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检索条件"任意字段=13rd IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2024"

共 89 条记录，以下是61-70 订阅

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Decentralized Non-Smooth Optimization Over the Stiefel Manifold

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

引用

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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High-Resolution DOA Estimation Using Single-Snapshot Music for Automotive Radar with Mixed-ADC Allocations

High-Resolution DOA Estimation Using Single-Snapshot Music f...

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sensor array and multichannel signal processing workshop

作者： Moritz Kahlert Lifan Xu Tai Fei Markus Gardill Shunqiao Sun HELLA GmbH & Co. KGaA Lippstadt Germany Department of Electrical and Computer Engineering University of Alabama Tuscaloosa AL USA University of Applied Sciences and Arts Dortmund Germany Brandenburg University of Technology Cottbus Germany

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Fine direction of arrival (DOA) estimations are required for accurate target detections in automotive radar systems. To address this issue, most spectral estimation methods assume many snapshots of measurements. However, due to the dynamic nature of automotive scenarios, methods using multiple snapshots are impractical for DOA estimation in automotive radars. Furthermore, to relax the hardware requirements on modern automotive radar systems, mixed-analog-to-digital converter (ADC) allocations, i.e., the coexistence of 1-bit and high-resolution ADCs, have gained more attention recently. In this work, we introduce a high-resolution DOA estimation approach based on single-snapshot multiple signal classification (MUSIC) estimation and evaluate the performance with various ADC allocations. The results show that mixed-ADC allocations can perform comparably to high-resolution ADC allocations.

关键词： Direction-of-arrival estimation array signal processing Estimation Radar detection Radar Multiple signal classification Resource management

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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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Near or Far: On Determining the Appropriate Channel Estimation Strategy in Cross-Field Communication

Near or Far: On Determining the Appropriate Channel Estimati...

引用

sensor array and multichannel signal processing workshop

作者： Simon Tarboush Anum Ali Tareq Y. Al-Naffouri Independent Researcher Dubai UAE Standards and Mobility Innovation Lab Samsung Research America Texas USA Department of CEMSE Information Science Lab King Abdullah University of Science and Technology Thuwal KSA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

The use of ultra-massive multiple-input multiple-output and high-frequency large bandwidth systems is likely in the next-generation wireless communication systems. In such systems, the user moves between near- and far-field regions, and consequently, the channel estimation will need to be carried out in the cross-field scenario. Channel estimation strategies have been proposed for both near- and far-fields, but in the cross-field problem, the first step is to determine whether the near- or far-field is applicable so that an appropriate channel estimation strategy can be employed. In this work, we propose using a hidden Markov model over an ensemble of region estimates to enhance the accuracy of selecting the actual region. The region indicators are calculated using the pair-wise power differences between received signals across the subarrays within an array-of-subarrays architecture. Numerical results show that the proposed method achieves a high success rate in determining the appropriate channel estimation strategy.

关键词： Wireless communication Measurement array signal processing Channel estimation Hidden Markov models Parallel processing Next generation networking

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Distributed Sparse Subspace Clustering by K-Means Subspace Fusion

Distributed Sparse Subspace Clustering by K-Means Subspace F...

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sensor array and multichannel signal processing workshop

作者： Liang-Chi Huang Y.-W. Peter Hong Jwo-Yuh Wu Institute of Communications Engineering National Yang Ming Chiao Tung Univ. Hsinchu Taiwan Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Sparse subspace clustering (SSC) achieves evidenced success in many areas but most related works focus on the centralized scenario. In many practical applications such as security surveillance, several distributed agents jointly collect a large dataset, while each one is however just able to observe a subset through which a local inference is to be made. This paper proposes a collaborative distributed SSC scheme targeted for this situation. In the proposed scheme, each agent first conducts a fast local data clustering, and transmits basis matrices of the estimated subspaces to the data center, which then conducts subspace information fusion using a k-means type method; the aggregated subspace information is fed back to local agents to update their data partitions. Computer simulations using real human face data are used to illustrate the effectiveness of the proposed method.

关键词： Data centers Protocols Computer simulation Surveillance signal processing algorithms Collaboration Distributed databases

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Unrolling Decentralized Stochastic Frank Wolfe Algorithm

Unrolling Decentralized Stochastic Frank Wolfe Algorithm

引用

sensor array and multichannel signal processing workshop

作者： Robin Francis Sai Rajaji Ramakrishnan Sundeep Prabhakar Chepuri Indian Institute of Science Bengaluru India

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Decentralized Frank-Wolfe methods are suitable for solving the decentralized constrained optimization with clients having computational limitations, as they solve a linear program to find the descent direction instead of the computationally intense projection operation. However, traditional decentralized Frank-Wolfe approaches may exhibit limitations in scenarios where convergence speed is critical. To address this challenge, we propose an unrolled decentralized Frank-Wolfe method to improve convergence. In particular, each layer learns the best descent direction by training a neural network, facilitating faster convergence through layer-wise updates. Furthermore, we introduce two weight sharing techniques, namely intra-client and inter-client weight sharing mechanisms, that can reduce the training complexity associated with a growing number of network layers and clients, respectively. These techniques ensure scalability of the model and improves its performance. We demonstrate the efficacy of the proposed unrolled model on various machine-learning tasks for both synthetic and real-world datasets.

