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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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8th ieee sensor array and multichannel signal processing workshop (SAM)

作者： Artiga, Xavier Perruisseau-Carrier, Julien Perez-Neira, Ana I. CTTC Barcelona Spain Ecole Polytech Fed Lausanne Adapt MicroNanoWave Syst Grp CH-1015 Lausanne Switzerland Univ Politecn Cataluna Dept Signal Theory & Commun Barcelona Spain

ISBN: (纸本)9781479914814

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 lambda/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： ANTENNA multiple input multiple output systems Antenna arrays BASE STATION channel model Elements Energy efficiency arrays

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HyperQUEEN-MF: Hyperspectral Quantum Deep Network with Multi-Scale Feature Fusion For Quantum Image Super-Resolution 13

HyperQUEEN-MF: Hyperspectral Quantum Deep Network with Multi...

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13rd ieee sensor array and multichannel signal processing workshop (SAM)

作者： Hsu, Shih-Min Lin, Tzu-Hsuan Lin, Chia-Hsiang Natl Cheng Kung Univ Dept Elect Engn Inst Comp & Commun Engn Tainan Taiwan Natl Cheng Kung Univ Dept Elect Engn Tainan Taiwan Natl Cheng Kung Univ Miin Wu Sch Comp Tainan Taiwan

ISBN: (纸本)9798350344820;9798350344813

Hyperspectral images (HSIs) have drawn considerable attention over the last decade thanks to their high spectral resolution. However, as hardware limitations often cause the low spatial resolution of HSIs compared to traditional RGB and multispectral images, the hyperspectral image super-resolution (HSR) problem becomes crucial in facilitating the subsequent material identification applications. Recently, we developed hyperspectral quantum deep network (HyperQUEEN) for satellite data restoration, thereby proving that quantum features do play some role in developing high-performance inverse imaging algorithms. Before HyperQUEEN, which elegantly performs the hyperspectral matrix/tensor factorization via the so-called low-rank module, other existing quantum image processing tasks can only achieve basic geometry transforms or simple classification-level tasks. In this work, we go a step further by investigating the quantum implementation of multi-scale feature fusion (MF) transformer and applying the HyperQUEEN-MF to solve the challenging inverse problem known as single HSR (SHSR). By learning multi-scale quantum features, the proposed HyperQUEEN-MF does show outstanding SHSR performances, while the experimental results also show the feasibility of quantum-driven hyperspectral super-resolution. This creates a door for solving other advanced challenging inverse problems via quantum feature information extraction and processing.

关键词： Quantum neural network hyperspectral image hyperspectral super-resolution quantum deep learning quantum image processing

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Mitigation of Ground Clutter in Airborne Bistatic Radar Systems 11

Mitigation of Ground Clutter in Airborne Bistatic Radar Syst...

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ieee 11th sensor array and multichannel signal processing workshop (SAM)

作者： Klintberg, Jacob McKelvey, Tomas Dammert, Patrik Chalmers Univ Technol Dept Elect Engn Gothenburg Sweden Saab AB Saab BA Surveillance Gothenburg Sweden

ISBN: (纸本)9781728119465

Space-Time Adaptive processing is a commonly used technique to mitigate ground clutter reflections from an airborne radar system. It estimates a covariance matrix based on spatial and temporal information, and the estimate is thereafter used to suppress the ground clutter. In a side-looking monostatic radar system, the estimate is rather straight forward based on radar observations. However, in this paper, we consider bistatic systems where the power of adaptivity is limited due to nonstationarity of the ground clutter reflections over the range dimension. To overcome this, scenario dependent transformations are commonly used when forming the sample covariance matrix. In this contribution we instead investigate a detector where the clutter covariance matrix is determined from the geometry of the bistatic scenario. Using a Monte-Carlo simulation, we investigate how sensitive the detector is to errors in the assumed geometry, and compare this with state-of-the-art adaptive methods. The results indicates that a good clutter rejection is obtained for errors of order 10(3) m for assumed transmitter position and 10(0) km/h for assumed transmitter velocity.

关键词： Space-Time Adaptive processing bistatic radar systems ground clutter mitigation

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Multi-Channel Reconstruction from a Randomly Sampled array

Multi-Channel Reconstruction from a Randomly Sampled Array

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3rd ieee International workshop on Computational Advances in Multi-sensor Adaptive processing (CAMSAP)

作者： Casey, Ryan B. Pesyna, Kenneth M. Smith, Christopher B. Southwest Res Inst San Antonio TX 78238 USA Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA

ISBN: (纸本)9781424451807

Compressive sampling has a rich theoretical background in a number of fields from image processing to medical imaging to geophysical data analysis. In this paper we explore compressive sampling from the perspective of classic array processing. We derive a basis appropriate for reconstructing multi-channel data which is sparsely sampled from a uniform linear array. Here we reconstruct both time and spatial components of the signal using randomly sampled time series. We present both theoretical and experimental evidence that compressive sampling can be successful for traditional array processing applications.

