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Location Sensing and Beamforming Design for IRS-Enabled Multi-User ISAC Systems

IEEE TRANSACTIONS ON signal processing 2022年 70卷 5178-5193页

作者： Yu, Zhouyuan Hu, Xiaoling Liu, Chenxi Peng, Mugen Zhong, Caijun Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China

This paper explores the potential of the intelligent reflecting surface (IRS) in realizing multi-user concurrent communication and localization, using the same time-frequency resources. Specifically, we propose an IRS-enabled multi-user integrated sensing and communication (ISAC) framework, where a distributed semi-passive IRS assists the uplink data transmission from multiple users to the base station (BS) and conducts multi-user localization, simultaneously. We first design an ISAC transmission protocol, where the whole transmission period consists of two periods, i.e., the ISAC period for simultaneous uplink communication and multi-user localization, and the pure communication (PC) period for only uplink data transmission. For the ISAC period, we propose a multi-user location sensing algorithm, which utilizes the uplink communication signals unknown to the IRS, thus removing the requirement of dedicated positioning reference signals in conventional location sensing methods. Based on the sensed users' locations, we propose two novel beamforming algorithms for the ISAC period and PC period, respectively, which can work with discrete phase shifts and require no channel state information (CSI) acquisition. Numerical results show that the proposed multi-user location sensing algorithm can achieve up to millimeter-level positioning accuracy, indicating the advantage of the IRS-enabled ISAC framework. Moreover, the proposed beamforming algorithms with sensed location information and discrete phase shifts can achieve comparable performance to the benchmark considering perfect CSI acquisition and continuous phase shifts, demonstrating how the location information can ensure the communication performance.

关键词： Sensors Location awareness Array signal processing Uplink signal processing algorithms Spectral efficiency Protocols Integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) multi-user location sensing discrete phase shifts joint active and passive beamforming

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Online Power System Event Detection via Bidirectional Generative Adversarial Networks

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IEEE TRANSACTIONS ON POWER SYSTEMS 2022年第6期37卷 4807-4818页

作者： Cheng, Yuanbin Yu, Nanpeng Foggo, Brandon Yamashita, Koji Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92521 USA

Accurate and speedy detection of power system events is critical to enhancing the reliability and resiliency of power systems. Although supervised deep learning algorithms show great promise in identifying power system events, they require a large volume of high-quality event labels for training. This paper develops a bidirectional anomaly generative adversarial network (GAN)-based algorithm to detect power system events using streaming PMU data, which does not rely on a huge amount of event labels. By introducing conditional entropy constraint in the objective function of GAN and graph signal processing-based PMU sorting technique, our proposed algorithm significantly outperforms state-of-the-art event detection algorithms in terms of accuracy. To facilitate the adoption of the proposed algorithm, a prototype online platform is also developed using Apache Hadoop, Kafka, and Spark to enable real-time event detection. The accuracy and computational efficiency of the proposed algorithm are validated using a large-scale real-world PMU dataset from the Eastern Interconnection of the United States.

关键词： Phasor measurement units Event detection Power systems Training signal processing algorithms Generative adversarial networks Numerical models Phasor measurement unit event detection generative adversarial networks

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An Arctan-Type Varying-Parameter ZNN for Solving Time-Varying Complex Sylvester Equations in Finite Time

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2022年第6期18卷 3651-3660页

作者： Xiao, Lin Tao, Juan Li, Weibing Hunan Normal Univ Hunan Prov Key Lab Intelligent Comp & Language In Changsha 410081 Peoples R China Jishou Univ Coll Informat Sci & Engn Jishou 416000 Peoples R China

