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Metalearning-Based Alternating Minimization Algorithm for Nonconvex Optimization

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第9期34卷 5366-5380页

作者： Xia, Jing-Yuan Li, Shengxi Huang, Jun-Jie Yang, Zhixiong Jaimoukha, Imad M. Gunduz, Deniz Natl Univ Def Technol Coll Elect Engn Coll Comp Changsha 410073 Peoples R China Beihang Univ Coll Elect Engn Beijing 100191 Peoples R China Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England Univ Modena & Reggio Emilia UNIMORE Dept Engn Enzo Ferrari I-41121 Modena Italy

In this article, we propose a novel solution for nonconvex problems of multiple variables, especially for those typically solved by an alternating minimization (AM) strategy that splits the original optimization problem into a set of subproblems corresponding to each variable and then iteratively optimizes each subproblem using a fixed updating rule. However, due to the intrinsic nonconvexity of the original optimization problem, the optimization can be trapped into a spurious local minimum even when each subproblem can be optimally solved at each iteration. Meanwhile, learning-based approaches, such as deep unfolding algorithms, have gained popularity for nonconvex optimization;however, they are highly limited by the availability of labeled data and insufficient explainability. To tackle these issues, we propose a meta-learning based alternating minimization (MLAM) method that aims to minimize a part of the global losses over iterations instead of carrying minimization on each subproblem, and it tends to learn an adaptive strategy to replace the handcrafted counterpart resulting in advance on superior performance. The proposed MLAM maintains the original algorithmic principle, providing certain interpretability. We evaluate the proposed method on two representative problems, namely, bilinear inverse problem: matrix completion and nonlinear problem: Gaussian mixture models. The experimental results validate the proposed approach outperforms AM-based methods.

关键词： Optimization Minimization Task analysis Deep learning Neural networks signal processing algorithms Iterative algorithms Alternating minimization (AM) deep unfolding gaussian mixture model (GMM) matrix completion meta-learning (ML)

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Density-Based Affinity Propagation Tensor Clustering for Intelligent Fault Diagnosis of Train Bogie Bearing

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第6期24卷 6053-6064页

作者： Wei, Zexian He, Deqiang Jin, Zhenzhen Liu, Bin Shan, Sheng Chen, Yanjun Miao, Jian Guangxi Univ Sch Mech Engn Guangxi Key Lab Mfg Syst & Adv Mfg Technol Nanning 530004 Peoples R China Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China Zhuzhou CRRC Times Elect Co Ltd Zhuzhou 412001 Peoples R China

Health monitor of bogie-bearing on the train can ensure constant operation of the rail transit system. Since the metro or other rail transit have high safety requirements, it is hard to acquire numerous fault samples. Besides, diagnosing train bogie-bearings under variable working conditions is challenging due to wheel-rail coupling, speed variation, and load fluctuation. An intelligent approach for bogie-bearing fault diagnosis is proposed to deal with the above problems. A third-order tensor model is established to be suitable for variable working conditions. Furthermore, a density-based affinity propagation tensor (DAP-Tensor) clustering algorithm is presented to identify different failures with unlabeled. Train bogie and public data sets were employed to simulate three probable conditions of train operation: high-frequency impact, speed variation, and load change. Compared with existing clustering methods in three cases, the proposed DAP-Tensor performs better in identifying bearing faults under variable working conditions. Moreover, The DAP-tensor has a comparable recognition rate to some deep learning methods, which unsupervised characteristics show it has potential for applications on rail transit trains.

