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A Fast and Scalable Polyatomic Frank-Wolfe Algorithm for the LASSO

IEEE signal processing LETTERS 2022年 29卷 637-641页

作者： Jarret, Adrian Fageot, Julien Simeoni, Matthieu Ecole Polytech Fed Lausanne Sch Comp & Commun Sci CH-1015 Lausanne Switzerland Ecole Polytech Fed Lausanne EPFL Ctr Imaging CH-1015 Lausanne Switzerland

We propose a fast and scalable polyatomic Frank-Wolfe (P-FW) algorithm for the resolution of high-dimensional LASSO regression problems. This algorithm improves upon traditional Frank-Wolfe methods by considering generalized greedy steps with polyatomic (i.e. linear combinations of multiple atoms) update directions, hence allowing for a more efficient exploration of the search space. To preserve sparsity of the intermediate iterates, we re-optimize the LASSO problem over the set of selected atoms at each iteration. For efficiency reasons, the accuracy of this re-optimization step is relatively low for early iterations and gradually increases with the iteration count. We provide convergence guarantees for our algorithm and validate it in simulated compressed sensing setups. Our experiments reveal that P-FW outperforms state-of-the-art methods in terms of runtime, both for FW methods and optimal first-order proximal gradient methods such as the Fast Iterative Soft-Thresholding Algorithm (FISTA).

关键词： signal processing algorithms Approximation algorithms Matching pursuit algorithms Convergence Optimization Search problems Minimization Conditional gradient Frank-Wolfe LASSO sparse recovery convex optimisation

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An Innovative Transient Analysis of Adaptive Filter With Maximum Correntropy Criterion

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IEEE signal processing LETTERS 2022年 29卷 1689-1693页

作者： Hou, Xinyan Zhao, Haiquan Long, Xiaoqiang Minist Educ Key Lab Magnet Suspens Technol & Maglev Vehicle Beijing 100816 Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China

The adaptive filtering algorithm based on the maximum correntropy criterion (MCC) is very effective in suppressing non-Gaussian noises and therefore attracts widespread attentions. At present, some works have been done for study the convergence and steady-state performance analysis of the MCC algorithm, but its transient performance analysis is still an open problem. To provide a comprehensive theoretical foundation for the MCC algorithm, we propose a method for transient performance analysis based on moment generating function (MGF). Since this method can efficiently calculate the expected value of the exponential term in the iterative update equation, it can avoid the discrepancies caused by introducing some approximation methods such as Taylor expansions in the analysis process. To date, there is no precedent for using this method to analyze the transient performance of the MCC algorithm. In addition, the steady-state performance and stability conditions of the MCC algorithm are discussed based on this method. Finally, the proposed analytical method is applied to the system identification problem, and the results show that the theoretical analysis results are agree well with the Monte Carlo simulation results.

关键词： signal processing algorithms Transient analysis Noise measurement Behavioral sciences Steady-state Kernel Prediction algorithms Adaptive filtering maximum correntropy criterion moment generating function performance analysis

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Improving Frame-Online Neural Speech Enhancement With Overlapped-Frame Prediction

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IEEE signal processing LETTERS 2022年 29卷 1422-1426页

作者： Wang, Zhong-Qiu Watanabe, Shinji Carnegie Mellon Univ Language Technol Inst Pittsburgh PA 15213 USA

Frame-online speech enhancement systems in the short-time Fourier transform (STFT) domain usually have an algorithmic latency equal to the window size due to the use of overlap-add in the inverse STFT (iSTFT). This algorithmic latency allows the enhancement models to leverage future contextual information up to a length equal to the window size. However, this information is only partially leveraged by current frame-online systems. To fully exploit it, we propose an overlapped-frame prediction technique for deep learning based frame-online speech enhancement, where at each frame our deep neural network (DNN) predicts the current and several past frames that are necessary for overlap-add, instead of only predicting the current frame. In addition, we propose a loss function to account for the scale difference between predicted and oracle target signals. Experiments on a noisy-reverberant speech enhancement task show the effectiveness of the proposed algorithms.