Zeroing neural network (ZNN) is an effective neural solution to time-varying problems, including time-varying complex Sylvester equations. Generally, a ZNN model involves a convergence design parameter (CDP) that influences its convergence rate. In traditional fixed-parameter ZNNs (FP-ZNNs), the CDPs are set to be constant, which is not realistic since the CDPs are actually time-varying in practical hardware environments. By considering this fact, varying-parameter ZNNs (VP-ZNNs) with time-varying CDPs have been researched in the literature. Although these VP-ZNNs have been demonstrated to deliver superior convergence as compared with FP-ZNNs, they have one drawback, that is, their CDPs usually keep increasing with time, meaning that the CDPs tend to be infinity large with time progresses. Evidently, infinity large CDPs are unacceptable in practice. Moreover, computing resources will be wasted by growing the CDPs with time after the VP-ZNNs become convergent. To tackle the above issues, this article, for the first time, proposes an arctan-type VP-ZNN (ATVP-ZNN) with finite-time convergence for solving time-varying complex Sylvester equations. The ATVP-ZNN is able to adjust its CDPs that finally converge to be constant when the ATVP-ZNN becomes convergent in finite time. In theory, the finite-time convergence of the ATVP-ZNN and the upper bound of the CDPs are mathematically analyzed. Numerical studies are comparatively performed with the superior convergence of the ATVP-ZNN substantiated.

关键词： Mathematical model Convergence Informatics Matrix converters Robot kinematics Parallel processing signal processing algorithms Arctan-type varying parameter complex zeroing neural network finite-time convergence time-varying complex Sylvester equation

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Kernel Regression Over Graphs Using Random Fourier Features

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 936-949页

作者： Meireles Elias, Vitor Rosa Gogineni, Vinay Chakravarthi Martins, Wallace A. Werner, Stefan Norwegian Univ Sci & Technol N-7491 Trondheim Norway Univ Fed Rio de Janeiro BR-21941901 Rio De Janeiro RJ Brazil Univ Luxembourg L-4365 Luxembourg Luxembourg

This paper proposes efficient batch-based and online strategies for kernel regression over graphs (KRG). The proposed algorithms do not require the input signal to be a graph signal, whereas the target signal is defined over the graph. We first use random Fourier features (RFF) to tackle the complexity issues associated with kernel methods employed in the conventional KRG. For batch-based approaches, we also propose an implementation that reduces complexity by avoiding the inversion of large matrices. Then, we derive two distinct online strategies using RFF, namely, the mini-batch gradient KRG (MGKRG) and the recursive least squares KRG (RLSKRG). The stochastic-gradient KRG (SGKRG) is introduced as a particular case of the MGKRG. The MGKRG and the SGKRG are low-complexity algorithms that employ stochastic gradient approximations in the regression-parameter update. The RLSKRG is a recursive implementation of the RFF-based batch KRG. A detailed stability analysis is provided for the proposed online algorithms, including convergence conditions in both mean and mean-squared senses. A discussion on complexity is also provided. Numerical simulations include a synthesized-data experiment and real-data experiments on temperature prediction, brain activity estimation, and image reconstruction. Results show that the RFF-based batch implementation offers competitive performance with a reduced computational burden when compared to the conventional KRG. The MGKRG offers a convenient trade-off between performance and complexity by varying the number of mini-batch samples. The RLSKRG has a faster convergence than the MGKRG and matches the performance of the batch implementation.

关键词： Kernel Training Complexity theory signal processing algorithms Prediction algorithms Stochastic processes Convergence Kernel regression on graphs random Fourier features recursive least squares stochastic gradient

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Continuous PPG-Based Blood Pressure Monitoring Using Multi-Linear Regression

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第5期26卷 2096-2105页

作者： Haddad, Serj Boukhayma, Assim Caizzone, Antonino Senbiosys SA CH-2000 Neuchatel Switzerland