关键词： Tensors Fault diagnosis Clustering algorithms signal processing algorithms Monitoring Rails Time complexity Bogie-bearing tensor clustering variable working conditions intelligent fault diagnosis

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Scalable Distributed Data-Driven State Estimation Algorithm via Gaussian Processes With Guaranteed Stability

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第6期59卷 9191-9204页

作者： Yu, Xingkai Sun, Xianzheng Li, Jianxun North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China Tsinghua Univ Dept Precis Instrument Beijing 100084 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

In this article, we focus on the scalable distributed data-driven state estimation problem using Gaussian processes (GPs). The framework includes two parts: 1) the data-driven training approach and 2) the state-estimation architecture. First, the objective is to obtain the transition and measurement functions of the considered state-space model by a data-driven training strategy via distributed GPs. In particular, to improve the training efficiency, we employ an online conditioning algorithm, which reduces the computational burden significantly. Then, all nodes exchange their own trained GPs with their respective neighbors. Furthermore, to achieve consensus on local trained GPs, we propose a Wasserstein weighted average consensus algorithm, which differs from the current Kullback-Leibler average consensus on probability densities. Second, based on the training results, we propose a distributed state estimation algorithm to perform fresh state estimation. After obtaining the state estimation results (mean and covariance) and exchanging them with their neighboring nodes, we then execute the Wasserstein weighted average to achieve consensus on state estimations. Also, we analyze the stability and robustness of the proposed distributed state estimation by using GP. Finally, numerical and real-world examples are provided to validate the effectiveness of the proposed training method and data-driven state estimation algorithms.

关键词： State estimation Training Kernel Mathematical models signal processing algorithms Interpolation Gaussian processes Data-driven distributed state estimation Gaussian processes (GPs) stability analysis Wasserstein average consensus

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Fast MSE-Based Sampling of Bandlimited Graph signals via Low-Pass Impulse Responses

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 4207-4223页

作者： Wang, Fen Cheung, Gene Ye, Minxiang Li, Taihao Feng, Yi-Tian Zhejiang Lab Hangzhou 311121 Peoples R China York Univ Dept EECS Toronto ON M3J 1P3 Canada

Sampling is a fundamental problem in graph signal processing that selects a node subset to collect samples, so that data in the remaining nodes can be well recovered. Existing eigen-decomposition-free (ED-free) graph sampling schemes are not designed to minimize mean square error (MMSE) of reconstructed bandlimited graph signals, often resulting in sub-par MSE performance. In this paper, we propose a lightweight ED-free algorithm to minimize an approximate MSE objective using per-node impulse responses of an ideal low-pass graph filter. Specifically, we first derive a proxy of the MMSE greedy sampling objective without matrix inverse. We then define node-dependent impulse responses of an ideal low-pass graph filter-analogous to the sinc function in traditional signal processing-which can be approximated very fast without ED. We use these response vectors to reformulate our derived sampling objective, then show that the optimal sampling set has supported low-pass impulse responses that are as orthogonal as possible-defaulting to uniform sampling for 1D regular kernels when the graph Fourier basis is DFT. For optimization, we propose a fast sampling algorithm to evaluate candidates via vector-vector multiplications by reusing previous greedy results. For faster sampling, we relax the MMSE-based greedy objective to a bounded approximation, so that candidate nodes can be easily appraised using simple scalar multiplications. Extensive experiments show that our sampling method achieved state-of-the-art sampling speed and had the best MSE performance among deterministic ED-free sampling methods in various scenarios.

关键词： Laplace equations Complexity theory signal processing algorithms Sampling methods Matrix decomposition Eigenvalues and eigenfunctions Approximation algorithms Graph sampling graph signal processing optimality design eigen-decomposition

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Reinforcement Learning Heuristic A

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第3期19卷 2307-2316页

作者： Ha, Junhyoung An, Byungchul Kim, Soonkyum Korea Inst Sci & Technol Ctr Healthcare Robot Dept Artificial Intelligence Robot Seoul 02792 South Korea Karlsruhe Inst Technol Inst Anthropomat & Robot D-76131 Karlsruhe Germany

In a graph search algorithm, a given environment is represented as a graph comprising a set of feasible system configurations and their neighboring connections. A path is generated by connecting the initial and goal configurations through graph exploration, whereby the path is often desired to be optimal or suboptimal. The computational performance of the optimal path generation depends on the avoidance of unnecessary explorations. Accordingly, heuristic functions have been widely adopted to guide the exploration efficiently by providing estimated costs to the goal configurations. The exploration is efficient when the heuristic functions estimate the optimal cost closely, which remains challenging because it requires a comprehensive understanding of the environment. However, this challenge presents the scope to improve the computational efficiency over the existing methods. Herein, we propose reinforcement learning heuristic A* (RLHA*), which adopts an artificial neural network as a learning heuristic function to closely estimate the optimal cost, while achieving a bounded suboptimal path. Instead of being trained by precomputed paths, the learning heuristic function keeps improving by using self-generated paths. Numerous simulations were performed to demonstrate the consistent and robust performance of RLHA* by comparing it with the existing methods.

