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De-Noising of Sparse signals Using Mixture Model Shrinkage Function

IEEE ACCESS 2023年 11卷 7551-7563页

作者： Ullah, Hayat Amir, Muhammad Iqbal, Muhammad Malik, Suheel Abdullah Jadoon, Muhammad Mohsin Khan NYU Tandon Sch Engn Dept Elect & Comp Engn New York NY 11201 USA Int Islamic Univ Islamabad Dept Elect Engn Islamabad 44000 Pakistan Tampere Univ Dept Automat & Mech Engn Tampere 33720 Finland Pak Austria Fachhsch Inst Fac Elect Com IT & Design Haripur 22653 Pakistan

In this work a new thresholding function referred to as 'mixture model shrinkage' (MMS) based on the minimization of a convex cost function is proposed. Normally, thresholding functions underestimate larger signal amplitudes during the de-noising process. The proposed model is a more flexible shrinkage function as it solves the underestimation problem to a greater extent and thus efficiently de-noises the signal without affecting signal amplitudes. The Expectation minimization (EM) algorithm is used to find the model parameters along with the majorization-minimization (MM) algorithm that minimize the monotonic cost function. The proposed model is then applied for de-noising group sparse signals and Shepp Logan phantom images. Our experimental study shows that MMS outclasses current thresholding functions and overlapping group shrinkage algorithm without results suffering from underestimation. Furthermore, the proposed model has the smallest Root Mean Square Error (RMSE) for de-noising group sparse signals.

关键词： Noise reduction Transforms Thresholding (Imaging) Mixture models signal processing algorithms Noise measurement Wavelet coefficients Convex optimization EM algorithm maximum a posteriori estimator (MAP) MM algorithm PSNR RMSE sparse signal processing

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VISION-ASSISTED MILLIMETER-WAVE BEAM MANAGEMENT FOR NEXT-GENERATION WIRELESS SYSTEMS

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IEEE VEHICULAR TECHNOLOGY MAGAZINE 2023年第3期18卷 58-68页

作者： Zheng, Kan Yang, Haojun Ying, Ziqiang Wang, Pengshuo Hanzo, Lajos Ningbo Univ Ningbo 315211 Peoples R China Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Informat & Commun Engn Beijing 100876 Peoples R China Univ Southampton Sch Elect & Comp Sci Southampton England

Beamforming techniques have been widely used in the millimeter-wave (mm-wave) bands to mitigate the path loss of mm-wave radio links as narrow straight beams by directionally concentrating the signal energy. However, traditional mm-wave beam management algorithms usually require excessive channel state information (CSI) overhead, leading to extremely high computational and communication costs. This hinders the widespread deployment of mm-wave communications. By contrast, the revolutionary vision-assisted beam management system concept employed at base stations (BSs) can select the optimal beam for the target user equipment (UE), based on location information determined by machine learning (ML) algorithms applied to visual data, without requiring channel information. In this article, we present a comprehensive framework for a vision-assisted mm-wave beam management system, its typical deployment scenarios, and the specifics of the framework. Then, some of the challenges faced by this system and their efficient solutions are discussed from the perspective of ML. Next, a new simulation platform is conceived to provide both visual and wireless data for model validation and performance evaluation. Our simulation results indicate that vision-assisted beam management is indeed attractive for next-generation wireless systems.

关键词： Wireless communication Detectors Computational modeling Cameras Predictive models Visualization signal processing algorithms

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ANR: A New Metric to Quantify the Appliance to Noise Ratio in Load Disaggregation Datasets

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1-1页

作者： Gois, Joao Pereira, Lucas Inst Super Tecn ITI LARSyS Lisbon & Funchal P-1049001 Lisbon Portugal

Nonintrusive load monitoring (NILM), or load disaggregation, aims to decompose aggregate power consumption into appliance components. Factors such as noise power affect algorithm performance, reducing accuracy and increasing complexity. While existing literature often relies on standard machine learning metrics to report noise power proportion in aggregate consumption, these are overall measures that do not specify the ratio between appliance consumption and noise power. This article proposes a noise metric that assesses the proportion of dataset noise relative to an appliance's consumption in NILM. The proposed metric's sensitivity and applicability are assessed for different data scenarios using a real-world dataset. Additionally, the article explores a potential association between the proposed metric and disaggregation performance is also inspected. Furthermore, the proposed metric is compared to existing noise metrics to highlight its unique contributions. The results demonstrate that the proposed metric effectively quantifies noise proportion with respect to specific appliances across various data scenarios. It exhibits robustness to suitable granularity variations and complements other noise metrics in interpreting NILM experiments.

