To improve the learning performance of the conventional diffusion least mean square (DLMS) algorithms, this article proposes Bayesian-learning-based DLMS (BL-DLMS) algorithms. First, the proposed BL-DLMS algorithms ar...
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To improve the learning performance of the conventional diffusion least mean square (DLMS) algorithms, this article proposes Bayesian-learning-based DLMS (BL-DLMS) algorithms. First, the proposed BL-DLMS algorithms are inferred from a Gaussian state-space model-based Bayesian learning perspective. By performing Bayesian inference in the given Gaussian state-space model, a variable step-size and an estimation of the uncertainty of information of interest at each node are obtained for the proposed BL-DLMS algorithms. Next, a control method at each node is designed to improve the tracking performance of the proposed BL-DLMS algorithms in the sudden change scenario. Then, a lower bound on the variable step-size of each node of the proposed BL-DLMS algorithms is derived to maintain the optimal steady-state performance in the nonstationary scenario (unknown parameter vector of interest is time-varying). Afterward, the mean stability and the transient and steady-state mean square performance of the proposed BL-DLMS algorithms are analyzed in the nonstationary scenario. In addition, two Bayesian-learning-based diffusion bias-compensated LMS algorithms are proposed to handle the noisy inputs. Finally, the superior learning performance of the proposed learning algorithms is verified by numerical simulations, and the simulated results are in good agreement with the theoretical results.
Available analyses of the diffusion LMS (DLMS) algorithm assume that the nodes probe the unknown system with zero delay. This assumption is unrealistic, since the unknown system is usually distant from the nodes. The ...
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Available analyses of the diffusion LMS (DLMS) algorithm assume that the nodes probe the unknown system with zero delay. This assumption is unrealistic, since the unknown system is usually distant from the nodes. The present paper studies the behavior of the algorithm without this assumption. The analysis is done for a network having a central combiner. This structure reduces the dimensionality of the resulting stochastic models while preserving important diffusion properties. Communication delays between the nodes and the central combiner are also considered in the analysis. The analysis is done for system identification for cyclostationary white Gaussian nodal inputs. Mean and mean-square behaviors of the algorithm are analyzed. It is found that delays in probing the unknown system yield a bias in the algorithm without increasing its convergence time. The communication delays between the nodes and the central combiner increase the convergence time without affecting the steady-state behavior. The stability of the algorithm is not affected by either type of delay. The analysis exactly matches the simulations.
The robustness of networks against malicious agents is a critical issue for their reliability in distributed learning. While a significant number of works in recent years have investigated the development of robust al...
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Many relevant problems in engineering demand the estimation of dynamic system states that are constrained to non-Euclidean spaces. Furthermore, it is generally advantageous to implement estimation methods in a network...
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Many relevant problems in engineering demand the estimation of dynamic system states that are constrained to non-Euclidean spaces. Furthermore, it is generally advantageous to implement estimation methods in a network of cooperating nodes instead of relying on a single data fusion center. With this in mind, we propose in this paper two new distributed particle filtering methods to track the states of a dynamic system that evolves on the Stiefel manifold, which arises naturally when the states are subject to nonlinear orthogonal constraints. The proposed algorithms are based on the Random Exchange and Adapt-then-Combine diffusion techniques, and perform parametric approximations using the matrix von Mises-Fisher distribution to compress information exchanged between network nodes. Estimates of the state are then determined via an empirical averaging method that approximates the centroid (Karcher mean) of the particle set. As we verify via numerical simulations, the proposed methods show improved performance compared to previous Particle and Extended Kalman filters designed for Euclidean state variables, and compared to a non-cooperative particle filtering algorithm. (C)& nbsp;2021 Elsevier Inc. All rights reserved.& nbsp;& nbsp
To improve the performance of the diffusion Huber-based normalized least mean square algorithm in the presence of impulsive noise, this paper proposes a distributed recursion scheme to adjust the thresholds. Because o...
