Analysis of Partial Diffusion LMS for Adaptive Estimation Over Networks with Noisy Links

Vahidpour, Vahid; Rastegarnia, Amir; Khalili, Azam; Bazzi, Wael M.; Sanei, Saeid

doi:10.1109/tnse.2017.2742360

Cited by 12 publications

(10 citation statements)

References 27 publications

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“…Therefore, the stability of (F ⊗ F ) has the same conditions as the stability of F . this means that, as F is stable (see [9], for details) then, the partial diffusion KF algorithm under imperfect information exchange is convergent and stable in mean-square sense, and the steady-state network MSD is given by (25).…”

Section: B Mean Performancementioning

confidence: 99%

“…This issue motivates us to investigate the performance of PDKF algorithm in such scenarios. Some useful results dealing with the effects of noisy links on the performance of diffusion-based strategies behavior are presented in [14]- [16], [24], [25]. In this paper, we figure out how the noisy links affect deterioration of the network performance during the exchange of weight estimates.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performance Analysis of Distributed Kalman Filtering With Partial Diffusion Over Noisy Network

Vahidpour

Rastegarnia

Latifi

et al. 2020

IEEE Trans. Aerosp. Electron. Syst.

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The performance of partial diffusion Kalman filtering (PDKF) algorithm for the networks with noisy links is studied here. A closed-form expression for the steady-state mean square deviation is then derived and theoretically shown that when the links are noisy, the communication-performance tradeoff, reported for the PDKF algorithm, does not hold. Additionally, optimal selection of combination weights is investigated and a combination rule along with an adaptive implementation is motivated. The results confirm the theoretical outcome.

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Section: B Mean Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Distributed Kalman Filtering With Partial Diffusion Over Noisy Network

Vahidpour

Rastegarnia

Latifi

et al. 2020

IEEE Trans. Aerosp. Electron. Syst.

Self Cite

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“…Moreover, the convergence rate would be "optimal" when Pk,i is chosen to be the form in the paper. Furthermore, some existing methods can be used to reduce the communication complexity and to make the algorithm suitable for higher dimensional signals, for examples, eventdriven methods [55], partial diffusion methods [21], [26], [27], and compressed methods [56] and so on.…”

Section: Algorithm 1 Distributed Ls Algorithmmentioning

confidence: 99%

Convergence of a Distributed Least Squares

Xie,

Zhang,

Guo

2019

Preprint

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With the fast development of the sensor and network technology, distributed estimation has attracted more and more attention, due to its capability in securing communication, in sustaining scalability, and in enhancing safety and privacy. In this paper, we consider a least-squares (LS)-based distributed algorithm build on a sensor network to estimate an unknown parameter vector of a dynamical system, where each sensor in the network has partial information only but is allowed to communicate with its neighbors. Our main task is to generalize the well-known theoretical results on the traditional LS to the current distributed case by establishing both the upper bound of the accumulated regrets of the adaptive predictor and the convergence of the distributed LS estimator, with the following key features compared with the existing literature on distributed estimation: Firstly, our theory does not need the previously imposed independence, stationarity or Gaussian property on the system signals, and hence is applicable to stochastic systems with feedback control. Secondly, the cooperative excitation condition introduced and used in this paper for the convergence of the distributed LS estimate is the weakest possible one, which shows that even if any individual sensor cannot estimate the unknown parameter by the traditional LS, the whole network can still fulfill the estimation task by the distributed LS. Moreover, our theoretical analysis is also different from the existing ones for distributed LS, because it is an integration of several powerful techniques including stochastic Lyapunov functions, martingale convergence theorems, and some inequalities on convex combination of nonnegative definite matrices.

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“…In [30][31][32], a Bernoulli variable is used to model the step-size parameter, so that when the step-size is zero, not all entries of intermediate estimates are updated and the adaptation step is skipped for some. In [33][34][35][36][37][38], a different notion of partial diffusion of information is employed which relies on the exchange of a subset of entries of the weight estimate vectors themselves. In other words, it is assumed that each node shares only a fraction of its local information with its neighbors.…”

Section: Introductionmentioning

confidence: 99%

Coordinate-Descent Adaptation over Hamiltonian Multi-Agent Networks

Khalili

Vahidpour

Rastegarnia

et al. 2021

Sensors

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The incremental least-mean-square (ILMS) algorithm is a useful method to perform distributed adaptation and learning in Hamiltonian networks. To implement the ILMS algorithm, each node needs to receive the local estimate of the previous node on the cycle path to update its own local estimate. However, in some practical situations, perfect data exchange may not be possible among the nodes. In this paper, we develop a new version of ILMS algorithm, wherein in its adaptation step, only a random subset of the coordinates of update vector is available. We draw a comparison between the proposed coordinate-descent incremental LMS (CD-ILMS) algorithm and the ILMS algorithm in terms of convergence rate and computational complexity. Employing the energy conservation relation approach, we derive closed-form expressions to describe the learning curves in terms of excess mean-square-error (EMSE) and mean-square deviation (MSD). We show that, the CD-ILMS algorithm has the same steady-state error performance compared with the ILMS algorithm. However, the CD-ILMS algorithm has a faster convergence rate. Numerical examples are given to verify the efficiency of the CD-ILMS algorithm and the accuracy of theoretical analysis.

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Analysis of Partial Diffusion LMS for Adaptive Estimation Over Networks with Noisy Links

Cited by 12 publications

References 27 publications

Performance Analysis of Distributed Kalman Filtering With Partial Diffusion Over Noisy Network

Performance Analysis of Distributed Kalman Filtering With Partial Diffusion Over Noisy Network

Convergence of a Distributed Least Squares

Coordinate-Descent Adaptation over Hamiltonian Multi-Agent Networks

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