关键词： Training Scalability Stochastic processes signal processing algorithms Machine learning Task analysis Standards

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Antenna Placement in Compressive Sensing Radar using Binary Optimization

Antenna Placement in Compressive Sensing Radar using Binary ...

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sensor array and multichannel signal processing workshop

作者： Adnan Hamida Mohammed Saif Jun Li Shahrokh Valaee Electrical and Computer Engineering University of Toronto Toronto ON Canada NXP Semiconductors San Jose CA USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Compressive sensing has allowed for the improve-ment of angular resolution in radar technology, which involves two aspects: sparse signal recovery and measurement matrix design. Assuming a sparse target scene, compressive sensing radar depends solely on the design of the measurement matrix to possess certain properties, such as satisfying the restricted isometry property (RIP) and low coherence. The design of the measurement matrix depends on the location of the antennas. In this work, we consider the antenna placement problem in compressive sensing radar. The problem is interpreted as a binary program, where we propose to solve it directly using a heuristic binary optimization algorithm. The proposed binary differen-tial evolution (BDE) algorithm is able to navigate the search space with relatively high diversity while still refining promising candidates. Results illustrate the superiority of approaching the problem directly using BDE rather than resorting to relaxation approaches in the literature.

关键词： Antenna measurements Radar measurements signal processing algorithms Radar Coherence Radar antennas Sparse matrices

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Byzantine-Resilient Bilevel Federated Learning

Byzantine-Resilient Bilevel Federated Learning

引用

sensor array and multichannel signal processing workshop

作者： Momin Abbas Yi Zhou Nathalie Baracaldo Horst samulowitz Parikshit Ram Theodoros Salonidis Rensselaer Polytechnic Institute Troy NY USA IBM T. J. Watson Research Center Yorktown Heights NY USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

To tackle new learning criteria such as robustness and automation, many machine learning problems today involve nested structures and are thus often formulated as bilevel learning problems. To leverage data from multiple data owners, we consider bilevel learning in the federated setting in the presence of Byzantine clients. We propose a byzantine-resilient bilevel federated optimization algorithm that we call BILANTINE and provide a theoretical analysis establishing its convergence rate. We empirically demonstrate our method's effectiveness on the data reweighting task under various attacks and show that it can achieve nearly the same performance as the system would have achieved in the absence of attacks. To the best of our knowledge, this is the first empirical and theoretical study of federated bilevel optimization under Byzantine attacks.

关键词： Machine learning algorithms Automation Federated learning Conferences signal processing algorithms Robustness Task analysis

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Block Sparsity Based Channel Estimation for IRS-Assisted mmWave MIMO Systems

Block Sparsity Based Channel Estimation for IRS-Assisted mmW...

引用

sensor array and multichannel signal processing workshop

作者： Fang Guo Zhenhua Zhou Bin Liao Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen Guangdong Province China

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

In this paper, we consider the downlink channel estimation for the intelligent reflecting surface (IRS) assisted MIMO systems in the millimeter wave band. It is challenging to perform the channel estimation for the IRS-assisted wireless communication systems, due to the large amount of passive reflecting elements at the IRS. On account of the inherent sparsity of millimeter-wave channels, the sparse representation expressions of the channel between the base station (BS) and IRS and that between the IRS and user equipment (UE) are obtained separately. Furthermore, the cascaded channel is estimated by solving the BS-IRS and IRS-UE channels in an alternating way. To improve the estimation accuracy and reduce the pilot over-head, the algorithm of the block-structured orthogonal matching pursuit (BS-OMP) is devised to estimate the BS-IRS channel by exploiting its block sparsity. The simulation results show that the proposed method provides the higher estimation accuracy and consumes less pilot overhead by comparing with its counterparts.

关键词： Wireless communication Accuracy Simulation Millimeter wave technology Channel estimation Matching pursuit algorithms Estimation

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Analysis of Cross Terms in Toeplitz Rectified sample Covariance Matrices

Analysis of Cross Terms in Toeplitz Rectified Sample Covaria...

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sensor array and multichannel signal processing workshop

作者： Vaibhav Chavali Kathleen E. Wage Electrical and Computer Engineering Department George Mason University Fairfax USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Toeplitz-rectification of a sample Covariance Matrix (SCM) is used to enhance the performance of array processing algorithms, particularly those relying on obtaining signal or noise subspace estimates, such as MUSIC Direction of Arrival (DoA) estimation and subspace-based adaptive beamforming. A Toeplitz-Rectified SCM (TR-SCM) is obtained by averaging along the diagonals of the SCM. This paper characterizes subspace estimates obtained using TR-SCMs in environments with a wide range of signal powers. Analysis of a single source in noise reveals a deterministic structure that provides valuable insights regarding the estimated subspaces. This paper derives an analytical prediction of the principal eigenvector of TR-SCMs. The eigenvector has reduced errors in directions orthogonal to the true source, but unlike the SCM, they are non-uniformly distributed. The consequence of this is observed in the noise subspace where eigenvectors corresponding to higher noise eigen-values tend to cluster around the true source. The paper presents a geometric picture of subspace estimates obtained through TR-SCMs. It shows that when estimating DoAs for multiple sources with diverse signal strengths, cross terms between a loud source and noise can significantly increase noise levels around the true source, thereby leading to inaccurate estimations of quieter sources.

关键词： Direction-of-arrival estimation Filters array signal processing Noise Estimation Multiple signal classification Covariance matrices

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