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Frequency Estimation in Data Streams: Learning the Optimal Hashing Scheme

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第2期35卷 1541-1553页

作者： Bertsimas, Dimitris Digalakis, Vassilis MIT Sloan Sch Management Cambridge MA 02139 USA MIT Operat Res Ctr Cambridge MA 02139 USA

We present a novel approach for the problem of frequency estimation in data streams that is based on optimization and machine learning. Contrary to state-of-the-art streaming frequency estimation algorithms, which heavily rely on random hashing to maintain the frequency distribution of the data steam using limited storage, the proposed approach exploits an observed stream prefix to near-optimally hash elements and compress the target frequency distribution. We develop an exact mixed-integer linear optimization formulation, which enables us to compute optimal or near-optimal hashing schemes for elements seen in the observed stream prefix;then, we use machine learning to hash unseen elements. Further, we develop an efficient block coordinate descent algorithm, which, as we empirically show, produces high quality solutions, and, in a special case, we are able to solve the proposed formulation exactly in linear time using dynamic programming. We empirically evaluate the proposed approach both on synthetic datasets and on real-world search query data. We show that the proposed approach outperforms existing approaches by one to two orders of magnitude in terms of its average (per element) estimation error and by 45-90 percent in terms of its expected magnitude of estimation error.

关键词： Frequency estimation Machine learning algorithms Machine learning signal processing algorithms Optimization Task analysis Monitoring Data streams streaming frequency estimation learning to hash optimal hashing scheme

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Fading Noise Suppression Method of Φ-OTDR System Based on GA-VMD Algorithm

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IEEE SENSORS JOURNAL 2023年第19期23卷 22608-22619页

作者： Li, Tian-Xiong Zhang, Fu-Dong Lin, Jun Bai, Xing-Ye Liu, Hao-Zhuang Jilin Univ Coll Instrumentat & Elect Engn Changchun 130061 Peoples R China

A distributed optical fiber acoustic sensing system (DAS) based on phase-sensitive optical time domain reflectometry (Phi-OTDR) can detect the vibration information along an optical fiber and has been widely used for various engineering detections;however, the high-coherence detection light source used in a DAS will cause coherent fading in the fiber, leading to system fading noise and affecting the monitoring of the target vibration events. In order to solve the problem of the fading noise in the DAS, this study considered a method of suppressing the fading noise based on the genetic algorithm (GA) and variational mode decomposition (VMD). Furthermore, the parameter selection problem in the VMD method is improved by introducing the GA. By introducing sample entropy as a fitness function and combining it with the GA, this method adaptively searches for the optimal decomposition parameters by iterating the minimum value of the fitness function according to the type of input signal. Then, it decomposes the noisy DAS data into multiple intrinsic mode functions (IMFs), and the permutation entropy and time-frequency domain information of each component were used to extract the effective IMF components for signal reconstruction to obtain the denoised signal. Experimental results show that compared with empirical mode decomposition (EMD), ensemble EMD (EEMD), and complete EEMD (CEEMD) methods, the proposed method can eliminate the mode aliasing problem, effectively suppress the fading noise of the DAS, improve the signal-to-noise ratio, and reduce the root-mean-square error. It is, thus, possible to more accurately monitor vibration events, improving the practical application value of the DAS.

关键词： signal processing algorithms Fading channels Optical fibers Optical fiber sensors Genetic algorithms Feature extraction signal resolution Distributed optical fiber fading noise parameter optimization variational mode decomposition (VMD)

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Uplink signal Detection via Look-Up Table-Based AMP for Massive MIMO Systems

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IEEE ACCESS 2023年 11卷 13044-13057页

作者： Shimamura, Atsunori Takahashi, Takumi Ibi, Shinsuke Sampei, Seiichi Osaka Univ Grad Sch Engn Suita 5650871 Japan Doshisha Univ Fac Sci & Engn Kyotanabe 6100394 Japan

