at least one beamformer in the set will have a null nearby. Additionally, when there are persistent sources of non-plane-wave interference then beamformers can be designed and included in the beamformer set to reject those interferers when they are present. This paper applies the NS-PWB beamformer to data from a vertical line array in the Philippine Sea. The results show that the NS-PWB approximates the SINR performance of DMR beamformers with significant computational savings.

关键词： array signal processing Conferences signal processing algorithms Interference Parallel processing Real-time systems Computational efficiency

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MLS-based Transmitter Orthogonality Analysis in MIMO-PMCW Automotive Radar Systems

MLS-based Transmitter Orthogonality Analysis in MIMO-PMCW Au...

引用

sensor array and multichannel signal processing workshop

作者： Moritz Kahlert Tai Fei Norwin Wilden Claas Tebruegge Markus Gardill HELLA GmbH & Co. KGaA Lippstadt Germany Brandenburg University of Technology Cottbus-Senftenberg Germany University of Applied Sciences and Arts Dortmund Germany

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Precise angular measurements are essential for au-tomated driving functions, and the antenna aperture and the number of antennas in a radar system determine the angle measuring ability. Recently, phase-modulated continuous wave (PMCW) technology has been integrated into multiple input multiple output (MIMO) systems, enabling a larger antenna aperture by multiplexing multiple transmitter antennas simul-taneously. In this work, we evaluate the performance of a large-scale MIMO-PMCW radar system in simulated scenarios using the peak-sidelobe level (PSL) after range-Doppler processing as a performance metric. The results show that the sequence length and the relative velocity in radar systems strongly influence the detection performance.

关键词： Antenna measurements Radar measurements Transmitting antennas Radar Radar antennas Aperture antennas Radar signal processing

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Learning the Topology of a Simplicial Complex Using Simplicial signals: A Greedy Approach

Learning the Topology of a Simplicial Complex Using Simplici...

引用

sensor array and multichannel signal processing workshop

作者： Andrei Buciulea Elvin Isufi Geert Leus Antonio G. Marques King Juan Carlos University Madrid Spain Delft University of Technology Delft The Netherlands

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Graphs are ubiquitous to model the irregular (non-Euclidean) structure of complex data, but they are limited to pairwise relationships and fail to model the complexities of the datasets exhibiting higher-order interactions. In that context, simplicial complexes (SCs) are emerging as a tractable candidate to handle such domains. The first step in using SC-based processing and learning schemes is to identify the topology of the SC, which is the problem investigated in this paper. In particular, we assume that we observe a number of signals (features) associated with the nodes of the SC (simplices of order 0) as well as signals (features) associated with a subset of the edges of the SC (simplices of order 1). The goal is then to use these signals to learn the remaining edges as well as the triangles that are filled (simplices of order 2). To address this problem, we assume that the signals are smooth on the unknown SC and that the higher-order relations are sparse. We then postulate the learning problem as a nonconvex optimization and develop an efficient (block-coordinate) algorithm to identify the SC topology.

关键词： array signal processing Conferences signal processing algorithms Data models Inference algorithms Topology Complexity theory

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Variable Selection for Max-Affine Regression via Sparse Gradient Descent

Variable Selection for Max-Affine Regression via Sparse Grad...

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sensor array and multichannel signal processing workshop

作者： Haitham Kanj Seonho Kim Kiryung Lee Department of Electrical and Computer Engineering The Ohio State University Columbus OH USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Sparse max-affine regression is introduced as a solution to variable selection for non-linear learning problems. A non-asymptotic local convergence analysis for Sparse Gradient Descent (Sp-GD) is presented when covariates are independent and identically distributed Gaussian random vectors. If at most $s$ out of $d$ covariates are actively contributing to the explanation of the target variable and the max-affine model combines a fixed number of linear models, then a suitably initialized Sp-GD linearly approaches the optimal solution and the true active covariates given $n=\mathcal{O}(s\log[(n\vee ed)/s])$ noise-free samples. Numerical Monte Carlo results corroborate these theoretical findings on the phase transition of exact parameter recovery.

关键词： Monte Carlo methods Input variables Conferences signal processing algorithms Vectors Numerical models Complexity theory

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Decentralized Non-Smooth Optimization Over the Stiefel Manifold

Decentralized Non-Smooth Optimization Over the Stiefel Manif...

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sensor array and multichannel signal processing workshop

作者： Jinxin Wang Jiang Hu Shixiang Chen Zengde Deng Anthony Man-Cho So Department of Syst. Eng. and Eng. Manag. CUHK Hong Kong SAR Mass General Hospital and Harvard Medical School Harvard University USA School of Mathematical Sciences USTC China ByteDance China

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

We focus on a class of non-smooth optimization problems over the Stiefel manifold in the decentralized setting, where a connected network of many agents cooperatively mini-mize a finite-sum objective function with each component being weakly convex in the ambient Euclidean space. Such optimization problems, albeit frequently encountered in applications, are quite challenging due to their non-smoothness and non-convexity. To tackle them, we propose an iterative method called the decentralized Riemannian sub gradient method (DRSM). When the problem at hand possesses a sharpness property, we show the local linear convergence of DRSM using geometrically di-minishing stepsizes. Numerical experiments are conducted to demonstrate the superior performance of DRSM in different applications.

关键词： Manifolds Gradient methods array signal processing Conferences Linear programming Iterative methods Optimization

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