关键词： Compressed sensing

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Novel nonlinear functions used for optimal detection in Gaussian mixture noise based on maximum entropy densities

Novel nonlinear functions used for optimal detection in Gaus...

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4th ieee sensor array and multichannel signal processing workshop

作者： Saberali, S. Mohammad Amindavar, Hamidreza Kirlin, Rodney Lynn Amirkabir Univ Technol Tehran Iran Univ Victoria Victoria BC V8W 3P6 Canada

ISBN: (纸本)1424403081

Non-Gaussian noise poses a challenge to conventional detector techniques. In such a case it is possible to design a nonlinear detector that performs better than the optimal linear detectors. Locally optimal detector is one that has good performance when the signal is weak and the probability density function (PDF) of noise is known precisely. This paper deals with a new nonlinear detector for binary signal detection in Gaussian mixture noise. This detector is optimal without any constraints on signal strength, and it is convenient for non-Gaussian even symmetric PDF's. furthermore, it does not require the knowledge of the exact noise PDF We use maximum likelihood (ML) and maximum entropy, that are two optimal criteria, in obtaining this new detector. The proposed detector consists of new nonlinear functions followed by an accumulator and threshold comparator. These new nonlinear functions are polynomials consisting of odd power terms. Simulation results confirm the superiority of the new detector with respect to the previously proposed methods.

关键词： Gaussian noise (electronic)

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multichannel sparse recovery of complex-valued signals using Huber's criterion 3

Multichannel sparse recovery of complex-valued signals using...

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3rd International workshop on Compressed Sensing Theory and its Application to Radar, Sonar, and Remote Sensing (CoSeRa)

作者： Ollila, Esa Aalto Univ Dept Signal Proc & Acoust POB 13000 FI-00076 Aalto Finland

ISBN: (纸本)9781479974207

In this paper, we generalize Huber's criterion to multichannel sparse recovery problem of complex-valued measurements where the objective is to find good recovery of jointly sparse unknown signal vectors from the given multiple measurement vectors which are different linear combinations of the same known elementary vectors. This requires careful characterization of robust complex-valued loss functions as well as Huber's criterion function for the multivariate sparse regression problem. We devise a greedy algorithm based on simultaneous normalized iterative hard thresholding (SNIHT) algorithm. Unlike the conventional SNIHT method, our algorithm, referred to as HUB-SNIHT, is robust under heavy-tailed non-Gaussian noise conditions, yet has a negligible performance loss compared to SNIHT under Gaussian noise. Usefulness of the method is illustrated in source localization application with sensor arrays.

关键词： Gaussian noise array signal processing iterative methods regression analysis Huber criterion SNIHT algorithm complex-valued signal multichannel sparse recovery elementary vector linear combination greedy algorithm heavy-tailed nonGaussian noise condition multiple measurement vector multivariate sparse regression problem sensor array simultaneous normalized iterative hard thresholding algorithm source localization application Brain modeling Compressed sensing Conferences Radar Robustness Sparse matrices Yttrium Gaussian noise greedy algorithm Brain modeling sensor arrays Sparse matrix array signal processing Compressed sensing iterative methods Rare earths yttrium radar regression analysis Robustness

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New Insights into Second and Fourth-Order Direction Finding for NonCircular Sources

New Insights into Second and Fourth-Order Direction Finding ...

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8th ieee sensor array and multichannel signal processing workshop (SAM)

作者： Ferreol, Anne Chevalier, Pascal THALES Commun & Secur F-92622 Gennevilliers France CNAM CEDRIC Lab F-75141 Paris 3 France

ISBN: (纸本)9781479914814

These last three decades, many second order (SO) and higher order (HO) high resolution (HR) direction finding (DF) methods, such as 2q-MUSIC (q >= 1), exploiting the information contained in the SO or HO circular (C) cumulants of the data, have been developed. However, for 2qth-order non-circular (NC) sources such as M-PSK sources with M <= 2q, strong gains in performance may be obtained by taking into account the information contained in both 2qth-order C and NC cumulants of the data, giving rise to NC 2qth-order DF methods. Numerous NC DF methods have been developed these last fifteen years but mainly at the SO and under restrictive assumptions on the sources. The purpose of this paper is to give new insights into NC 2q-MUSIC methods for 1 <= q <= 2 and for arbitrary NC sources.

