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检索条件"任意字段=2010 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2010"

共 1026 条记录，以下是221-230 订阅

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Hybrid Beamforming Design for Downlink MU-MIMO-OFDM Millimeter-Wave Systems

Hybrid Beamforming Design for Downlink MU-MIMO-OFDM Millimet...

引用

sensor array and multichannel signal processing workshop

作者： Sepideh Gherekhloo Khaled Ardah Martin Haardt Communications Research Laboratory (CRL) TU Ilmenau Ilmenau Germany

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

Hybrid beamforming (HBF) structures have been recently proposed as one of the solutions to reduce cost and energy consumption of fully-digital multi-user MIMO systems in the millimeter-wave range. However, most of the initial references on HBF design assume frequency-flat single-carrier MIMO systems. Their extension to the frequency-selective multi-carrier MIMOOFDM case is, in general, not a straightforward task, especially in partially-connected architectures. In this paper, we propose a new HBF design approach for multi-user massive MIMO-OFDM systems. Simulation results reveal that the proposed solution achieves a comparable performance to the benchmark methods in the fully-connected case, while significantly outperforming them in case of partially-connected architectures.

关键词： Energy consumption array signal processing Simulation Millimeter wave technology Interference Benchmark testing Downlink

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Integrated waveform design for an Integrated Radar and Communication System with a Uniform Linear array

Integrated waveform design for an Integrated Radar and Commu...

引用

sensor array and multichannel signal processing workshop

作者： Mengchao Jiang Guisheng Liao Zhiwei Yang Yongjun Liu Yufeng Chen Haichuan Li National Laboratory of Radar Signal Processing Xidian University Xi’an China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

To reduce the consumption of spectrum and hardware resource, two methods are proposed to design the integrated waveform that can satisfy the requirements for both radar and communication. First, a linear superposition (LS) method is proposed to design the transmit waveforms of a uniform linear array (ULA). In the far field of the ULA, the designed transmit waveforms can simultaneously synthesize the desired radar and communication waveforms in different directions. However, the designed waveforms usually have high peak-to-average power ratio (PAPR). Then, an alternative projection (AP) method is proposed to reduce the PAPR of the transmit waveforms. The AP method can devise the waveforms with constant modulus. Both the proposed methods can perform radar and communication function in different directions. Finally, several simulation results are given to verify the effectiveness of the proposed methods.

关键词： Filtering Simulation Design methodology Conferences Radar Peak to average power ratio Radar signal processing

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Greedy coordinate descent method on non-negative quadratic programming

Greedy coordinate descent method on non-negative quadratic p...

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sensor array and multichannel signal processing workshop

作者： Chenyu Wu Yangyang Xu Department of Mathematical Sciences Rensselaer Polytechnic Institute Troy NY USA

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

The coordinate descent (CD) method has recently become popular for solving very large-scale problems, partly due to its simple update, low memory requirement, and fast convergence. In this paper, we explore the greedy CD on solving non-negative quadratic programming (NQP). The greedy CD generally has much more expensive per-update complexity than its cyclic and randomized counterparts. However, on the NQP, these three CDs have almost the same per-update cost, while the greedy CD can have significantly faster overall convergence speed. We also apply the proposed greedy CD as a subroutine to solve linearly constrained NQP and the non-negative matrix factorization. Promising numerical results on both problems are observed on instances with synthetic data and also image data.

关键词： array signal processing Algorithms Conferences Memory management Complexity theory Quadratic programming Convergence

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Extended Object Tracking Using Hierarchical Truncation Model with Partial-View Measurements

Extended Object Tracking Using Hierarchical Truncation Model...

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sensor array and multichannel signal processing workshop

作者： Yuxuan Xia Pu Wang Karl Berntorp Hassan Mansour Petros Boufounos Philip V. Orlik Chalmers University of Technology Sweden

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

This paper introduces the hierarchical truncated Gaussian model in representing automotive radar measurements for extended object tracking. The model aims at a flexible spatial distribution with adaptive truncation bounds to account for partial-view measurements caused by self-occlusion. Built on a random matrix approach, we propose a new state update step together with an adaptively update of the truncation bounds. This is achieved by introducing spatial-domain pseudo measurements and by aggregating partial-view measurements over consecutive time-domain scans. The effectiveness of the proposed algorithm is verified on a synthetic dataset and an independent dataset generated using the MathWorks Automated Driving toolbox.

关键词： Adaptation models Graphical models Radar measurements signal processing algorithms Radar Radar tracking Object tracking

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On the convergence of Jacobi-type algorithms for Independent Component Analysis

On the convergence of Jacobi-type algorithms for Independent...

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sensor array and multichannel signal processing workshop

作者： Jianze Li Konstantin Usevich Pierre Comon Shenzhen Research Institute of Big Data Shenzhen China CNRS CRAN Universite de Lorrainé Nancy France CNRS GIPSA-Lab Univ. Grenoble Alpes Grenoble France

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

Jacobi-type algorithms for simultaneous approximate diagonalization of real (or complex) symmetric tensors have been widely used in independent component analysis (ICA) because of their good performance. One natural way of choosing the index pairs in Jacobitype algorithms is the classical cyclic ordering, while the other way is based on the Riemannian gradient in each iteration. In this paper, we mainly review in an accessible manner our recent results in a series of papers about weak and global convergence of these Jacobitype algorithms. These results are mainly based on the Lojasiewicz gradient inequality.

关键词： Jacobian matrices Tensors Conferences signal processing algorithms Independent component analysis Approximation algorithms Cost function

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Distributed Nonnegative Tensor Canonical Polyadic Decomposition With Automatic Rank Determination

Distributed Nonnegative Tensor Canonical Polyadic Decomposit...

