Abstract
Available analyses of the diffusion LMS (DLMS) algorithm assume that the nodes probe the unknown system with zero delay. This assumption is unrealistic, since the unknown system is usually distant from the nodes. The present paper studies the behavior of the algorithm without this assumption. The analysis is done for a network having a central combiner. This structure reduces the dimensionality of the resulting stochastic models while preserving important diffusion properties. Communication delays between the nodes and the central combiner are also considered in the analysis. The analysis is done for system identification for cyclostationary white Gaussian nodal inputs. Mean and mean-square behaviors of the algorithm are analyzed. It is found that delays in probing the unknown system yield a bias in the algorithm without increasing its convergence time. The communication delays between the nodes and the central combiner increase the convergence time without affecting the steady-state behavior. The stability of the algorithm is not affected by either type of delay. The analysis exactly matches the simulations.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Signal and Information Processing over Networks
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