关键词： Costs Heuristic algorithms Path planning signal processing algorithms Robots Reinforcement learning Planning Graph search path planning reinforcement learning

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A Clutter Suppression Algorithm via Enhanced Sparse Bayesian Learning for Airborne Radar

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IEEE SENSORS JOURNAL 2023年第10期23卷 10900-10911页

作者： Wang, Degen Wang, Tong Cui, Weichen Zhang, Xinying Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

The traditional space-time adaptive processing (STAP) method based on sparse Bayesian learning (SBL) has the problems of low computational efficiency and slow convergence speed. In this article, a novel SBL approach based on a statistical threshold is proposed to address these issues. To discriminate between the active and inactive atoms of the dictionary, we first develop an adaptive decision test. Next, the adaptive decision test is integrated into the SBL method, which increases its accuracy and convergence rate. In addition, the detection threshold has an important property that is independent of signal and noise power. Therefore, the adaptive decision test is robust to external changes. Numerous simulations demonstrate that the proposed algorithm has excellent clutter suppression performance and fast convergence rate.

关键词： Clutter signal processing algorithms Convergence Sensors Symbols Sparse matrices Matching pursuit algorithms Space--time adaptive processing (STAP) sparse Bayesian learning (SBL) statistical threshold

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Covariance Matrix Recovery From One-Bit Data With Non-Zero Quantization Thresholds: Algorithm and Performance Analysis

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 4060-4076页

作者： Xiao, Yu-Hang Huang, Lei Ramirez, David Qian, Cheng So, Hing Cheung Shenzhen Univ State Key Lab Radio Frequency Heterogeneous Integr Shenzhen 518060 Peoples R China Univ Carlos III Madrid Dept Signal Theory & Commun Madrid 28911 Spain Gregorio Maranon Hlth Res Inst Madrid 28007 Spain IQVIA Inc Cambridge MA 02139 USA City Univ Hong Kong Dept Elect Engn Hong Kong 999077 Peoples R China

Covariance matrix recovery is a topic of great significance in the field of one-bit signal processing and has numerous practical applications. Despite its importance, the conventional arcsine law with zero threshold is incapable of recovering the diagonal elements of the covariance matrix. To address this limitation, recent studies have proposed the use of non-zero clipping thresholds. However, the relationship between the estimation error and the sampling threshold is not yet known. In this article, we undertake an analysis of the mean squared error by computing the Fisher information matrix for a given threshold. Our results reveal that the optimal threshold can vary considerably, depending on the variances and correlation coefficients. As a result, it is inappropriate to adopt a constant threshold to encompass parameters that vary widely. To mitigate this issue, we present a recovery scheme that incorporates time-varying thresholds. Our approach differs from existing methods in that it utilizes the exact values of the threshold, rather than its statistical properties, to increase the estimation accuracy. Simulation results, including those of the direction-of-arrival estimation problem, demonstrate the efficacy of the developed scheme, especially in complex scenarios where the covariance elements are widely separated.

