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检索条件"任意字段=2008 IEEE Sensor Array and Multichannel Signal Processing Workshop"

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Joint beamforming and transmit design for the non-regenerative MIMO broadcast relay channel

Joint beamforming and transmit design for the non-regenerati...

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sensor array and multichannel signal processing workshop

作者： Jens Steinwandt Sergiy A. Vorobyov Martin Haardt Communications Research Laboratory Ilmenau University of Technology Ilmenau Germany Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

In this paper, we consider a multiple-input multiple-output (MIMO) broadcast relay channel (BRC), in which the communication of a multi-antenna base station (BS) with several multi-antenna mobile stations (MS) is assisted by a fixed half-duplex multi-antenna relay station (RS). Applying dirty paper coding (DPC) at the BS and beamforming at the RS, we jointly optimize the transmit covariance matrices at the BS and the beamforming matrix at the RS by maximizing the system sum rate, which is a nonconvex problem. To solve this problem, we resort to the more tractable sum rate maximization in the dual multiple access relay channel (MARC), which is still a nonconvex difference of convex functions (DC) programming problem. We develop an iterative algorithm, termed alternating matrix polynomial time DC (POTDC) algorithm, based on an alternating optimization of the beamforming matrix and the transmit covariance matrices. The resulting covariance matrices for the MARC are then mapped to the desired BRC covariance matrices. The sum rate performance of the proposed algorithm is demonstrated by simulations.

关键词： Covariance matrices MIMO Relays array signal processing signal processing algorithms Optimization

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Imputation of streaming low-rank tensor data

Imputation of streaming low-rank tensor data

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sensor array and multichannel signal processing workshop

作者： Morteza Mardani Gonzalo Mateos Georgios B. Giannakis Dept. of ECE and Digital Technology Center University of Minnesota

Unraveling latent structure by means of multilinear models of tensor data is of paramount importance in timely inference tasks encountered with `Big Data' analytics. However, increasingly noisy, heterogeneous, and incomplete datasets as well as the need for real-time processing of streaming data pose major challenges to this end. The present paper introduces a novel online (adaptive) algorithm to decompose low-rank tensors with missing entries, and perform imputation as a byproduct. The novel estimator minimizes an exponentially-weighted least-squares fitting error along with a separable regularizer of the PARAFAC decomposition factors, to trade-off fidelity for complexity of the approximation captured by the decomposition's rank. Leveraging stochastic gradient descent iterations, a scalable, real-time algorithm is developed and its convergence is established under simplifying technical assumptions. Simulated tests with cardiac magnetic resonance imagery (MRI) data confirm the efficacy of the proposed algorithm in imputing up to 75% missing entries.

关键词： Tensile stress signal processing algorithms Approximation algorithms arrays Matrix decomposition Magnetic resonance imaging Real-time systems

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Fast multilinear Singular Value Decomposition for higher-order Hankel tensors

Fast multilinear Singular Value Decomposition for higher-ord...

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sensor array and multichannel signal processing workshop

作者： Maxime Boizard Rémy Boyer Gérard Favier Pascal Larzabal SATIE Cachan France LSS Gifsur-Yvette France I3S Université de Nice Sophia Antipolis Sophia Antipolis France

The Higher-Order Singular Value Decomposition (HOSVD) is a possible generalization of the Singular Value Decomposition (SVD) to tensors, which have been successfully applied in various domains. Unfortunately, this decomposition is computationally demanding. Indeed, the HOSVD of a N th -order tensor involves the computation of the SVD of N matrices. Previous works have shown that it is possible to reduce the complexity of HOSVD for third-order structured tensors. These methods exploit the columns redundancy, which is present in the mode of structured tensors, especially in Hankel tensors. In this paper, we propose to extend these results to fourth order Hankel tensor. We propose two ways to extend Hankel structure to fourth order tensors. For these two types of tensors, a method to build a reordered mode is proposed, which highlights the column redundancy and we derive a fast algorithm to compute their HOSVD. Finally we show the benefit of our algorithms in terms of complexity.

关键词： Tensile stress signal processing algorithms Redundancy Complexity theory Harmonic analysis arrays

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Sparse delay-Doppler image reconstruction under off-grid problem

Sparse delay-Doppler image reconstruction under off-grid pro...