关键词： Prediction algorithms Speech enhancement Discrete Fourier transforms Spectrogram Predictive models signal processing algorithms Time-domain analysis Deep learning online speech enhancement

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Auto-Determining Mainlobe Width for Beampattern Synthesis via Relaxation Optimization

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IEEE signal processing LETTERS 2022年 29卷 314-318页

作者： Tang, Kangyu Zhang, Xuejing Univ Elect Sci & Technol China Chengdu 611731 Peoples R China

Thispaper presents a novelarray pattern synthesis algorithm based on relaxation optimization. We consider the problem of how to auto-determine the width of mainlobe region in beampattern synthesis. By introducing a slack region and a slack vector, the beampattern synthesis problem can be transformed as a relaxation optimization problem. The relaxation optimization problem is non-convex with an objective of sparsifying the slack vector. The sequential convex optimization procedure is then applied to solve the relaxation optimization problem and find its sparse solution. Representative simulations are provided to demonstrate the effectiveness of the proposed method in beampattern synthesis.

关键词： Optimization Convex functions signal processing algorithms Shape Radar antennas Genetic algorithms Convergence Beampattern synthesis relaxation optimization mainlobe width sparse optimization

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ENF Based Robust Media Time-Stamping

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IEEE signal processing LETTERS 2022年 29卷 1963-1967页

作者： Vatansever, Saffet Dirik, Ahmet Emir Memon, Nasir Bursa Tech Univ Dept Mechatron Engn TR-16333 Bursa Turkey Bursa Uludag Univ Fac Engn Dept Comp Engn TR-16059 Bursa Turkey NYU Tandon Sch Engn Dept Comp Sci & Engn New York NY 11201 USA

Electric Network Frequency (ENF) continuously fluctuates around a nominal value (50/60 Hz) due to a persistent imbalance between supplied and demanded power. In certain circumstances, ENF gets intrinsically embedded into audio and video recordings and can be extracted from these recordings. Consequently, ENF can be used in a number of media forensic applications, such as verifying the time of recording of the media. In this work, a robust media time-stamping approach is proposed for media whose ENF content is relatively contaminated. It essentially entails two procedures: first, detecting all useful, i.e., considerably accurate, samples of an estimated ENF signal, and then applying an adapted normalized cross-correlation process that is designed for exploiting just the selected ENF portions based on a binary mask of the identified accurate samples. Experimental results show that the proposed approach provides significantly increased performance.

关键词： Media Noise measurement Forensics Recording Harmonic analysis Estimation signal processing algorithms ENF electric network frequency media forensics multimedia forensics time-of-recording time-stamping

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Energy Efficiency Optimization of Reconfigurable Intelligent Surfaces With Electromagnetic Field Exposure Constraints

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IEEE signal processing LETTERS 2022年 29卷 1447-1451页

作者： Zappone, Alessio Di Renzo, Marco Univ Cassino & Southern Lazio I-03043 Cassino Italy Univ Paris Saclay CNRS Cent Supelec Lab Signaux & Syst F-91192 Gif Sur Yvette France

This work considers the problem of energy efficiency (EE) maximization in a reconfigurable intelligent surface (RIS)-aided multiple input multiple output (MIMO) communication link, subject to maximum power constraints and to additional constraints on the maximum exposure of the end-users to electromagnetic radiations. The RIS phase shifts, the transmit beamforming, the linear receive filter, and the transmit power are jointly optimized, and two provably convergent and low-complexity algorithms are developed. One algorithm applies to the general system setup, but does not guarantee global optimality. The other is provably optimal in the notable special case of isotropic electromagnetic field (EMF) exposure constraints. The numerical results show that an RIS ensures an EE of the same order of magnitude as when no EMF constraints are enforced.

