English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

This article presents the formulation and steady-state analysis of thedistributed estimation algorithms based on the diffusion cooperation scheme inthe presence of errors due to the unreliable data transfer among nodes. Inparticular, we highlight the impact of transmission errors on the least-meansquares (LMS) adaptive networks. We develop the closed-form expressions of thesteady-state mean-square deviation (MSD) which is helpful to assess the effectsof the imperfect information flow on on the behavior of the diffusion LMSalgorithm in terms of the steady-state error. The model is then validated byperforming Monte Carlo simulations. It is shown that local and global MSDcurves are not necessarily monotonic increasing functions of the errorprobability. We also assess sufficient conditions that ensure mean andmean-square stability of diffusion LMS strategies in the presence oftransmission errors. Moreover, issues such as scalability in the sense ofnetwork size and regressor size, spatially correlated observations, as well asthe effect of the distribution of the noise variance are studied.  While the proposed theoretical framework is general in the sense that it isnot confined to a particular source of error during information diffusion, forpractical reasons we additionally study a specific scenario where errors occurat the medium access control (MAC) level. We develop a model to quantify theMAC-level transmission errors according to the network topology and systemparameters for a set of nodes employing a backoff procedure to access thechannel. To overcome the problem of unreliable data exchange, we propose anenhanced combining rule that can be deployed in order to improve theperformance of diffusion estimation algorithms by using the knowledge of theproperties of the transmission errors.

Formulation and Steady-state Analysis of LMS Adaptive Networks for Distributed Estimation in the Presence of Transmission Errors