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sensor array and multichannel signal processing workshop

作者： Oguzhan Teke Ali Cafer Gurbuz Orhan Arikan Department of Electrical Electronics Engineering Bilkent University Turkey Department of Electrical Electronics Engineering TOBB ETU Turkey

Pulse-Doppler radar has been successfully applied to surveillance and tracking of both moving and stationary targets. For efficient processing of radar returns, delay-Doppler plane is discretized and FFT techniques are employed to compute matched filter output on this discrete grid. However, for targets whose delay-Doppler values do not coincide with the computation grid, the detection performance degrades considerably. Especially for detecting strong and closely spaced targets this causes miss detections and false alarms. Although compressive sensing based techniques provide sparse and high resolution results at sub-Nyquist sampling rates, straightforward application of these techniques is significantly more sensitive to the off-grid problem. Here a novel and OMP based sparse reconstruction technique with parameter perturbation, named as PPOMP, is proposed for robust delay-Doppler radar processing even under the off-grid case. In the proposed technique, the selected dictionary parameters are perturbed towards directions to decrease the orthogonal residual norm. A new performance metric based on Kull-back-Leibler Divergence (KLD) is proposed to better characterize the error between actual and reconstructed parameter spaces.

关键词： Vectors signal processing Radar imaging Delays Doppler effect Image reconstruction

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Tensor-based power spectra separation and emitter localization for cognitive radio

Tensor-based power spectra separation and emitter localizati...

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sensor array and multichannel signal processing workshop

作者： Xiao Fu Nicholas D. Sidiropoulos Wing-Kin Ma Department of EE Chinese University of Hong Kong Department of ECE University of Minnesota

This paper considers the problem of separating the power spectra and mapping the locations of co-channel transmitters using compound measurements from multiple sensors. This kind of situational awareness is important in cognitive radio practice, for spatial spectrum interpolation, transmission opportunity mining, and interference avoidance. Using temporal auto- and cross-correlations of the sensor outputs, it is shown that the power spectra separation task can be cast as a tensor decomposition problem in the Fourier domain. In particular, a joint diagonalization or (symmetric) parallel factor analysis (PARAFAC) model emerges, with one loading matrix containing the sought power spectra - hence being nonnegative, and locally sparse. Exploiting the latter two properties, it is shown that a very simple algebraic algorithm can be used to speed up the factorization. Assuming a path loss model, it is then possible to identify the transmitter locations by focusing on exclusively used (e.g., carrier) frequencies. The proposed approaches offer identifiability guarantees, and simplicity of implementation. Simulations show that the proposed approaches are effective in separating the spectra and localizing the transmitters.

关键词： Spectral analysis Radio transmitters signal processing algorithms Cognitive radio Receivers sensors Correlation

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Massive MIMO for interference management in heterogeneous networks

Massive MIMO for interference management in heterogeneous ne...

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sensor array and multichannel signal processing workshop

作者： Johannes Dommel Paul-Philipp Knust Lars Thiele Thomas Haustein Fraunhofer Institute for Telecommunications Heinrich Hertz Institute Einsteinufer 37 Berlin Germany

In this paper, a spatial interference coordination scheme for a typical macro/small-cell overlay scenarios is proposed which protects small-cell user equipments (UEs) from macro-cell interference and has potential applications in massive multiple-input multiple-output (MIMO) deployments. We assume the macro base stations (BSs) to be equipped with a large number of transmit antennas which are employed to reduce interference towards small-cell users by means of spatial transmit processing. Uncoordinated spatial multiplexing (SMUX) per access point (AP) is considered as a baseline, i.e. the macro BS uses zero-forcing (ZF)-based block diagonalization (BD) [1] to serve their user without taking interference towards small-cell UEs into account. The proposed method considers a joint BD/interference-alignment (IA) - approach to calculate the macro pre-coder and reduces the dimension of the interference sub-space for small-cell UEs without destroying the orthogonality of the macro BD. It results, that the the interference can be reduced even if the number of protected small-cell UEs exceeds the number of free spatial dimensions at the macro BS.

关键词： Interference MIMO Transmitting antennas Multiplexing Vectors signal processing Conferences

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Decentralized direction finding using Lanczos method

Decentralized direction finding using Lanczos method

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sensor array and multichannel signal processing workshop

作者： Wassim Suleiman Pouyan Parvazi Marius Pesavento Abdelhak Zoubir Department of Computer Science and Information Engineering National Chung Cheng University Chiayi County Taiwan Technische Universität Darmstadt Germany Darmstadt University of Technology Germany

The problem of Direction of Arrival (DoA) estimation using partly calibrated arrays composed of multiple identically oriented subarrays is considered. The subarrays are assumed to possess the shift invariance property which is exploited to achieve decentralized search free DoA estimation based on the ESPRIT method. In our previous work, the decentralized power method and the Averaging Consensus (AC) algorithm were used in the subspace estimation. To reduce the communicational cost without compromising the performance, our new algorithm uses the decentralized Lanczos method in combination with the AC algorithm to estimate the signal subspace. We further address the problem of Spurious Eigenvalues (SEVs) that usually arises in the Lanczos method. We propose a scheme to avoid the occurrence of SEVs while keeping the communicational cost low. Simulation results demonstrate that the proposed scheme is able to achieve similar performance as the decentralized power method with substantially reduced communicational cost. Furthermore, our method is able to estimate more DoAs than each subarray can autonomously identify.