关键词： Correlation coefficient Estimation error Quantization (signal) Direction-of-arrival estimation Simulation signal processing algorithms Numerical simulation Covariance matrix estimation mean squared error analysis non-zero threshold one-bit sampling

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Sensorless Saliency-Based Control of Dual Induction Machines Under Dynamic Load Imbalances Using Three Current Sensors

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2023年第12期70卷 12093-12101页

作者： Montero, Eduardo Rodriguez Vogelsberger, Markus Wolbank, Thomas Vienna Univ Technol A- 1040 Vienna Austria ALSTOM Transport Austria GmbH Rolling Stock Platform Components Div DRIVES A-1220 Vienna Austria

To perform vector control of single-inverter dual induction motors, drives are standardly equipped with current and position sensors. In medium-power railway applications, a shaft encoder is usually mounted on each motor, and the inverter output current is measured using two current sensors. Yet, this configuration features some disadvantages. First, torque-sharing cannot be computed because stator current of each motor is unknown. Similarly, saliency extraction of each motor is not achievable since individual current drawn by each motor is not measured. An option that achieves torque-sharing calculation is to attach two current sensors to each motor, provided that both rotor positions are known. However, industrial inverter/control platforms considered offer a maximum of three input channels for current sensors. Besides, some industry fields tend towards encoderless speed control. Therefore, this article proposes to use three current sensors in total and no shaft encoders. Thanks to a special current sensor arrangement and a voltage step excitation method, saliency-based encoderless control is achieved. In addition, a method is presented to estimate torque-sharing. Experimental measurements, taken on a dual induction motor test bench, will show the capability of the proposed methodology to identify individual loading as well as excellent encoderless control performance.

关键词： Angular velocity control harmonic analysis induction motor sensorless control signal processing algorithms

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Leveraging Deep Neural Networks for Massive MIMO Data Detection

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IEEE WIRELESS COMMUNICATIONS 2023年第1期30卷 174-180页

作者： Nguyen, Ly V. Nguyen, Nhan T. Tran, Nghi H. Juntti, Markku Swindlehurst, A. Lee Nguyen, Duy H. N. San Diego State Univ San Diego CA 92182 USA Univ Oulu Oulu Finland Univ Akron Akron OH USA Univ Calif Irvine Irvine CA USA

Massive multiple-input multiple-output (MIMO) is a key technology for emerging next-generation wireless systems. Utilizing large antenna arrays at base-stations, massive MIMO enables substantial spatial multiplexing gains by simultaneously serving a large number of users. However, the complexity in massive MIMO signal processing (e.g., data detection) increases rapidly with the number of users, making conventional hand-engineered algorithms less computationally efficient. Low-complexity massive MIMO detection algorithms, especially those inspired or aided by deep learning, have emerged as a promising solution. While there exist many MIMO detection algorithms, the aim of this magazine article is to provide insight into how to leverage deep neural networks (DNN) for massive MIMO detection. We review recent developments in DNN-based MIMO detection that incorporate the domain knowledge of established MIMO detection algorithms with the learning capability of DNNs. We then present a comparison of the key numerical performance metrics of these works. We conclude by describing future research areas and applications of DNNs in massive MIMO receivers.

关键词： Detectors Massive MIMO Symbols signal processing algorithms Detection algorithms Deep learning Antenna arrays

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Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraints

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IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2023年第1期17卷 98-111页

作者： Ribero, Monica Vikalo, Haris de Veciana, Gustavo Univ Texas Austin Dept Elect & Comp Engn Austin TX 78703 USA

Federated learning systems facilitate the training of global models across large numbers of distributed edge-devices with potentially heterogeneous data. Such systems operate in resource constrained settings with intermittent client availability and/or time-varying communication constraints. As a result, the global models trained by federated learning systems may be biased towards clients with higher availability. We propose Federated Averaging Aided by an Adaptive Sampling Technique (F3ast), an unbiased algorithm that dynamically learns an availability-dependent client selection strategy which asymptotically minimizes the impact of client-sampling variance on the global model's convergence, enhancing performance of federated learning. The proposed algorithm is tested in a variety of settings for intermittently available clients operating under communication constraints, and its efficacy demonstrated on synthetic data and realistically federated benchmarking experiments using CIFAR100 and Shakespeare datasets. We report up to 186% and 8% accuracy improvements over FedAvg, and 8% and 7% over FedAdamon CIFAR100 and Shakespeare, respectively.

关键词： Federated learning Servers Convergence signal processing algorithms Data models Optimization Adaptation models Edge learning distributed learning federated learning resource management communication efficiency

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