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Partial diffusion-based recursive least squares (PDRLS) is an effectivemethod for reducing computational load and power consumption in adaptivenetwork implementation. In this method, each node shares a part of itsintermediate estimate vector with its neighbors at each iteration. PDRLSalgorithm reduces the internode communications relative to the full-diffusionRLS algorithm. This selection of estimate entries becomes more appealing whenthe information fuse over noisy links. In this paper, we study the steady-stateperformance of PDRLS algorithm in presence of noisy links and investigate itsconvergence in both mean and mean-square senses. We also derive a theoreticalexpression for its steady-state meansquare deviation (MSD). The simulationresults illustrate that the stability conditions for PDRLS under noisy linksare not sufficient to guarantee its convergence. Strictly speaking, consideringnonideal links condition adds a new complexity to the estimation problem forwhich the PDRLS algorithm becomes unstable and do not converge for any value ofthe forgetting factor.

Partial Diffusion Recursive Least-Squares for Distributed Estimation under Noisy Links Condition