Diffusion normalized least mean squares over wireless sensor networks

Baqi, Syed Abdul; Zerguine, Azzedine; Saeed, Muhammad Omer Bin

doi:10.1109/iwcmc.2013.6583770

Cited by 6 publications

(3 citation statements)

References 21 publications

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“…An important factor in learning abilities of adaptive networks is the adaptive filter that is embedded at the nodes. The mentioned adaptive networks use different types of adaptive filters such as leas meant-square (LMS) [8,14], recursive least-squares (RLS) [7,21], affine projection algorithm (APA) [10,11] and normalized least mean squares (NLMS) [31]. The LMS algorithm is a popular choice due to its stability and low complexity.…”

Section: Motivation For Current Workmentioning

confidence: 99%

Analysis of Incremental Augmented Affine Projection Algorithm for Distributed Estimation of Complex-Valued Signals

Khalili

Rastegarnia

Bazzi

et al. 2016

Circuits Syst Signal Process

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This paper considers the problem of distributed estimation in an incremental network when the measurements taken by the node follow a widely linear model. The proposed algorithm which we refer to it as incremental augmented affine projection algorithm (incAAPA) utilizes the full second order statistical information in the complex domain. Moreover, it exploits spatio-temporal diversity to improve the estimation performance. We derive steady-state performance metric of the incAAPA in terms of the mean-square deviation (MSD).We further derive sufficient conditions to ensure mean-square convergence. Our analysis illustrate that the proposed algorithm is able to process both second

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Section: Motivation For Current Workmentioning

confidence: 99%

Analysis of Incremental Augmented Affine Projection Algorithm for Distributed Estimation of Complex-Valued Signals

Khalili

Rastegarnia

Bazzi

et al. 2016

Circuits Syst Signal Process

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“…However, it does not exploit all the data available to a particular node for inference. Diffusion based algorithms [6,23,7,35,8,15,3,13,12,2] exploit the weighted local information from its neighbours and has been established as a viable solution for distributed adaptive filtering. This gives faster convergence as compared to incremental based approaches at the cost of slightly higher computational complexity.…”

Section: Related Workmentioning

confidence: 99%

Finite Dictionary Variants of the Diffusion KLMS Algorithm

Mitra¹,

Bhatia²

2015

Preprint

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The diffusion based distributed learning approaches have been found to be a viable solution for learning over linearly separable datasets over a network. However, approaches till date are suitable for linearly separable datasets and need to be extended to scenarios in which we need to learn a non-linearity. In such scenarios, the recently proposed diffusion kernel least mean squares (KLMS) has been found to be performing better than diffusion least mean squares (LMS). The drawback of diffusion KLMS is that it requires infinite storage for observations (also called dictionary). This paper formulates the diffusion KLMS in a fixed budget setting such that the storage requirement is curtailed while maintaining appreciable performance in terms of convergence. Simulations have been carried out to validate the two newly proposed algorithms named as quantised diffusion KLMS (QDKLMS) and fixed budget diffusion KLMS (FBDKLMS) against KLMS, which indicate that both the proposed algorithms deliver better performance as compared to the KLMS while reducing the dictionary size storage requirement.

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“…Compared to centralised estimation in which a fusion center is needed for receiving and processing data from all agents in the network, distributed estimation reduces energy consumption, and is insensitive to the failure of the fusion center [1–3]. In terms of cooperation strategy among nodes in the network, distributed estimation algorithms are generally classified into the diffusion kind [4–15] and the incremental kind [16–18]. In comparison, the diffusion algorithms are less sensitive to link and node failures while incremental algorithms become less robust as the number of nodes increases.…”

Section: Introductionmentioning

confidence: 99%

Component‐wise variable step‐size diffusion least mean square algorithm for distributed estimation

Huang

Yao

2019

IET signal process.

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In this study, the authors propose a novel component‐wise variable step‐size diffusion least mean square (CWVSS‐DLMS) algorithm for distributed estimation. Different from the traditional variable step‐size DLMS (VSS‐DLMS) algorithms in which the updating of all components in the weight vector are the same, the step sizes vary from each other on all components at each iteration in the CWVSS‐DLMS algorithm. After deriving the CWVSS‐DLMS algorithm, they perform theoretical analysis in terms of mean stability and mean‐square behaviour. They have also compared the performance of the CWVSS‐DLMS algorithm with several other DLMS algorithms through numerical simulations in both stationary and non‐stationary environments. Simulation results show that the performance of the CWVSS‐DLMS algorithm is more outstanding than the fixed step‐size DLMS algorithm, several non‐component‐wise VSS‐DLMS algorithms and existing component‐wise VSS‐DLMS algorithms in balancing high convergence rates and low steady‐state misadjustment. Moreover, they have investigated the performance of the CWVSS‐DLMS algorithm for estimating sparse parameter in a distributed way. Simulation results show that the CWVSS‐DLMS algorithm can yield satisfying performance in sparsely distributed estimation regardless of the degree of sparsity in the real parameter.

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Diffusion normalized least mean squares over wireless sensor networks

Cited by 6 publications

References 21 publications

Analysis of Incremental Augmented Affine Projection Algorithm for Distributed Estimation of Complex-Valued Signals

Analysis of Incremental Augmented Affine Projection Algorithm for Distributed Estimation of Complex-Valued Signals

Finite Dictionary Variants of the Diffusion KLMS Algorithm

Component‐wise variable step‐size diffusion least mean square algorithm for distributed estimation

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