关键词： Measurement signal to noise ratio Aggregates signal processing algorithms Sensitivity Power demand Washing machines Appliance consumption comparability disaggregation complexity load disaggregation signal noise

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Real-Time Polarization Demultiplexing by Multi-Thread Constant Modulus Algorithm for Fully Softwarized Access Networks

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2023年第5期41卷 1346-1356页

作者： Suzuki, Takahiro Kim, Sang-Yuep Kani, Jun-ichi Yoshida, Tomoaki NTT Corp NTT Access Network Serv Syst Labs Yokosuka Kanagawa 2390847 Japan

Optical access networks are replacing dedicated hardware-based architectures with architectures centered on general-purpose servers based on network function virtualization (NFV) and the software-defined network (SDN) in order to efficiently support various new services. To maximize the advantage of this new approach, studies are now targeting the fully softwarized access network which requires softwarization of digital signal processing (DSP). However, polarization demultiplexing includes sequential processing, and it is difficult to softwarize high-speed filter updates in a constant modulus algorithm (CMA) that supports real-world state of polarization (SOP) change. This paper proposes a novel multi-thread CMA that can track fast SOP changes by using multiple threads to implement filter convolution. It tracks faster SOP changes with the same filter update cycle as the conventional method by calculating multiple filters on multiple threads and averaging them. Its graphics processing unit (GPU) implementation adopts parallel reduction for averaging the filters and parallelizes the update of filters for tributaries X and Y in order to take advantage of many GPU threads. We evaluate the real-time bit error rate (BER) performance achieved by implementing the DSP functions with the proposal in a 5-Gb/s dual-polarization-quadrature phase-shift keying (DP-QPSK) system. The demonstration results show that the proposed algorithm supports 2,000 rd/s real-time performance for optical systems and offers high feasibility for application to the fully softwarized access network.

关键词： Instruction sets Equalizers Demultiplexing Software Graphics processing units signal processing algorithms Optical network units NFV PON SDN Softwarization and polarization demultiplexing

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Tensor-Based Sparse Bayesian Learning With Intra-Dimension Correlation

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 31-46页

作者： Song, Yuhui Gong, Zijun Chen, Yuanzhu Li, Cheng Mem Univ Dept Comp Sci St John NL A1B 3X5 Canada HKUST IoT Thrust Guangzhou 999077 Peoples R China HKUST Dept ECE Hongkong Peoples R China Queens Univ Sch Comp Kingston ON K7L 2N8 Canada Mem Univ Dept Elect & Comp Engn St John NL A1B 3X5 Canada

Unlike the flat-view matrix, tensor provides an elegant representation to preserve the structure of data in the multidimensional nature. The wide use of tensor-based approaches highlights the limitation of matrix-based methods and has generated increasing needs in multidimensional data analytics. Driven by such demands, tensor decomposition with automatic rank determination has recently emerged as a promising tool for image processing, wireless communications and neural network compression. However, unlike the success in matrix-based schemes of exploring the correlation for performance improvement, existing tensor-based methods fail to consider the correlation structure in the tensor. In this paper, we propose to exploit the tensor intra-dimension correlation within the framework of sparse Bayesian learning. We provide the sparsity-inducing probabilistic models under both canonical polyadic decomposition and Tucker decomposition to capture the correlation structure effectively. We derive an efficient model inference method with a fast convergence rate based on the expectation-maximization algorithm. Furthermore, a complexity reduction method is proposed to speed up the computation by exploiting several properties of the Kronecker product. We further extend the proposed framework for the incomplete tensor corrupted by sparse outliers to achieve effective tensor completion and outlier mitigation. Simulation results demonstrate the superior performance of the proposed framework over those ignoring the intra-dimension correlation in terms of accuracy and sparsity.

