Randomized incremental protocols over adaptive networks

Bazzi

et al. 2015

IET signal process.

In this paper the authors propose an adaptive estimation algorithm for in-network processing of complex signals over distributed networks. In the proposed algorithm, as the incremental augmented complex least mean square (IAC-LMS) algorithm, nodes of the network are allowed to collaborate via incremental cooperation mode to exploit the spatial dimension; while at the same time are equipped with LMS learning rules to endow the network with adaptation. The authors have extracted closed-form expressions that show how IAC-LMS algorithm performs in the steady-state. The authors further have derived the required conditions for mean and mean-square stability of the proposed algorithm. The authors use both synthetic benchmarks and real world non-circular data to evaluate the performance of the proposed algorithm. Simulation results also reveal that the IAC-LMS algorithm is able to estimate both second order circular (proper) and non-circular (improper) signals. Moreover, IAC-LMS algorithm outperforms the non-cooperative solution.

Section: Introductionmentioning

confidence: 99%

Derivation and analysis of incremental augmented complex least mean square algorithm

Bazzi

et al. 2015

IET signal process.

“…An adaptive network consists of a collection of spatially distributed nodes that are able to communicate with each other through a topology. Two major class of adaptive networks, based on the network topology are incremental networks [7,8,9,10,11,12,13,14,15,16,17,18,19] or diffusion algorithms [20,21,22,23,24,25,26,27,28,29,30]. In the incremental algorithms, a cyclic path through the network is established, and nodes communicate with neighbors within this path.…”

Section: Introductionmentioning

confidence: 99%

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

Circuits Syst Signal Process

Bazzi

et al. 2016

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

“…Lately, within the conceptual structure of in‐network distributed processing, there has been much interest in developing new distributed adaptive algorithms (also known as adaptive networks) for the solution of the parameter estimation problem in which the underlying signal statistics are unknown or time‐varying. In , distributed adaptive least‐mean squares (LMS) and recursive least squares (RLS) algorithms are proposed based on incremental and diffusion cooperation strategies. In comparison, incremental networks offer better estimation performance; however, they are less robust to node failures.…”

Section: Introductionmentioning

confidence: 99%

“…The algorithms in are developed based on the ideal channel assumption; that is, no noise is assumed on the paths, over which the nodes communicate with each other during the learning. The performance of adaptive networks will be affected by the presence of such noisy channels .…”

Section: Introductionmentioning

confidence: 99%

Transient analysis of diffusion least‐mean squares adaptive networks with noisy channels

Tinati

Adaptive Control & Signal

et al. 2011

In this paper, we study the effect of noisy channels on the transient performance of diffusion adaptive network with least-mean squares (LMS) learning rule. We first drive the update equation of diffusion LMS which incorporates the effects of noisy channels. Then, using the framework of fundamental weighted energy conservation relation, we derive closed-form expressions for learning curves in terms of mean-square deviation and excess mean-square error. We also find the mean and mean-square stability bounds of step-size for diffusion LMS with noisy channels. We show that although noisy channels affect the performance of the diffusion LMS network, the stability bounds of the step-size are the same form as in the ideal channels case. The derived closed-form expressions are shown to provide a good match with values found by simulation.