关键词： direction finding Numerical controls DEUTERIUM FLUORIDES NON-CIRCULAR CUMULANT Holmium

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Information theoretic criterion for waveform selection

Information theoretic criterion for waveform selection

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4th ieee sensor array and multichannel signal processing workshop

作者： Roman, Jaime R. Garnham, John W. Antonik, Paul SAIC 2109 Air Pk Rd SE Albuquerque NM 87111 USA Rome Res Site SNRT AF Res Lab Griffiss AFB NY 13441 USA

ISBN: (纸本)1424403081

A novel criterion for waveform selection in adaptive radar and other sensing applications is presented that is based on the information theoretic concept of mutual information. In addition, its application to the area of waveform diversity for synthetic aperture radar (SAR) systems is examined. Mutual information is a measure of the information (in the sense of Shannon) in a random variable or vector about another random variable or vector. It is shown herein that such a framework provides the basis for a conceptually simple and powerful criterion for context-dependent evaluation of candidate waveforms, as well as context-dependent design of waveforms. Furthermore, in the special case of zero-mean Gaussian-distributed clutter, interference, and noise, the criterion attains an analytically simple form - a scalar function of the singular values of a specific cross-covariance matrix. The criterion can be used in distinct optimization contexts (minimization or maximization) as a function of the problem to be addressed. In addition, the criterion inherently includes the statistical information of the sensed parameters. More specifically, in the context of a SAR system the criterion includes the covariance matrix of the set of illuminated ground scatterers. The formulation and simulation-based results are presented in the context of a first-order radar system model for simplicity, but the approach can be extended in a straightforward manner to cover more complex models. Results presented show that the criterion is an effective means for waveform selection in an adaptive SAR system.

关键词： Waveform analysis

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array processing estimation of non-linearly superimposed acoustic waves

Array processing estimation of non-linearly superimposed aco...

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1st ieee sensor array and multichannel signal processing workshop (SAM 2000)

作者： Piñero, G Vergara, L Univ Politecn Valencia Dept Comunicac Valencia 46022 Spain

ISBN: (纸本)0780363396

Acoustic pressure waves are non-linearly superimposed when a periodic gas flow is perturbing the propagating medium as' it arises in car exhaust systems. la such an environment, the application of standard beamforming techniques to the estimation of the forward and backward waves needs a previous reformulation to establish a linear superposition model, In this paper a new linearization method for the superposition of waves is proposed and compared to the classical assumption of linear pressure superposition, Moreover, a new problem formulation including the noise corruption of source waves is presented, In both new and classical linear contexts, three different beamformers - Delay-and-Sum (DS), Linearly Constrained Minimum Variance (LCMV) and Minimum mum Mean Squared Error (MMSE) - have been applied to numerical data generated by a thermodynamics modeling software. Results have shown that the new linearization method improves the backward wave estimation error in almost 3 dB for typical experiment conditions (few sensors and moderate signal-to-noise ratio).

关键词： Acoustic propagation Acoustic waves array signal processing Context modeling Estimation error Exhaust systems Fluid flow Nonlinear acoustics signal to noise ratio Thermodynamics

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EVALUATION OF EMERGING FREQUENCY DOMAIN CONVOLUTIVE BLIND SOURCE SEPARATION ALGORITHMS BASED ON REAL ROOM RECOrdINGS

EVALUATION OF EMERGING FREQUENCY DOMAIN CONVOLUTIVE BLIND SO...

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ieee sensor array and multichannel signal processing workshop

作者： Naqvi, S. M. Zhang, Y. Tsalaile, T. Sanei, S. Chambers, J. A. Univ Loughborough Dept Elect & Elect Engn Adv Signal Proc Grp Loughborough LE11 3TU Leics England Cardiff Univ Ctr Digital Signal Proc Cardiff CF24 3AA S Glam Wales

ISBN: (纸本)9781424422401

This paper presents a comparative study of three of the emerging frequency domain convolutive blind source separation (FDCBSS) techniques i.e. convolutive blind separation of non-stationary sources due to Parra and Spence, penalty function-based joint diagonalization approach for convolutive blind separation of nonstationary sources due to Wang et al. and a geometrically constrained multimodal approach for convolutive blind source separation due to Sanei et al. Objective evaluation is performed on the basis of signal to interference ratio (SIR), performance index (PI) and solution to the permutation problem. The results confirm that a multimodal approach is necessary to properly mitigate the permutation in BSS and ultimately to solve the cocktail party problem. In other words, it is to make BSS semiblind by exploiting prior geometrical information, and thereby providing the framework to find robust solutions for more challenging source separation with moving speakers.

关键词： Frequency domain (BSS) geometrical constraints orthogonal/nonorthogonal constraints penalty functions cocktail party problem and multimodal signal separation

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