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A Distributed Adaptive Algorithm for Non-Smooth Spatial Filtering Problems in Wireless Sensor Networks

IEEE TRANSACTIONS ON signal processing 2024年 72卷 4682-4697页

作者： Hovine, Charles Bertrand, Alexander Katholieke Univ Leuven STADIUS Ctr Dynam Syst Signal Proc & Data Analyt B-3001 Leuven Belgium Katholieke Univ Leuven Leuven AI Inst Artificial Intelligence B-3001 Leuven Belgium

A wireless sensor network often relies on a fusion center to process the data collected by each of its sensing nodes. Such an approach relies on the continuous transmission of raw data to the fusion center, which typically has a major impact on the sensors' battery life. To address this issue in the particular context of spatial filtering and signal fusion problems, we recently proposed the Distributed Adaptive signal Fusion (DASF) algorithm, which distributively computes a spatial filter expressed as the solution of a smooth optimization problem involving the network-wide sensor signal statistics. In this work, we show that the DASF algorithm can be extended to compute the filters associated with a certain class of non-smooth optimization problems. This extension makes the addition of sparsity-inducing norms to the problem's cost function possible, allowing sensor selection to be performed in a distributed fashion, alongside the filtering task of interest, thereby further reducing the network's energy consumption. We provide a description of the algorithm, prove its convergence, and validate its performance and solution tracking capabilities with numerical experiments.

关键词： signal processing algorithms Wireless sensor networks Optimization Spatial filters Linear programming Europe Distributed databases Real-time systems Convergence Adaptive systems distributed signal processing non-smooth optimization spatial filtering

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Online Distributed Optimization With Nonconvex Objective Functions Under Unbalanced Digraphs: Dynamic Regret Analysis

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第5期11卷 4241-4251页

作者： Qin, Yanfu Lu, Kaihong Wang, Hongxia Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China

In this paper, the problem of online distributed optimization subject to a convex set is studied by employing a network of agents. Each agent's objective function is time-varying and nonconvex and the agents exchange information with their neighbors through a time-varying directed graph. Particularly, here the graph is not assumed to be balanced. To handle this problem, an online distributed algorithm is proposed based on the projection-free strategy and the push-sum protocol. The algorithm's performance is measured using dynamic regrets whose offline benchmark is to find a stationary point of the objective function at each time. Under mild assumptions on the graph and objective functions, we prove that if the deviation of the objective function sequence is sublinear with the square root of the time horizon, and the deviation of the objective function gradient sequence is sublinear with the time horizon, then the dynamic regret increases sublinearly. Finally, simulation experiments are presented to verify the effectiveness of the theoretical results.

关键词： Optimization Linear programming Heuristic algorithms signal processing algorithms Vectors Benchmark testing Multi-agent systems Dynamic regrets nonconvex optimization online distributed optimization unbalanced graphs

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Favor the Tortoise Over the Hare: An Efficient Detection Algorithm for Cooperative Networks

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 3153-3170页

作者： Feitosa, Allan E. Nascimento, Vitor H. Lopes, Cassio G. Univ Sao Paulo Dept Elect & Syst BR-05508010 Sao Paulo Brazil

We develop a low-cost algorithm to decide the current state of an environment being monitored by a cooperative and fully distributed wireless network of intelligent sensors, with a low time to reach a given performance. We consider WSNs (Wireless Sensor Networks) deployed under stringent power conditions, a situation for which low computational complexity and low power consumption is highly desired. We model a multiple hypothesis test using the diffusion Least Mean Square (dLMS) algorithm, a well known estimation technique used in distributed networks, to process data and also share information among nodes across the network. Our first contribution is showing that the performance of this theoretical detector, given in terms of the average probability of error, approximates the optimal performance if the underlying estimator operates at a slow learning rate, which is achieved by a sufficiently small step size. Notably, the detector performance improves as the value of the this step size is reduced, without any reduction in the detection error convergence rate, despite the slower estimation convergence rate. This somewhat counter-intuitive behavior is explained theoretically and confirmed by simulations. From this theoretical formulation, we devise a new detector with low computational complexity whose performance also closely matches that of the optimal and shows the same aforementioned behavior, where the slowest learning rate provides the best detection performance in terms of both the probability of error and convergence rate. We also show that this performance can be easily achieved provided that an adequate initialization of the estimation algorithm is chosen.