In this work, we present a photoplethy smography-based blood pressure monitoring algorithm (PPG-BPM) that solely requires a photoplethysmography (PPG) signal. The technology is based on pulse wave analysis (PWA) of PPG signals retrieved from different body locations to continuously estimate the systolic blood pressure (SBP) and the diastolic blood pressure (DBP). The proposed algorithm extracts morphological features from the PPG signal and maps them to SBP and DBP values using a multiple linear regression (MLR) model. The performance of the algorithm is evaluated on the publicly available Multiparameter Intelligent Monitoring in Intensive Care (MIMIC I) database. We utilize 28 data-sets (records) that contain both PPG and brachial arterial blood pressure (ABP) signals. The collected PPG and ABP signals are synchronized and divided into intervals of 30 seconds, called epochs. In total, we utilize 47153 clean 30-second epochs for the performance analysis. Out of the 28 data-sets, we use only 2 data-sets with a total of 2677 clean 30-second epochs to build the MLR model of the algorithm. For the SBP, a mean absolute error (MAE) of 6.10 mmHg between the arterial line and the PPG-based values are achieved, with a Pearson correlation coefficient r = 0.90, p = .001. For the DBP, and an MAE of 4.65 mmHg between the arterial line and the PPG-based values are achieved, with a Pearson correlation coefficient r = 0.85, p < .001. We also use a binary classifier for the BP values with the positives indicating SBP = 130 mmHg and/or DBP = 80 mmHg and the negatives indicating otherwise. The classifier results generated by the PPG-based SBP and DBP estimates achieve a sensitivity and a specificity of 79.11% and 92.37%, respectively.

关键词： Blood pressure Biomedical monitoring Feature extraction Monitoring signal processing algorithms MIMICs Hypertension Hypertension cuff-less blood pressure monitoring photoplethysmography pulse wave analysis multiple linear regression

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Robust Channel Estimation for RIS-Aided Millimeter-Wave System With RIS Blockage

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第5期71卷 5621-5626页

作者： Ma, Siqi Shen, Wenqian Gao, Xinyu An, Jianping Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Huawei Technol Co Ltd Beijing 100085 Peoples R China

Recently, the reconfigurable intelligent surface (RIS) aided communication system has emerged as a promising candidate for future wireless communications. The existing channel estimation methods for RIS-aided millimeter-wave systems assume that the RIS is ideal without blockage. However, in practice, RIS may be blocked by the rain, snow, or dust, which will cause absorption and scattering of the incident/reflected signals and change the channel characteristics. In this paper, we formulate the system model of RIS-aided multi-user communications considering the blocked RIS. Then, we propose a variational Bayesian based channel estimation algorithm that is robust to RIS blockage, where we can simultaneously estimate the channel information and the RIS blockage. Simulation results demonstrate the superior performance of the proposed algorithm to existing ones.

关键词： Channel estimation Bayes methods Estimation signal processing algorithms Matching pursuit algorithms Snow Millimeter wave technology Reconfigurable intelligent surface (RIS) channel estimation blockage

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DCT2net: An Interpretable Shallow CNN for Image Denoising

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IEEE TRANSACTIONS ON IMAGE processing 2022年 31卷 4292-4305页

作者： Herbreteau, Sebastien Kervrann, Charles Inria Rennes Bretagne Atlantique F-35042 Rennes France PSL Res Univ UMR144 Insitut Curie CNRS F-75005 Paris France

This work tackles the issue of noise removal from images, focusing on the well-known DCT image denoising algorithm. The latter, stemming from signal processing, has been well studied over the years. Though very simple, it is still used in crucial parts of state-of-the-art "traditional" denoising algorithms such as BM3D. For a few years however, deep convolutional neural networks (CNN), especially DnCNN, have outperformed their traditional counterparts, making signal processing methods less attractive. In this paper, we demonstrate that a DCT denoiser can be seen as a shallow CNN and thereby its original linear transform can be tuned through gradient descent in a supervised manner, improving considerably its performance. This gives birth to a fully interpretable CNN called DCT2net. To deal with remaining artifacts induced by DCT2net, an original hybrid solution between DCT and DCT2net is proposed combining the best that these two methods can offer;DCT2net is selected to process non-stationary image patches while DCT is optimal for piecewise smooth patches. Experiments on artificially noisy images demonstrate that two-layer DCT2net provides comparable results to BM3D and is as fast as DnCNN algorithm.