We propose a novel look-up table (LUT)-based multi-user detection (MUD) algorithm to imitate approximate message passing (AMP) for uplink signal detection in massive multi-user multi-input multi-output (MU-MIMO) systems. When AMP is implemented with double-precision arithmetic, the dramatic increase in power consumption and process latency becomes a major barrier to practical application, as the system scale expands. To circumvent this issue, we design a novel referential AMP detector composed of many small LUTs cascaded hierarchically, where only informative integer-valued messages are exchanged on a factor graph (FG). Our method tracks the discrete distribution of the LUT outputs according to the multi-layer structure, and successively determines non-uniform quantization thresholds of the LUTs, using an unsupervised learning approach. Specifically, the thresholds at each level of the hierarchy, i.e., layer, are optimized by clustering with the Lloyd-Max algorithm with initial values given by the k-means++ method, in order to minimize the performance degradation caused by quantization errors. The efficacy of the proposed referential AMP detector is confirmed by numerical simulations, which show that the proposed method significantly outperforms the state-of-the-art (SotA) in terms of the bit error rate (BER) performance in various channel models. The results also indicate the referential AMP is robust to changes in wireless channels, and then clarify the reason in terms of the algorithmic structure.

关键词： Table lookup Multiuser detection Wireless communication Quantization (signal) Detectors Uplink signal processing algorithms Massive MIMO detection approximate message passing quantization look-up table unsupervised learning

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Fast algorithms for Deep Octonion Networks

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第1期34卷 543-548页

作者： Cariow, Aleksandr Cariowa, Galina West Pomeranian Univ Technol Dept Comp Sci & Informat Technol PL-70310 Szczecin Poland West Pomeranian Univ Technol Dept Comp Sci & Technol PL-70310 Szczecin Poland

This brief presents the results of a study of the possibilities of reducing the arithmetic complexity of computing basic operations in octonionic neural networks and also proposes new algorithmic solutions for efficiently performing these operations. Here, we primarily mean the operation of multiplying octonions, the operation of computing the dot product of two octonion-valued vectors, and the operation of multiple multiplications of an octonion by several other octonions. In order to reduce the computational complexity of these operations, it is proposed to use the fast Walsh-Hadamard transform, which is well known in digital signal processing. Using this transform reduces the number of multiplications and additions of real numbers required to perform computations. Thus, the use of the proposed algorithms will speed up computations in octonion-valued neural networks.

关键词： Neural networks Transforms signal processing algorithms Learning systems Symmetric matrices Sparse matrices Deep learning Acceleration of calculations deep neural networks fast Walsh-Hadamard transform (FWHT) octonions

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Nonlinear Spline Versoria Prioritization Optimization Adaptive Filter for Alpha-Stable Clutter

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第1期59卷 734-744页

作者： Guo, Wenyan Zhi, Yongfeng Northwestern Polytech Univ Sch Automat Xian 710129 Peoples R China

The increasing demand for a better quality of service in radar and wireless communications has attracted research on interference suppression. Nonlinear filtering methods based on spline architecture have been widely utilized in signal processing due to their effectiveness against alpha-stable interference. However, existing nonlinear spline adaptive filter algorithms suffer from high steady-state misalignment. To achieve lower steady-state misalignment along with having comparable computational complexity, we propose a nonlinear spline Versoria prioritization optimization adaptive filter (SPOAF-MVC) for alpha-stable clutter in this article. Furthermore, we study the bound on learning rate and computational complexity for the proposed algorithm. Numerical simulations confirm the effectiveness and efficiency of the proposed SPOAF-MVC algorithm for alpha-stable clutter under the Wiener system identification.