关键词： signal processing algorithms Sensors Wireless sensor networks Vectors Task analysis Estimation Environmental monitoring Distributed detection wireless sensor networks detection in WSN diffusion-LMS Bayesian detection

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Spectral Graph Learning With Core Eigenvectors Prior via Iterative GLASSO and Projection

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 3958-3972页

作者： Bagheri, Saghar Do, Tam Thuc Cheung, Gene Ortega, Antonio York Univ Dept Elect Engn & Comp Sci Toronto ON M3J 1P3 Canada Univ Southern Calif Dept Elect Comp Engn Los Angeles CA 90089 USA

Before the execution of many standard graph signal processing (GSP) modules, such as compression and restoration, learning of a graph that encodes pairwise (dis)similarities in data is an important precursor. In data-starved scenarios, to reduce parameterization, previous graph learning algorithms make assumptions in the nodal domain on i) graph connectivity (e.g., edge sparsity), and/or ii) edge weights (e.g., positive edges only). In this paper, given an empirical covariance matrix (C) over bar estimated from sparse data, we consider instead a spectral-domain assumption on the graph Laplacian matrix L: the first K eigenvectors (called"core" eigenvectors){u(k)}of L are pre-selected-e.g., based on domain-specific knowledge-and only the remaining eigen vectors are learned and parameterized. We first prove that, inside a Hilbert space of real symmetric matrices, the subspace H+u of positive semi-definite (PSD) matrices sharing a common set of core K eigenvectors {u(k)} is a convex cone. Inspired by the Gram-Schmidt procedure, we then construct an efficient operator to project a given positive definite (PD) matrix on to H-u(+). Finally, we design a hybrid graphical lasso/projection algorithm to compute a locally optimal inverse Laplacian L-1 is an element of H-u(+) given (C) over bar. We apply our graph learning algorithm in two practical settings: parliamentary voting interpolation and predictive transform coding in image compression. Experiments show that our algorithm outperformed existing graph learning schemes in data-starved scenarios for both synthetic data and these two settings

关键词： Laplace equations Symmetric matrices signal processing algorithms Covariance matrices Eigenvalues and eigenfunctions Optimization Reviews Graph signal processing graph learning convex optimization

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Robust Multitask Diffusion Bias Compensation M-Estimate algorithms for Distributed Adaptive Learning With Noisy Input

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IEEE signal processing LETTERS 2025年 32卷 1146-1150页

作者： Peng, Senran Jia, Lijuan Yang, Zi-Jiang Tao, Ran Wang, Yue Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Ibaraki Univ Coll Engn Dept Mech Syst Engn Ibaraki 3168511 Japan

This letter studies the issue of robust multitask distributed estimation under the error-in-variable (EIV) model where input noise and output impulsive noise are considered. In such cases, existing distributed algorithms suffer from severe performance degradation. To tackle this problem, a robust multitask diffusion bias-compensated least mean M-estimate (R-MD-BCLMM) is proposed. We adopt a new real-time input noise variance estimation method which utilizes piecewise linearity of the modified Huber function to resist input noises. To further improve network information exchange capability and estimation performance, a robust spatial average combination based multitask adaptive clustering strategy is proposed. Finally, simulations demonstrate that the proposed R-MD-BCLMM algorithm outperforms some state-of-the-art distributed algorithms.

关键词： Noise Estimation signal processing algorithms Clustering algorithms Noise measurement Vectors Real-time systems Cost function Adaptation models Modeling Multitask networks robust M-estimate bias compensation spatial average combination

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Exact Equality of the MSEs for Two Types of Nonlinear Adaptive Systems: Saturation and Dead-Zone Types

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IEEE ACCESS 2024年 12卷 181704-181717页

作者： Miyoshi, Seiji Yamaguchi, Yua Kansai Univ Fac Engn Sci Dept Elect Elect & Informat Engn Suita Osaka 5648680 Japan Nidec Adv Technol Corp Muko Kyoto 6170003 Japan

Adaptive signal processing systems, commonly utilized in applications such as active noise control and acoustic echo cancellation, often encompass nonlinearities due to hardware components such as loudspeakers, microphones, and amplifiers. Examining the impact of these nonlinearities on the overall performance of adaptive systems is critically important. In this study, we employ a statistical-mechanical method to investigate the behaviors of adaptive systems, each containing an unknown system with a nonlinearity in its output. We specifically address two types of nonlinearity: saturation and dead-zone types. We analyze both the dynamic and steady-state behaviors of these systems under the effect of such nonlinearities. Our findings indicate that when the saturation value is equal to the dead-zone width, the mean square errors (MSEs) in steady states are identical for both nonlinearity types. Furthermore, we derive a self-consistent equation to obtain the saturation value and dead-zone width that maximize the steady-state MSE. We theoretically clarify that these values depend on neither the step size nor the variance of background noise.

