Performance Of Adaptive Network Research Articles

Convergence Analysis of Diffusion Adaptive Networks Working Through Free Space Optical Communication Channels

Our endeavour in this paper is about studying the performance of adaptive networks with the communication links between nodes that are based on free space optical (s) communications technology. In this case the communication links between nodes are assumed to suffer from turbulence induced fading along with Gaussian noise. The effects of FSO channels are represented by irradiance coefficients that follow Log-normal and Gamma–Gamma distributions. The log-normal distribution is used for modelling weak and moderate turbulence regimes and the Gamma–Gamma distribution is mainly used for modelling strong turbulence cases. We study the steady-state behaviour of the diffusion adaptive network under the FSO channel conditions and derive closed-form relations for mean square deviation (MSD) and excess mean square error for the diffusion network. The results of our simulations for implementing adaptive networks with wireless optical communication technology show the perfect match with derived theoretical outcomes that are presented based on MSD measurements. We conclude that the performance of adaptive network in FSO environment, depends directly on the mean and second order moment of the assumed channel distribution and as expected, for the channels with strong turbulence regimes, the convergence errors of adaptive networks becomes higher.

Asynchronous Adaptation and Learning Over Networks—Part III: Comparison Analysis

In Part II [3] we carried out a detailed mean-square-error analysis of the performance of asynchronous adaptation and learning over networks under a fairly general model for asynchronous events including random topologies, random link failures, random data arrival times, and agents turning on and off randomly. In this Part III, we compare the performance of synchronous and asynchronous networks. We also compare the performance of decentralized adaptation against centralized stochastic-gradient (batch) solutions. Two interesting conclusions stand out. First, the results establish that the performance of adaptive networks is largely immune to the effect of asynchronous events: the mean and mean-square convergence rates and the asymptotic bias values are not degraded relative to synchronous or centralized implementations. Only the steady-state mean-square-deviation suffers a degradation in the order of $\nu$, which represents the small step-size parameters used for adaptation. Second, the results show that the adaptive distributed network matches the performance of the centralized solution. These conclusions highlight another critical benefit of cooperation by networked agents: cooperation does not only enhance performance in comparison to stand-alone single-agent processing, but it also endows the network with remarkable resilience to various forms of random failure events and is able to deliver performance that is as powerful as batch solutions.

Amplify-and-forward scheme in incremental LMS adaptive network with noisy links: Minimum transmission power design

Abstract The performance of adaptive networks, considerably reduces when the communication links between nodes are subject to channel noise. One way to mitigate the effect of noisy links is to use the amplify-and-forward scheme. As the amplifying factor increases, the effect of channel noise decreases at the cost of more power consumption in nodes. In this paper we focus on design of incremental least-mean square (I-LMS) adaptive network with amplify-and-forward scheme. Our goal is to find the minimum amplifying factor at node k − 1 such that the steady-state value of mean-square deviation (MSD) at node k becomes smaller than a desired (predefined) value. We derive an expression for the desired amplifying factor at node k − 1 and provide the simulation results to clarify the derived theoretical result.