Steady-State Analysis of Incremental LMS Adaptive Networks With Noisy Links

Khalili, Azam; Tinati, Mohammad Ali; Rastegarnia, Amir

doi:10.1109/tsp.2011.2112654

Cited by 51 publications

(24 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, we assumed ideal links between nodes in the networks. As we have shown in [22,23], the performance of incremental adaptive networks deteriorates in the presence of noisy links. Moreover, the performance of adaptive networks can vary significantly when they are implemented in finite-precision arithmetic, which makes it vital to analyze their performance in a quantized environment.…”

Section: Discussionmentioning

confidence: 82%

See 1 more Smart Citation

A Quality-aware Incremental LMS Algorithm for Distributed Adaptive Estimation

Bazzi

Rastegarnia

Khalili

2014

Int. J. Autom. Comput.

Self Cite

View full text Add to dashboard Cite

Abstract:In this paper, we consider the problem of unknown parameter estimation using a set of nodes that are deployed over an area. The recently proposed distributed adaptive estimation algorithms (also known as adaptive networks) are appealing solutions to the mentioned problem when the statistical information of the underlying process is not available or it varies over time. In this paper, our goal is to develop a new incremental least-mean square (LMS) adaptive network that considers the quality of measurements collected by the nodes. Thus, we use an adaptive combination strategy which assigns each node a step size according to its quality of measurement. The adaptive combination strategy improves the robustness of the proposed algorithm to the spatial variations of signal-to-noise ratio (SNR). The performance of our algorithm is more remarkable in inhomogeneous environments when there are some nodes with low SNRs in the network. The simulation results indicate the efficiency of the proposed algorithm.

show abstract

Section: Discussionmentioning

confidence: 82%

“…To have an adaptive distributed solution, we need to eliminate the constraint V k from the constrained optimization problem in (23). By applying a similar technique introduced in [18,19], we finally arrive at the following iterative solution…”

Section: Proposed Algorithmmentioning

confidence: 99%

A Quality-aware Incremental LMS Algorithm for Distributed Adaptive Estimation

Bazzi

Rastegarnia

Khalili

2014

Int. J. Autom. Comput.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Such data model is customary in distributed estimation methods 4,9,10,20,26 and will be applied to the target localization for sensor networks in the next section.…”

Section: Mean Stability Analysismentioning

confidence: 99%

An improved incremental least-mean-squares algorithm for distributed estimation over wireless sensor networks

Tan

Rong

et al. 2017

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

Distributed parameter estimation problem has attracted much attention due to its important foundation of some applications in wireless sensor networks. In this work, we first investigate the performance tradeoff between existing incremental least-mean-squares algorithm and traditional steepest-descent algorithm from the aspects of initial convergence rate, steady-state convergence behavior, and oscillation, and present the motivations for improvements. Thereby we propose an improved incremental least-mean-squares distributed estimation algorithm that starts from the incremental least-mean-squares algorithm and works toward the objective of improvement on initial convergence rate and steadystate performance. The proposed algorithm meets the requirements of diminishing step size for wireless sensor networks without any increase in communication overhead and the mean stability condition is derived for a practical guideline. A target localization model in wireless sensor networks is used to illustrate the effect of the proposed algorithm. Simulation results confirm the performance improvement of our method, as well as the effectiveness of application in wireless sensor networks.

show abstract

“…The effect of noisy links on the performance of incremental and diffusion adaptive networks are studied in [11,12]. The importance of such study stems from this fact that the performance of distributed adaptive estimation algorithm can drastically be deteriorated in the presence of noisy links [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…In all of the previous works [3][4][5][6][7][8][9][10][11][12], it is assumed that the length of the adaptive filter in each node is equal to that of the unknown parameter. Actually, the length of the unknown parameter similar to its coefficients is unknown.…”

Section: Introductionmentioning

confidence: 99%