Tracking performance of incremental augmented complex least mean square adaptive network in the presence of model non-stationarity

This study addresses the tracking performance of the incremental augmented complex least mean square (IAC-LMS) algorithm, operating in the presence of model non-stationarities. The authors consider the mean-square deviation and excess mean square error as performance metrics and use energy conservation argument to derive closed-form expressions for the mentioned metrics. The expression describes how the IAC-LMS algorithm performs under such non-stationary conditions. The authors further find the step size range where the mean-square stability of the IAC-LMS algorithm is guaranteed. The authors provide some simulation results to support the theoretical derivations.