关键词： Tensors Correlation Bayes methods Matrix decomposition Sparse matrices Computational modeling signal processing algorithms Tensor canonical polyadic decomposition Tucker decomposition intra-dimension correlation sparse Bayesian learning expectation-maximization

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Joint Generalized Inner Product Method for Main Lobe Jamming Suppression in Distributed Array Radar

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第5期59卷 6940-6953页

作者： Pu, Weiming Liang, Zhennan Wu, Jianxin Liu, Quanhua Beijing Inst Technol Minist Educ Key Lab Elect & Informat Technol Satellite Nav Beijing 100081 Peoples R China Sun Yat Sen Univ Sch Elect & Commun Engn Guangzhou 510275 Peoples R China Beijing Inst Technol Chongqing Innovat Ctr Chongqing 401135 Peoples R China

Modern radar detection faces an increasingly complex electromagnetic environment, and strong clutter and strong jamming have severely restricted radar performance. This article proposes a robust jamming suppression method that is based on the joint generalized inner product (JGIP) method in a distributed array radar, which utilizes the characteristics of the extremely high angular resolution of the distributed array radar to effectively suppress main lobe jamming. Thus, the advantages of flexible aperture synthesis of distributed array radar are investigated to design a JGIP to detect targets in a cluttered environment. This method is not sensitive to the various nonideal factors that exist in distributed array radar engineering practice and is highly robust. Simulation and actual measured data verify the correctness and effectiveness of the proposed method.

关键词： Jamming Radar Radar antennas Clutter Radar clutter Spaceborne radar signal processing algorithms Distributed array radar joint generalized inner product (JGIP) main lobe jamming measured data

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Iterative Difference Hard-Thresholding Algorithm for Sparse signal Recovery

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 1093-1102页

作者： Cui, Angang He, Haizhen Xie, Zhiqi Yan, Weijun Yang, Hong Yulin Univ Sch Math & Stat Yulin 719000 Peoples R China Yulin Univ Sch Int Educ Yulin 719000 Peoples R China

In this paper, a nonconvex surrogate function, namely, Laplace norm, is studied to recover the sparse signals. Firstly, we discuss the equivalence of the optimal solutions of $l_{0}$-norm minimization problem, Laplace norm minimization problem and regularization Laplace norm minimization problem. It is proved that the $l_{0}$-norm minimization problem can be solved by solving the regularization Laplace norm minimization problem if the certain conditions are satisfied. Secondly, an iterative difference hard-thresholding algorithm and its adaptive version algorithm are proposed to solve the regularization Laplace norm minimization problem. Finally, we provide some numerical experiments to test the performance of the adaptive iterative difference hard-thresholding algorithm, and the numerical results show that the adaptive iterative difference hard-thresholding algorithm performs better than some state-of-art methods in recovering the sparse signals.

关键词： Minimization signal processing algorithms Iterative algorithms Thresholding (Imaging) Indexes Eigenvalues and eigenfunctions Linear matrix inequalities Laplace norm equivalence iterative difference hard-thresholding algorithm adaptive iterative difference hard-thresholding algorithm

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Gradient-Push Algorithm for Distributed Optimization With Event-Triggered Communications

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IEEE ACCESS 2023年 11卷 517-534页

作者： Kim, Jimyeong Choi, Woocheol Sungkyunkwan Univ Dept Math Suwon 16419 South Korea

Decentralized optimization problems consist of multiple agents connected by a network. The agents have each local cost function, and the goal is to minimize the sum of the functions cooperatively. It requires the agents to communicate with each other, and reducing the cost for communication is desired for a communication-limited environment. Recently, the decentralized gradient descent algorithms involving event-triggered communication have been proposed when the network of the agents is an undirected graph. On the other hand, the network of agents is often directed graph for realistic scenarios whose communication resources are limited. In this work, we first propose a gradient-push algorithm involving event-triggered communication on a directed network. Each agent sends its current states to its neighbors only when the differences between the latest sent states and the current states are larger than thresholds. The convergence of the algorithm is established under suitable decays and summability conditions on a stepsize and triggering thresholds. Numerical experiments are presented to support the effectiveness and the convergence results of the algorithm. More precisely, the numerical results reveals that the proposed algorithm may reduce the communication cost significantly compared to the gradient-push algorithm not involving the event-triggered communication.