关键词： arrays Estimation Eigenvalues and eigenfunctions Direction-of-arrival estimation signal processing algorithms Covariance matrices Vectors

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Antenna array configurations for massive MIMO outdoor base stations

Antenna array configurations for massive MIMO outdoor base s...

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sensor array and multichannel signal processing workshop

作者： Xavier Artiga Julien Perruisseau-Carrier Ana I. Pérez-Neira Centre Tecnològic de Telecomunicacions de Catalunya (CTTC) Barcelona Spain Adaptive MicroNano Wave Systems Group Ecole Poly technique Federale de Lausanne (EPFL) Lausanne Switzerland

Massive MIMO predicts unprecedented capacity and energy efficiency improvements in future networks, by the use of base stations with hundreds of antennas. However the crucial question of determining suitable, or even optimal, antenna hardware solutions for deploying such a large number of antennas in constrained physical spaces is still open. Here we analyse the potential benefits of using compact arrays, dual-polarized arrays, and 2D arrays in outdoor scenarios using a realistic 3D spatial channel model and accurate antenna electromagnetic simulations. In particular we show that it is detrimental to reduce the distance between elements below λ/3. Actually, better performances can be achieved by increasing the number of antennas using dual polarization instead (if random polarization of user antennas is assumed). We also show that good performance can be achieved by using 2D arrays, but at the expense of an increased number of total elements when compared to horizontal linear arrays.

关键词： arrays Mutual coupling MIMO Antenna arrays signal to noise ratio Channel models

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State estimation with sampling offsets in Wide Area Measurement Systems

State estimation with sampling offsets in Wide Area Measurem...

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sensor array and multichannel signal processing workshop

作者： Hoi-To Wai Anna Scaglione Department of Electrical and Computer Engineering University of California at Davis Davis CA USA

An implicit assumption made in studies on state estimation is that the time and frequency at which these measurements are taken is consistent across all the distributed sensing sites. For instance, in the literatures on Wide Area Measurement Systems (WAMS) deployed in the power grid, where the sensors equipped with Global Positioning signals (GPS), the sensing sites are deemed capable to provide perfectly synchronous readings at the various sampling sites. The validity of the assumption may need to be re-examined with the recent advancements in decentralized state estimation algorithms. Importantly, when there are timing offsets between sampling devices, the effects on the measurement system's performance can be catastrophic. The prevalent point of view is to either study the resulting error, or to resort to Kalman filtering for aligning the measurements. Taking on this view typically requires additional information about the underlying state. In this paper, we revisit the problem of state estimation and propose a new model for data acquisition under asynchronous sampling. The key idea is to apply sampling theory and to exploit the redundancy in the spatial sampling to interpolate the system state. We provide a necessary and sufficient condition for identifiability of the time offsets and propose an algorithm for the joint regression on state and timing offsets. The efficacy of the proposed algorithm is shown by numerical simulations.

关键词： sensors Timing Phasor measurement units signal processing algorithms State estimation Joints

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Efficient linear precoding for massive MIMO systems using truncated polynomial expansion

Efficient linear precoding for massive MIMO systems using tr...

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sensor array and multichannel signal processing workshop

作者： Axel Müller Abla Kammoun Emil Björnson Mérouane Debbah Alcatel-Lucent Chair on Flexible Radio Supélec France KAUST Thuwal Makkah Province Saudi Arabia Dept. of Signal Processing ACCESS KTH Sweden

Massive multiple-input multiple-output (MIMO) techniques have been proposed as a solution to satisfy many requirements of next generation cellular systems. One downside of massive MIMO is the increased complexity of computing the precoding, especially since the relatively “antenna-efficient” regularized zero-forcing (RZF) is preferred to simple maximum ratio transmission. We develop in this paper a new class of precoders for single-cell massive MIMO systems. It is based on truncated polynomial expansion (TPE) and mimics the advantages of RZF, while offering reduced and scalable computational complexity that can be implemented in a convenient parallel fashion. Using random matrix theory we provide a closed-form expression of the signal-to-interference-and-noise ratio under TPE precoding and compare it to previous works on RZF. Furthermore, the sum rate maximizing polynomial coefficients in TPE precoding are calculated. By simulation, we find that to maintain a fixed peruser rate loss as compared to RZF, the polynomial degree does not need to scale with the system, but it should be increased with the quality of the channel knowledge and signal-to-noise ratio.

关键词： MIMO Polynomials signal to noise ratio Interference Complexity theory Antennas Approximation methods

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