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To improve the performance of the diffusion Huber-based normalized least mean square algorithm in the presence of impulsive noise, this paper proposes a distributed recursion scheme to adjust the thresholds. Because of the decreasing characteristic of the thresholds, the proposed algorithm can also be interpreted as a robust diffusion normalized least mean square algorithm with variable step sizes so that it has not only fast convergence but also small steady-state estimation error. Based on the contaminated Gaussian model, we analyze the mean square behavior of the algorithm in impulsive noise. Moreover, to ensure good tracking capability of the algorithm for the sudden change of parameters of interest, a control strategy is given that resets the thresholds with their initial values. Simulations in various noise scenarios show that the proposed algorithm performs better than many existing diffusion algorithms.
The paper proposes an on-line distributed implementation of the particle filter (DPF) for applications, where the sensing and consensus time scales are the same. We are motivated by state estimation problems in large,...
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ISBN:
(纸本)9781479999880
The paper proposes an on-line distributed implementation of the particle filter (DPF) for applications, where the sensing and consensus time scales are the same. We are motivated by state estimation problems in large, geographically-distributed agent/sensor networks, where bandwidth constraints limit the number of information transfers between neighbouring nodes. As an alternative to consensus strategies often used by the DPF, we propose a diffusive framework to eliminate the need of running the consensus step. In our Monte Carlo simulations, the proposed diffusion based DPF (D/DPF) outperforms the state-of-the-art consensus based DPF approaches in environments with limited bandwidth or/and intermittent connectivity.
A diffusion widely linear quaternion least mean square (D-WLIQLMS) algorithm for the collaborative processing of quaternion signals over distributed networks is proposed. We show that the underlying quaternion divisio...
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ISBN:
(纸本)9781479936878
A diffusion widely linear quaternion least mean square (D-WLIQLMS) algorithm for the collaborative processing of quaternion signals over distributed networks is proposed. We show that the underlying quaternion division algebra and the widely linear model allow for a unified processing of 3D and 4D data, which can exhibit both circular and noncircular distributions. The analysis shows that the D-WLIQLMS provides a solution that is robust to link and node failures in sensor networks. Simulations on benchmark 4D signals illustrate the advantages offered by the D-WLIQLMS.
A diffusion widely linear quaternion least mean square (D-WLIQLMS) algorithm for the collaborative processing of quaternion signals over distributed networks is proposed. We show that the underlying quaternion divisio...
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ISBN:
(纸本)9781479936878
A diffusion widely linear quaternion least mean square (D-WLIQLMS) algorithm for the collaborative processing of quaternion signals over distributed networks is proposed. We show that the underlying quaternion division algebra and the widely linear model allow for a unified processing of 3D and 4D data, which can exhibit both circular and noncircu-lar distributions. The analysis shows that the D-WL1QLMS provides a solution that is robust to link and node failures in sensor networks. Simulations on benchmark 4D signals illustrate the advantages offered by the D-WLIQLMS.
In this paper, we investigate the impact of peer bandwidth heterogeneity on the performance of a mesh-based P2P system for live streaming. We show that bandwidth heterogeneity constitutes an important resource for P2P...
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In this paper, we investigate the impact of peer bandwidth heterogeneity on the performance of a mesh-based P2P system for live streaming. We show that bandwidth heterogeneity constitutes an important resource for P2P live streaming systems. Indeed, by effectively exploiting it, the overall performance of the system is significantly improved. This requires the adoption of smart schemes for both the overlay topology construction and chunk scheduling mechanisms that discriminate among peers based on their bandwidth.
We consider large-scale mesh-based P2P systems for the distribution of real-time video content. Our goal is to study the impact that different design choices adopted while building the overlay topology may have on the...
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We consider large-scale mesh-based P2P systems for the distribution of real-time video content. Our goal is to study the impact that different design choices adopted while building the overlay topology may have on the system performance. In particular, we show that the adoption of different strategies leads to overlay topologies with different macroscopic properties. Representing the possible overlay topologies with different families of random graphs, we develop simple, yet accurate, fluid models that capture the dominant dynamics of the chunk distribution process over several families of random graphs. Our fluid models allow us to compare the performance of different strategies providing a guidance for the design of new and more efficient systems. In particular, we show that system performance can be significantly improved when possibly available information about peers location and/or peer access bandwidth is carefully exploited in the overlay topology formation process.
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