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probabilistic quantization of unbiased broadcast gossip algorithms for consensus in distributed networks

WIRELESS NETWORKS 2024年第5期30卷 3349-3355页

作者： Wu, Shaochuan Wei, Yuming Gao, Yulong Zhang, Wenbin Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China

In this paper, probabilistic quantization on Unbiased Broadcast Gossip Algorithms (UBGA) is introduced to adapt to the limited bandwidth of distributed network channels. This innovative quantized consensus algorithm is called Quantized UBGA (QUBGA). By briefly describing the graph and time model, and reviewing UBGA, it is proved that QUBGA can converge in expectation and in the second moment for strongly connected digraphs. Furthermore, it is proved that the convergence rate of QUBGA is related to quantized resolution and topology structure. Performance analysis results are consistent with theoretical analysis.

关键词： Consensus probabilistic quantization Gossip algorithm Satellite sensor networks

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Robust Resilient H8 State Estimation for Time-varying Recurrent Neural Networks Subject to probabilistic quantization Under Variance Constraint

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2023年第2期21卷 684-695页

作者： Gao, Yan Hu, Jun Yu, Hui Du, Junhua Harbin Univ Sci & Technol Dept Math Harbin 150080 Peoples R China Harbin Univ Sci & Technol Sch Automat Harbin 150080 Peoples R China Qiqihar Univ Coll Sci Qiqihar Peoples R China

This paper is concerned with the robust resilient H-& INFIN;state estimation problem for time-varying recurrent neural networks (TVRNNs) with probabilistic quantization under variance constraint. Here, a situation is considered where the signals are quantized before entering the network, and the occurrence probability is assumed to be known. In addition, during the design of the state estimation algorithm, the additive variation of the estimator gain is considered to reflect the parameter deviation that may occur during the execution. The main purpose is to design a finite-horizon resilient state estimation algorithm such that, in the presence of probabilistic quantization and estimator gain perturbation, some sufficient criteria are obtained for the estimation error system to satisfy the prescribed H-& INFIN;performance requirement within the finite-horizon and the error variance boundedness. Finally, a numerical example is conducted to verify the feasibility of the presented estimation algorithm against the probabilistic quantization and estimator gain perturbation.

关键词： H-infinity performance probabilistic quantization resilient state estimator time-varying recurrent neural networks variance constraint

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Second-order Distributed Consensus with Modified probabilistic quantization

Second-order Distributed Consensus with Modified Probabilist...

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12th International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Peng Huanxin Sheng Andong Wang Wenkai Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Jiangsu Peoples R China Nanjing Inst Ind Technol Sch Mech Engn Nanjing 210046 Jiangsu Peoples R China

ISBN: (纸本)9781467318723;9781467318716

In order to improve the accuracy of distributed consensus under quantized communication, in the paper, we firstly propose a modification to the probabilistically quantized distributed (PQDA) algorithm, and we update the state of every node by its state and the probabilistically quantized information of the adjacency nodes. Moreover, based on the modification, we propose a second-order modified probabilistically quantized algorithm. We analyze the performance and the mean square errors of the second-order modified probabilistic quantization distributed consensus, by analysis and simulation, the results show the second-order distributed consensus with modified probabilistic quantization fails to reach a consensus, but the mean square error is far smaller than that of the PQDA algorithm, and the curve chart of the new algorithm is similar to that of the distributed average consensus algorithm without distortion.

关键词： distributed consensus quantized communication probabilistic quantization

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Accumulative-Event-Based Proportional-Integral Observer Design for Partially State-Saturated Systems Under probabilistic quantizations

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第3期55卷 2086-2097页

作者： Guo, Jiyue Wang, Zidong Zou, Lei Dong, Hongli Han, Qing-Long Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China Brunel Univ London Dept Comp Sci Uxbridge UB8 3PH Middx England Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Minist Educ Engn Res Ctr Digitalized Text & Fash Technol Shanghai 201620 Peoples R China Northeast Petr Univ Artificial Intelligence Energy Res Inst Daqing 163318 Peoples R China Northeast Petr Univ Heilongjiang Prov Key Lab Networking & Intelligent Daqing 163318 Peoples R China Swinburne Univ Technol Sch Sci Comp & Engn Technol Melbourne Vic 3122 Australia