关键词： signal processing algorithms Optimization Transmitting antennas Array signal processing Wireless communication Receiving antennas Radio transmitters RIS energy efficiency EMF exposure resource allocation

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Relationship Between Criteria Based on Correntropy and Second Order Statistics for Equalization of Communication Channels

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IEEE signal processing LETTERS 2022年 29卷 1317-1321页

作者： Gois, Lucas Suyama, Ricardo Fantinato, Denis Neves, Aline Fed Univ ABC BR-09210580 Santo Andre SP Brazil

Correntropy has been successfully used for signal processing in a large variety of applications. Usually, correntropy estimator uses a Gaussian kernel, which leads to a measure that takes into account all even order statistical moments of the underlying signal. In this paper we analyze correntropy implemented with the Epanechnikov kernel, which is given by a second order polynomial. Considering an equalization scenario, we compare such criterion with the one obtained through the use of the Gaussian kernel and also the Correlation Retrieval Criterion. We discuss similarities and differences, theoretically and through simulations, between the three criteria.

关键词： Kernel Equalizers Cost function Delays signal processing algorithms Estimation Binary phase shift keying Correntropy correlation retrieval Epanechnikov kernel second order statistics

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Distributed signal processing for Out-of-System Interference Suppression in Cell-Free Massive MIMO

Distributed Signal Processing for Out-of-System Interference...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Zakir Hussain Shaik Erik G. Larsson Department of Electrical Engineering (ISY) Linköping University Sweden

Cell-free massive multiple-input-multiple-output (CF-mMIMO) is a next-generation wireless access technology that offers superior coverage and spectral efficiency compared to conventional MIMO. With many future applications in unlicensed spectrum bands, networks will likely experience and may even be limited by out-of-system (OoS) interference. The OoS interference differs from the in-system interference from other serving users in that for OoS interference, the associated pilot signals are unknown or non-existent, which makes estimation of the OoS interferer channel *** this paper, we propose a novel sequential algorithm for the suppression of OoS interference for uplink CF-mMIMO with a stripe (daisy-chain) topology. The proposed method has comparable performance to that of a fully centralized interference rejection combining algorithm but has substantially less fronthaul load requirements.

关键词： Wireless communication Spectral efficiency signal processing algorithms Interference signal processing Numerical simulation Topology

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Performance Analysis of Separating Function Estimation Test for Impropriety of Complex signals

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IEEE signal processing LETTERS 2022年 29卷 2013-2017页

作者： Lai, Huadong Xu, Weichao Dai, Jisheng Zhou, Yanzhou Yang, Qiyu Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Jiangsu Peoples R China

In this work, a separating function estimation test (SFET) based detector is proposed for the problem of impropriety test of complex signals. With the establishment of closed-form expressions concerning the first two raw moments of the proposed test statistic, we can obtain the analytic form for the probability of false alarm (PFA) based on moment-matching method. Moreover, the threshold can be further obtained for a given user-specified PFA. Numerical examples are provided to verify our theoretical results and demonstrate the performance gain of our proposed detector.

关键词： Detectors Coherence signal processing algorithms Eigenvalues and eigenfunctions Maximum likelihood estimation Probability Performance gain Separating function estimation test (SFET) impropriety test canonical correlation coefficients (CCCs)

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Smart DC Optimizer for DC Series Arc Fault Detection and Extinguishing

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IEEE TRANSACTIONS ON POWER ELECTRONICS 2022年第9期37卷 10117-10121页

作者： Park, Hwa-Pyeong Kim, Mina Chae, Suyong Korea Inst Energy Res KIER Daejeon 34129 South Korea Korea Elect Technol Inst KETI Naju 13509 South Korea Pohang Univ Sci & Technol POSTECH Pohang 37673 South Korea

A dc arc faultdetection has been studied to improve the reliability of renewable energies and energy storage systems. In this Letter, the control algorithm of dc optimizer is proposed to detect the dc series arc fault condition between the PV panel and dc optimizer. The operational principle of the detection algorithm considers the characteristics of both the PV panel and arc fault, which can detect and extinguish the dc series arc fault condition. The validity of the proposed algorithm is verified through the simulation and experimental results.

关键词： Circuit faults Resistance Fault detection Voltage control signal processing algorithms Load modeling Integrated circuit modeling Arc discharges fault diagnosis DC-DC power converters photovoltaic systems

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