关键词： Adaptive filters Clutter Splines (mathematics) Kernel signal processing algorithms Robustness Computational complexity Alpha-stable clutter maximum Versoria criterion (MVC) nonlinear adaptive filter Wiener system identification

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Phase Retrieval Using Deep Dual Alternating Direction Method of Multipliers Network With Deep Sparse Prior Knowledge

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IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE processing 2025年 33卷 557-569页

作者： Kim, Moogyeong Chung, Wonzoo Korea Univ Dept Artificial Intelligence Seoul 02841 South Korea

In this paper, a deep phase retrieval algorithm for speech signals based on the dual Alternating Direction Method of Multipliers (ADMM) incorporating a deep prior network that exploits the sparsity of speech signals is presented. The proposed network, named DADMM-net, unfolds the dual ADMM for the $\ell _{1}$-regularized non-convex optimization problem of phase retrieval with several two-dimensional convolutional neural networks (2D-CNNs). In order to efficiently optimize the deep unfolding network for high-dimensional parameter vectors, a novel updating scheme referred to as soft coordinate descent (soft-CD) is proposed, where dual parameter updates are determined through interpolation between the current values and the updated values coordinate-wise with respect to the weights computed by deep networks in each layer. Numerical simulations on a publicly available dataset confirm the state-of-the-art performance of the proposed method in terms of perceptual evaluation of speech quality and short-time objective intelligibility with a significantly faster convergence speed compared to existing methods.

关键词： Optimization Convex functions Time-domain analysis Spectrogram signal processing algorithms Vectors Sparse approximation Approximation algorithms Knowledge engineering Time-frequency analysis Deep sparse prior deep unfolding dual ADMM non-convex optimization phase retrieval

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Research on Mixed-Strategy Based Grey Wolf Optimization for Gene Expression Classification

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IEEE ACCESS 2023年 11卷 103254-103273页

作者： Wang, Yang Liaoning Petrochem Univ Coll Sci Fushun 113001 Peoples R China

With the advantages of easy implement and few adjustment parameters, grey wolf optimization (GWO) performs well in solving global optimization problems. However, the original GWO can suffer from slow convergence speed and plunge into local optimums in dealing with complex problems. To address these issues, improvement of mixed-strategy based grey wolf optimization (MGWO) is proposed in this paper. Firstly, we present chaos strategy in combination with quasi-opposition learning strategy for generating more high-quality population to improve convergence speed of the algorithm. Then, we embed sine cosine mechanism into the original GWO to jump out of local optimums. We utilize the interaction of sine and cosine to better balance the exploration and exploitation capabilities of the algorithm. Finally, we use benchmark functions for testing the performance of proposed algorithm. Experimental results demonstrate that proposed algorithm can improve search accuracy and convergence speed compared with other state-of-the-art algorithms. Simultaneously, to verify further the performance of proposed algorithm, classification problems on gene expression datasets are also evaluated. Both continuous and binary versions of MGWO are respectively used for completing parameters optimization and feature selection. Experimental results show that proposed algorithm has better performance than others in terms of Sensitivity, Specificity, G-mean, Accuracy and F-measure.

关键词： Optimization Classification algorithms Behavioral sciences Heuristic algorithms Statistics Sociology signal processing algorithms Grey wolf optimization chaos strategy quasi-opposition learning sine cosine mechanism gene expression dataset

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Squared Sine Adaptive Algorithm and Its Performance Analysis

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE processing 2023年 31卷 3617-3628页

作者： Huang, Xinqi Li, Yingsong Zakharov, Yuriy Miao, Yongchun Huang, Zhixiang Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China Univ York Sch Phys Engn & Technol York YO10 5DD England

The squared sine adaptive (SSA) algorithm is presented for identification scenarios, such as acoustic-echo cancellation (AEC) applications, in non-Gaussian environments. To devise the SSA algorithm, a novel cost function is constructed by exerting a sliding window-type squared sine function on the estimation error vector, which provides robustness in impulsive-noise environments and speeds up convergence when the input is colored. Theoretical results are presented for predicting the mean-weight, convergence, transient excess-mean-square-error (EMSE), and tracking behaviour. Moreover, the minimum EMSE and the optimum step size for tracking are presented. The computational complexity of the SSA algorithm has also been investigated. Numerical experiments demonstrate that results of the theoretical analysis match the simulated results very well and the proposed SSA algorithm outperforms known algorithms in AEC applications.