In this article, we address the design issue of the proportional-integral observer (PIO) for partially state-saturated systems, which are affected by probabilistic quantizations and an accumulation-based event-triggered mechanism (ABETM). A comprehensive model is established to characterize partial state saturations, wherein only a part of the state variables are saturated while the remaining variables maintain normal conditions. To conserve communication resources, an ABETM is employed to determine the release of system measurements to the PIO. Before transmission over the communication network, the triggered signal is quantized through a probabilistic quantization mechanism. The goal of this article is to devise a PIO that guarantees ultimate boundedness of the estimation error dynamics in the mean square. Initially, a sufficient condition is derived to ensure that the estimation error dynamics are exponentially ultimately bounded in the mean square. Following this, necessary PIO gains are identified by solving specific matrix inequalities, and the efficacy of this proposed PIO approach is validated using a three-tank system simulation.

关键词： quantization (signal) probabilistic logic Observers Electronic mail Event detection Petroleum Noise Linear matrix inequalities Estimation error Electrical engineering Accumulation-based event-triggered mechanism (ABETM) matrix inequality partially state-saturated system probabilistic quantization proportional-integral observer (PIO)

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Distributed Parameter Estimation With Quantized Communication via Running Average

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2015年第17期63卷 4634-4646页

作者： Zhu, Shanying Soh, Yeng Chai Xie, Lihua Nanyang Technol Univ Sch Elect & Elect Engn Ctr Syst Intelligence & Efficiency EXQUISITUS IoT Lab Singapore 639798 Singapore

In this paper, we consider the problem of parameter estimation over sensor networks in the presence of quantized data and directed communication links. We propose a two-stage distributed algorithm aiming at achieving the centralized sample mean estimate in a distributed manner. Different from the existing algorithms, a running average technique is utilized in the proposed algorithm to smear out the randomness caused by the probabilistic quantization scheme. With the running average technique, it is shown that the centralized sample mean estimate can be achieved both in the mean square and almost sure senses, which is not observed in the standard consensus algorithms. In addition, the rates of convergence are given to quantify the mean square and almost sure performances. Finally, simulation results are presented to illustrate the effectiveness of the proposed algorithm and highlight the improvements by using running average technique.

关键词： Directed topology distributed estimation probabilistic quantization running average

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Optimal Identical Binary Quantizer Design for Distributed Estimation

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2012年第7期60卷 3896-3901页

作者： Kar, Swarnendu Chen, Hao Varshney, Pramod K. Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA Boise State Univ Coll Engn Boise ID 83725 USA

We consider the design of identical one-bit probabilistic quantizers for distributed estimation in sensor networks. We assume the parameter-range to be finite and known and use the maximum Cramer-Rao lower bound (CRB) over the parameter-range as our performance metric. We restrict our theoretical analysis to the class of antisymmetric quantizers and determine a set of conditions for which the probabilistic quantizer function is greatly simplified. We identify a broad class of noise distributions, which includes Gaussian noise in the low-SNR regime, for which the often used threshold-quantizer is found to be minimax-optimal. Aided with theoretical results, we formulate an optimization problem to obtain the optimum minimax-CRB quantizer. For a wide range of noise distributions, we demonstrate the superior performance of the new quantizer-particularly in the moderate to high-SNR regime.

关键词： Distributed estimation dithering minimax CRLB probabilistic quantization

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Quantized Consensus by the ADMM: probabilistic Versus Deterministic Quantizers

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2016年第7期64卷 1700-1713页

作者： Zhu, Shengyu Chen, Biao Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

This paper develops efficient algorithms for distributed average consensus with quantized communication using the alternating direction method of multipliers (ADMM). We first study the effects of probabilistic and deterministic quantizations on a distributed ADMM algorithm. With probabilistic quantization, this algorithm yields linear convergence to the desired average in the mean sense with a bounded variance. When deterministic quantization is employed, the distributed ADMM converges to a consensus within 3 + right perpendicularlog(1+delta) Omega left perpendicular iterations where delta > 0 depends on the network topology and Omega is a polynomial fraction depending on the quantization resolution, the agents' data, and the network topology. A tight upper bound on the consensus error is also obtained, which depends only on the quantization resolution and the average degree of the graph. This bound is much preferred in large scale networks over existing algorithms whose consensus errors are increasing in the range of agents' data, the quantization resolution, and the number of agents. We finally propose our algorithm which combines both probabilistic and deterministic quantizations. Simulations show that the consensus error of our algorithm is typically less than one quantization resolution for all connected networks where agents' data can be of arbitrary magnitudes.