关键词： Adaptive systems Adaptation models Adaptive signal processing Adaptive equalizers Noise Vectors Steady-state signal processing algorithms Mathematical models Finite impulse response filters Adaptive filter adaptive signal processing system identification LMS algorithm saturation-type nonlinearity dead-zone-type nonlinearity statistical-mechanical analysis

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Optimizing a-DCF for Spoofing-Robust Speaker Verification

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IEEE signal processing LETTERS 2025年 32卷 1081-1085页

作者： Kurnaz, Oguzhan Mishra, Jagabandhu Kinnunen, Tomi H. Hanilci, Cemal Univ Eastern Finland Joensuu 80130 Finland Bursa Tech Univ TR-16310 Bursa Turkiye

Automatic speaker verification (ASV) systems are vulnerable to spoofing attacks. We propose a spoofing-robust ASV system optimized directly for the recently introduced architecture-agnostic detection cost function (a-DCF), which allows targeting a desired trade-off between the contradicting aims of user convenience and robustness to spoofing. We combine a-DCF and binary cross-entropy (BCE) with a novel straightforward threshold optimization technique. Our results with an embedding fusion system on ASVspoof2019 data demonstrate relative improvement of 13% over a system trained using BCE only (from minimum a-DCF of 0.1445 to 0.1254). Using an alternative non-linear score fusion approach provides relative improvement of 43% (from minimum a-DCF of 0.0508 to 0.0289).

关键词： Measurement Costs Training Cost function signal processing algorithms Error analysis Security Robustness Computer architecture Training data a-DCF spoofing-robust speaker verification

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Continuous-Time Distributed Algorithm for Seeking Fixed Points of Multiagent Quasi-Nonexpansive Operators

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2024年第3期11卷 1238-1250页

作者： Nian, Xiaohong Liu, Dongxin Li, Fan Cent South Univ Key Lab Inst Cluster Unmanned Syst Sch Automat Changsha 410083 Peoples R China

This article investigates the distributed fixed point seeking problem for the operator in real Hilbert spaces over the multiagent networks, where each local operator can only be approximately and privately accessed by the corresponding agent. Specifically, two cases, including the global operator is sum separable and block separable, are considered. Under the assumption that the local operator is Lipschitz and the global operator is quasi-nonexpansive, continuous-time distributed algorithms over balanced time-varying digraphs and strongly connected fixed digraphs are designed for two cases, respectively, and it is proved in both cases that the algorithms weakly converge to a fixed point of the considered operator. To the best of our knowledge, these two algorithms are the first continuous-time dynamical system results for the distributed fixed point seeking problem. Meanwhile, the designed algorithms can also be applied to solve resource allocation, noncooperative games, and multicluster games, providing a unified framework for their solution. Finally, several numerical simulations are presented to verify the theoretical results.

关键词： Distributed algorithms signal processing algorithms Heuristic algorithms Optimization Games Hilbert space Network systems Distributed algorithm distributed optimization fixed point theory game theory multiagent networks

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Distributed Precoder Based on Weighted MMSE With Low Complexity for Massive MIMO Systems

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IEEE COMMUNICATIONS LETTERS 2025年第3期29卷 482-486页

作者： Zhou, Ningxin Wang, Zheng Ma, Cong Huang, Yongming Shi, Qingjiang Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Peoples R China ZTE Corp Shenzhen 518057 Peoples R China Tongji Univ Sch Software Engn Shanghai 201804 Peoples R China

The weighted sum mean squared error minimization (WMMSE) algorithm has gained widespread adoption owing to its superior performance. In this letter, we propose a novel low-complexity distributed WMMSE (LCD-WMMSE) algorithm, which is implemented over a decentralized architecture based on ring topology. Although each distributed unit (DU) in LCD-WMMSE works only with the local channel state information (CSI), LCD-WMMSE algorithm is still able to approach the performance of traditional WMMSE. Moreover, we show that LCD-WMMSE is also scalable since its required interconnection bandwidth is independent of the number of transmitter antennas, making it promising to various scenarios of massive MIMO. Simulation results validate that the proposed LCD-WMMSE algorithm not only achieves the low complexity cost in a distributed manner but also exhibits negligible performance loss.

关键词： Precoding signal processing algorithms Massive MIMO Computer architecture Vectors Topology Downlink Interference Distributed databases Antenna arrays Distributed precoding massive MIMO decentralized architecture maximization sum-rate WMMSE

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Augmented Affine Projection Algorithm Based on Error Nonlinearity and Its Simplified Version

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 2767-2782页

作者： Gu, Yanglong Ni, Jingen Soochow Univ Sch Elect & Informat Engn Suzhou 215006 Peoples R China

The augmented affine projection algorithm (AAPA) has a faster convergence rate than the augmented complex-valued LMS (ACLMS) algorithm for correlated inputs. However, they are both not robust against impulsive interference. This work proposes an improved AAPA based on error nonlinearity, which is implemented with the derivative of the logistic distance metric. To reduce its computational complexity, this work also proposes a simplified version of the improved AAPA using the first-order Taylor approximation of the exponential function that is included in the derivative of logistic distance metric. To study the stochastic behaviors of the improved AAPA and its simplified version, their stability conditions and transient and steady-state performance are analyzed. Simulations are performed in the context of system identification and stereo acoustic echo cancellation to show the merits of the two proposed algorithms and to verify the accuracy of the theory on stability conditions and transient and steady-state performance.

关键词： Adaptive filters Vectors Logistics Measurement signal processing algorithms Filtering algorithms Noise Affine projection algorithm augmented linear model impulsive interference logistic distance metric

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