关键词： Convergence signal processing algorithms Transient analysis Cost function Speech processing Prediction algorithms Behavioral sciences Adaptive filters AEC non-Gaussian environment performance analysis

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Contextual Bandit-Based Amplifier IBO Optimization in Massive MIMO Network

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IEEE ACCESS 2023年 11卷 127035-127042页

作者： Hoffmann, Marcin Kryszkiewicz, Pawel Poznan Univ Tech Inst Radiocommun PL-60965 Poznan Poland

Massive Multiple-Input Multiple-Output (MMIMO) is one of the 5G key enablers. Though, most of the works consider MMIMO under assumptions of ideal hardware. It has been shown that Power Amplifiers (PAs) introduce nonlinear distortion while operating close to their saturation power. Moreover, these distortions are in some cases beamformed toward the user, preventing antenna array gain from solving this problem. One of the possible solutions is an adaptive adjustment of the PA operating point, measured by Input Back off (IBO), to find a balance between wanted signal power and nonlinear distortion power. This work proposes a Contextual Bandit-Based IBO Optimization (COBBIO) algorithm to find rate-maximizing IBO for a given user's radio conditions using learning through interaction. The proposed solution is tested in a realistic analog beamforming MMIMO cell simulator with multiple functional blocks, e.g., precoder, user scheduler, and utilizing an accurate 3D Ray-Tracing radio channel model. COBBIO provides throughput gains both over fixed-IBO schemes and state-of-the-art analytical IBO adjustment algorithms. The highest gains were observed for the so-called cell-edge users, where up to 83% improvement over the state-of-the-art algorithm was observed for the proposed COBBIO algorithm.

关键词： OFDM signal to noise ratio Throughput Nonlinear distortion Optimization 5G mobile communication signal processing algorithms Machine learning Massive MIMO 5G machine learning nonlinear distortion input back-off (IBO)

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Generalized Modified Blake-Zisserman Robust Sparse Adaptive Filters

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2023年第1期53卷 647-652页

作者： Kumar, Krishna Karthik, M. L. N. S. George, Nithin V. Indian Inst Technol Gandhinagar Dept Elect Engn Gandhinagar 382355 India

In the past years, the generalized maximum correntropy criterion (GMCC) has been widely used in adaptive filters to provide robust behavior under non-Gaussian/impulsive noise environments. However, GMCC-based adaptive filters are affected by high steady-state misalignment. In order to enhance the robustness under non-Gaussian noise environments and reduce steady-state misalignment, a generalized modified Blake-Zisserman (GMBZ) robust loss function is introduced in this correspondence. Furthermore, a GMBZ adaptive filter (GMBZ-AF) has been developed that provides improved convergence performance over other existing algorithms. The proposed learning scheme has a computational complexity very similar to that of the GMCC-based adaptive filtering method. In order to further exploit the sparse nature of the system for identifying sparse systems and simultaneously provide robust convergence, two new robust sparse adaptive filters: 1) zero attracting GMBZ-AF (ZA-GMBZ-AF) and 2) reweighted ZA-GMBZ-AF (RZA-GMBZ-AF) have also been proposed. To further enhance the filter convergence performance, a new robust and sparsity-aware loss function called generalized modified dual Blake-Zisserman (GMDBZ) is also introduced in this correspondence and the corresponding GMDBZ adaptive filter (GMDBZ-AF) has been developed.

关键词： Cost function signal processing algorithms Steady-state Convergence Computational complexity Background noise Adaptation models Adaptive filters impulsive noise maximum correntropy criterion (MCC) robust learning sparse system identification

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