  Partial diffusion-based recursive least squares (PDRLS) is an effective
method for reducing computational load and power consumption in adaptive
network implementation. In this method, each node shares a part of its
intermediate estimate vector with its neighbors at each iteration. PDRLS
algorithm reduces the internode communications relative to the full-diffusion
RLS algorithm. This selection of estimate entries becomes more appealing when
the information fuse over noisy links. In this paper, we study the steady-state
performance of PDRLS algorithm in presence of noisy links and investigate its
convergence in both mean and mean-square senses. We also derive a theoretical
expression for its steady-state meansquare deviation (MSD). The simulation
results illustrate that the stability conditions for PDRLS under noisy links
are not sufficient to guarantee its convergence. Strictly speaking, considering
nonideal links condition adds a new complexity to the estimation problem for
which the PDRLS algorithm becomes unstable and do not converge for any value of
the forgetting factor.