关键词： Alternating direction method of multipliers deterministic quantization dither linear convergence probabilistic quantization quantized consensus

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Distributed average consensus via gossip algorithm with real-valued and quantized data for 0 <q <1

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SYSTEMS & CONTROL LETTERS 2010年第9期59卷 536-542页

作者： Yuan, Deming Xu, Shengyuan Zhao, Huanyu Chu, Yuming Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Jiangsu Peoples R China Huzhou Teachers Coll Dept Math Huzhou 313000 Zhejiang Peoples R China

This paper studies the problem of the gossip consensus algorithm with real-valued and quantized data. We study the effect of the mixing parameter on the convergence rate of the proposed gossip consensus algorithm, and show when the proposed bounds are optimized with respect to the mixing parameter. For a gossip consensus algorithm with quantized data, we prove that it can achieve the consensus almost surely, and the expected value of the final states is equal to the average of the initial states. Moreover, we provide a result characterizing the convergence performance of the distance from consensus and make a comparison with the non-quantized gossip consensus algorithm. Finally, simulation results are provided to evaluate the effectiveness of the proposed algorithm. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Average consensus Gossip algorithm probabilistic quantization

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Distributed average consensus with dithered quantization

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2008年第10期56卷 4905-4918页

作者： Aysal, Tuncer Can Coates, Mark J. Rabbat, Michael G. Cornell Univ Sch Elect & Comp Engn Commun Res Signal Proc Grp Ithaca NY 14853 USA McGill Univ Dept Elect & Comp Engn Telecommun & Signal Proc Comp Networks Lab Montreal PQ H3A 2A7 Canada

In this paper, we develop algorithms for distributed computation of averages of the node data over networks with bandwidth/power constraints or large volumes of data. Distributed averaging algorithms fail to achieve consensus when deterministic uniform quantization is adopted. We propose a distributed algorithm in which the nodes utilize probabilistically quantized information, i.e., dithered quantization, to communicate with each other. The algorithm we develop is a dynamical system that generates sequences achieving a consensus at one of the quantization values almost surely. In addition, we show that the expected value of the consensus is equal to the average of the original sensor data. We derive an upper bound on the mean-square-error performance of the probabilistically quantized distributed averaging (PQDA). Moreover, we show that the convergence of the PQDA is monotonic by studying the evolution of the minimum-length interval containing the node values. We reveal that the length of this interval is a monotonically nonincreasing function with limit zero. We also demonstrate that all the node values, in the worst case, converge to the final two quantization bins at the same rate as standard unquantized consensus. Finally, we report the results of simulations conducted to evaluate the behavior and the effectiveness of the proposed algorithm in various scenarios.

关键词： average consensus distributed algorithms dithering probabilistic quantization sensor networks

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Robust decentralized estimation fusion in energy-constrained wireless sensor networks with correlated noises

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DIGITAL SIGNAL PROCESSING 2018年 78卷 305-315页

作者： Qu, Xiaomei Zhou, Jie Tan, Wenrong Southwest MinZu Univ Coll Comp Sci & Technol Chengdu 610041 Sichuan Peoples R China Sichuan Univ Coll Math Chengdu 610064 Sichuan Peoples R China

Decentralized estimation fusion based on quantized local estimates is investigated. Motivated by the fact that the variance of quantization noise suffers from bounded uncertainty, we address the robust quantized estimation fusion problem in energy constrained wireless sensor networks. The quantization levels and fusion weights are jointly determined by minimizing worst-case of mean squared error of fused estimate among the uncertainties. Moreover, the effects of observation noise correlations on the fusion performance is explored by considering three different correlation scenarios. The optimal solution of the proposed robust fusion problem in each scenario is derived, in a closed-form or by solving a semidefinite programming. Theoretical analysis indicates that if the correlations exist, the fusion performance might improve or degrade, depending on correlation structure. However, appropriate usage of the correlations in fusion center can asymptotically improve the estimation fusion performance. Numerical simulations are provided to verify the theoretical analysis, and the results illustrate the good performance of the proposed method. (C) 018 Elsevier Inc. All rights reserved.

关键词： Decentralized estimation Correlated noises probabilistic quantization Minimax robust fusion Energy constraint

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