A robust LMS adaptive algorithm over distributed networks

This work studies the effect of erroneous noise power estimates on the behavior of a noise constrained diffusion-based adaptive algorithm for distributed adaptive networks. For good performance, the noise constrained diffusion least mean square (NCDLMS) algorithm assumes knowledge of the noise variance is available at each node. In this work, it is shown that the NCDLMS algorithm is robust to large variations in noise variance estimation. Moreover, the mean and steady-state analyses of the NCDLMS algorithm are carried out and simulation results are found to corroborate the theoretical findings. Great improvement in performance is obtained through the use of the proposed algorithm even when no information on the noise variance is available. The increased computational complexity of the NCDLMS algorithm is justified through the performance improvement it offers.