关键词： signal processing algorithms Convergence Directed graphs Cost function Distributed databases Costs Event detection Gradient methods Decentralized optimization directed graph event-triggered communication gradient push algorithm

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Direction Finding in Partly Calibrated Arrays Exploiting the Whole Array Aperture

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第5期59卷 5978-5992页

作者： Zhang, Guangbin Huang, Tianyao Liu, Yimin Wang, Xiqin Eldar, Yonina C. Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Weizmann Inst Sci Dept Comp Sci & Appl Math IL-7610001 Rehovot Israel

We consider the problem of direction finding using partly calibrated arrays, a distributed subarray with position errors between subarrays. The key challenge is to enhance angular resolution in the presence of position errors. To achieve this goal, existing algorithms, such as subspace separation and sparse recovery, have to rely on multiple snapshots, which increases the burden of data transmission and the processing delay. Therefore, we aim to enhance angular resolution using only a single snapshot. To this end, we exploit the orthogonality of the signals of partly calibrated arrays. Particularly, we transform the signal model into a multiple-measurement model, and show that there is approximate orthogonality between the source signals in this model. We then use blind source separation to exploit the orthogonality. Simulation and experiment results both verify that our proposed algorithm achieves high angular resolution as distributed arrays without position errors, inversely proportional to the whole array aperture.

关键词： Apertures signal resolution Array signal processing Sensors Transforms signal processing algorithms Sensor arrays Blind source separation high angular resolution orthogonality partly calibrated array

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Gerchberg-Saxton Based FIR Filter for Electronic Dispersion Compensation in IM/DD Transmission Part II: Experimental Demonstration and Analysis

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2023年第5期41卷 1428-1435页

作者： Wu, Xiong Karar, Abdullah S. Zhong, Kangping Gurkan, Zeynep Nilhan Lau, Alan Pak Tao Lu, Chao Hong Kong Polytech Univ Photon Res Inst Dept Elect & Informat Engn Hong Kong Peoples R China Hong Kong Polytech Univ Shenzhen Res Inst Shenzhen 518057 Peoples R China Amer Univ Middle East Coll Engn & Technol Egaila 15453 Kuwait Poet Technol Inc Shenzhen 518057 Peoples R China

In this article, the first experimental demonstration of a non-iterative electronic dispersion compensation (EDC) solution implemented at the transmitter using a finite impulse response (FIR) filter optimized with the Gerchberg-Saxton (GS) algorithm, is presented, for intensity-modulation and direct-detection (IM/DD) systems. The theoretical framework and preliminary simulations have been presented in Part I of this work. Here, the performance is compared between the GS FIR filter and the iterative GS algorithm in 56-Gb/s on-off keying (OOK) transmissions over 80-km single mode fiber (SMF) with a post feed-forward equalizer (FFE) for combating residual inter-symbol interference. Furthermore, the influence of the pulse shape (raised cosine or rectangular) and modulation format (return-to-zero (RZ) or non-return-to-zero (NRZ)) on the measured bit error ratio (BER) is investigated, while changing the number of FIR taps, post-FFE taps, and post-FFE samples-per-symbol. It is shown that within the range of the target digital extinction ratios (DERs) for which the original iterative GS algorithm offers benefit, both analytical and numerical methods for calculating the optimum FIR taps, outlined in Part I, produce similar BER performance as predicted. Hence, the former method is extended, here, through a non-recursive frequency response formula, which offers insight into the action of the GS filter with different pulse shaping and enables the derivation of the explicit GS impulse response. It is also shown that rectangular RZ exploits the full benefit of GS filtering through a uniform spectrum, achieving BER < 3.8x10(-3), with a 641-tap T/2-spaced pre-EDC FIR filter and a 3-tap adaptive T-spaced post-FFE.

关键词： Finite impulse response filters Modulation Dispersion Filtering algorithms signal processing algorithms Frequency-domain analysis Transfer functions Digital to analog converter digital signal processing electronic dispersion compensation finite impulse response filter Gerchberg-Saxton algorithm intensity modulation and direct detection Mach-Zehnder modulator

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