关键词： Discrete cosine transforms Noise reduction Convolutional neural networks Transforms Kernel Convolution signal processing algorithms Convolutional neural network image denoising Canny edge detector artifact removal

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A Novel Module-Sign Low-Power Implementation for the DLMS Adaptive Filter With Low Steady-State Error

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2022年第1期69卷 297-308页

作者： Meo, Gennaro Di De Caro, Davide Saggese, Gerardo Napoli, Ettore Petra, Nicola Strollo, Antonio Giuseppe Maria Univ Naples Federico II Dept Elect Engn & Informat Technol I-80125 Naples Italy

In this paper, a novel implementation is proposed for the Delayed LMS (DLMS) filter, able to reduce the power dissipation while preserving regime performances. The approach relies on the observation that the error signal is small in magnitude and oscillates around zero when the circuit is close to the convergence point. Therefore, the most significant bits of the error signal continuously toggle from positive to negative values causing high switching activity in the multipliers of the feedback section. This paper proposes to employ a sign-modulus representation of the error signal, to substantially reduce the switching activity of the feedback path of the filter. Additional approximation techniques are also devised to further reduce power dissipation. Comparisons with the state-of-the-art show that the proposed filter is the only one able to approach the MSE of the exact implementation with a remarkable reduction of power dissipation. A test-chip in TSMC 28nm CMOS technology has been realized to experimentally verify the validity of our technique. The experimental results show the possibility of saving up to 45.4% of power consumption with respect to the exact implementation of the filter.

关键词： Finite impulse response filters Approximation algorithms signal processing algorithms Registers Switches Convergence Table lookup Adaptive filter delay-least mean square algorithm error signal absolute value low-power implementation

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The Methodology for the Reliability Evaluation of the signal processing Methods Used for the Dispersion Estimation of Lamb Waves

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷 1页

作者： Draudviliene, Lina Meskuotiene, Asta Kaunas Univ Technol Ultrasound Res Inst LT-51423 Kaunas Lithuania Kaunas Univ Technol Metrol Inst LT-51368 Kaunas Lithuania

The applications of ultrasonic Lamb waves are becoming more and more frequent and diverse in industrial fields. These waves are used in non-destructive testing (NDT), structural health monitoring (SHM), for the assessment of the quality of materials, etc. However, due to the unusual properties of these waves, new or adapted signal processing methods are necessary to use for the analysis of such wave signals. Based on the requirements of ISO 17025, the work presents the methodology for verification of the signal processing methods used to reconstruct the dispersion curves of the phase and group velocities of Lamb waves. The key sequence of the mathematical and experimental stages, which must be passed to evaluate the reliability of the methods are developed. The present methodology includes the algorithm to optimally quantify the uncertainty, as to avoid additional testing, saving cost and time. The main characteristics and sources of the uncertainties, that mainly affect the accuracy of the obtained results are identified, analyzed, and presented. New standards for certification testing, inspection, and monitoring can be developed more easily and quickly based on the provided methodology in the future.

关键词： Dispersion signal processing Reliability Uncertainty Testing Acoustics signal processing algorithms Dispersion curve method verification reliability signal processing method ultrasonic Lamb waves uncertainty

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On Asymptotic Linear Convergence of Projected Gradient Descent for Constrained Least Squares

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 4061-4076页

作者： Vu, Trung Raich, Raviv Oregon State Univ Sch Elect Engn & Comp Sci Corvallis OR 97331 USA

Many recent problems in signal processing and machine learning such as compressed sensing, image restoration, matrix/tensor recovery, and non-negative matrix factorization can be cast as constrained optimization. Projected gradient descent is a simple yet efficient method for solving such constrained optimization problems. Local convergence analysis furthers our understanding of its asymptotic behavior near the solution, offering sharper bounds on the convergence rate compared to global convergence analysis. However, local guarantees often appear scattered in problem-specific areas of machine learning and signal processing. This manuscript presents a unified framework for the local convergence analysis of projected gradient descent in the context of constrained least squares. The proposed analysis offers insights into pivotal local convergence properties such as the conditions for linear convergence, the region of convergence, the exact asymptotic rate of convergence, and the bound on the number of iterations needed to reach a certain level of accuracy. To demonstrate the applicability of the proposed approach, we present a recipe for the convergence analysis of projected gradient descent and demonstrate it via a beginning-to-end application of the recipe on four fundamental problems, namely, linear equality-constrained least squares, sparse recovery, least squares with the unit norm constraint, and matrix completion.

关键词： Convergence Optimization Manifolds signal processing algorithms Machine learning Symmetric matrices Sparse matrices Asymptotic convergence rate constrained least squares local linear convergence projected gradient descent

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