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sensor array and multichannel signal processing workshop

作者： Lei Cheng Xueke Tong Yik-Chung Wu Shenzhen Research Institute of Big Data The University of Hong Kong

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

Nonnegative tensor canonical polyadic decomposition (CPD) has found wide-spread applications in various signal processing tasks. However, the implementation of most existing algorithms needs the knowledge of tensor rank, which is difficult to acquire. To address this issue, by interpreting the nonnegative CPD problem using probability density functions (pdfs), the problem is recasted as probabilistic inference with integrated feature of automatic rank determination. Furthermore, to scale the inference algorithm to massive data, its implementation under modern distributed computing architecture is investigated, giving rise to a distributed probabilistic nonnegative tensor CPD algorithm. Numerical studies are presented to show the remarkable performance of the proposed algorithms in terms of accuracy and scalability.

关键词： Tensors Scalability signal processing algorithms Distributed databases Computer architecture Probability density function Probabilistic logic

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Waveform Design for Dual-function MIMO Radar-communication Systems

Waveform Design for Dual-function MIMO Radar-communication S...

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sensor array and multichannel signal processing workshop

作者： Bo Tang Hai Wang Lilong Qin Longxiang Li College of Electronic Engineering National University of Defense Technology Hefei China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

This paper addresses the design of constant-modulus waveforms for a dual-function multiple-input-multiple-output (MIMO) radar-communication system. The purpose of the design is to match a desired beampattern for radar sensing, and minimize the transmission distortion of communication signals. To tackle the non-convex waveform design problem, we develop an iterative algorithm based on cyclic optimization and majorization-minimization (MM). We show that the proposed algorithm has guaranteed convergence of the objective values. Numerical results demonstrate that the transmit beampattern of the synthesized waveforms can well approximate the desired one, and the emitted communication signals from the dual-function system has little distortions.

关键词： signal processing algorithms Radar Distortion Approximation algorithms sensors Iterative methods MIMO communication

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Target Reflectivity Characterization for FDA Radar

Target Reflectivity Characterization for FDA Radar

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sensor array and multichannel signal processing workshop

作者： Ronghua Gui Wen-Qin Wang Hing-Cheung So Can Cui University of Electronic Science and Technology of China Chengdu China City University of Hong Kong Hong Kong China Shanghai Electronic Mechanical Institute Shanghai China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

As an emerging array processing technique, frequency diverse array (FDA) differs from conventional phased-array in that it employs a frequency increment across the array elements. The use of frequency increment provides FDA with an array factor with joint range-angle dependency, which finds wide applications in joint range-angle target localization and range-dependent interference/clutter suppression. In the open literature, an ideal point-like target in far field is generally assumed for FDA signal modelling. However, the reflectivity characterization for more realistic targets, which are not ideally point-like and even extended in range and azimuth angle, has not been reported for FDA radar. In this paper, we establish an echo signal model of FDA radar for a general target, and then analyze the statistics of the echo signal amplitude. More specifically, we reveal the amplitude decorrelation property between different FDA elements due to the use of frequency increment, which provides useful insight into frequency increment design. The target reflectivity characteristic analysis is validated by numerical results.

关键词： Reflectivity Phased arrays Radar cross-sections array signal processing Radar Frequency diversity Radar signal processing

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Iterative Tensor Receiver for MIMO-GFDM systems

Iterative Tensor Receiver for MIMO-GFDM systems

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sensor array and multichannel signal processing workshop

作者： Damir Rakhimov Sai Pavan Deram Bruno Sokal Kristina Naskovska André de Almeida Martin Haardt Communications Research Laboratory Ilmenau University of Technology Ilmenau Germany Department of Teleinformatics Engineering Federal University of Ceara (UFC) Fortaleza Brazil

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

In this paper, we present a tensor MIMO-GFDM system model that is based on the double contraction operator. Based on the derived system model, we propose an iterative tensor based MIMOGFDM receiver, that is initialized with the channel estimation obtained via pilots transmitted in the first data frame. The proposed algorithm exploits the tensor structure by using several unfoldings of the received signal sequentially to obtain estimates of the transmitted symbols and the channel. Simulation results show the tensor gain for the proposed algorithm in addition to the improved channel estimation. Numerical results confirm that the receiver requires the same amount of pilots as the Zero Forcing (ZF) receiver, while having a better symbol error rate (SER) performance and a better channel estimation accuracy.

关键词： Tensors Error analysis Simulation Channel estimation signal processing algorithms Receivers Data models

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Optimization Inspired Learning Network for Multiuser Robust Beamforming

Optimization Inspired Learning Network for Multiuser Robust ...

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sensor array and multichannel signal processing workshop

作者： Minghe Zhu Tsung-Hui Chang School of Science & Engineering The Chinese University of Hong Kong Shenzhen China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

For real-time wireless networks with strict latency and energy constraints, deep neural networks have been used to approximate the resource allocation solutions that are previously obtained by advanced but computationally expensive optimization algorithms. In this paper, we consider the multi-user beamforming design problem for sum rate maximization in multi-antenna interference channels. Specifically, we propose a gradient projection inspired recurrent neural network for efficient beamforming optimization. The key ingredient is to explore the structure of the gradient vector of the sum rate so that the network learns only a set of dimension reduced coefficients. Furthermore, we extend it to the robust beamforming design for worst-case sum rate maximization in the presence of bounded channel errors. Numerical results show that the proposed learning networks can achieve high accuracy of the sum rates while with significantly reduced runtime.

关键词： Runtime Recurrent neural networks array signal processing Wireless networks signal processing algorithms Real-time systems Interference channels

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