Abstract
In this study, the authors propose a novel component-wise variable step-size diffusion least mean square (CWVSS-DLMS) algorithm for distributed estimation. Different from the traditional variable step-size DLMS (VSS-DLMS) algorithms in which the updating of all components in the weight vector are the same, the step sizes vary from each other on all components at each iteration in the CWVSS-DLMS algorithm. After deriving the CWVSS-DLMS algorithm, they perform theoretical analysis in terms of mean stability and mean-square behaviour. They have also compared the performance of the CWVSS-DLMS algorithm with several other DLMS algorithms through numerical simulations in both stationary and non-stationary environments. Simulation results show that the performance of the CWVSS-DLMS algorithm is more outstanding than the fixed step-size DLMS algorithm, several non-component-wise VSS-DLMS algorithms and existing component-wise VSS-DLMS algorithms in balancing high convergence rates and low steady-state misadjustment. Moreover, they have investigated the performance of the CWVSS-DLMS algorithm for estimating sparse parameter in a distributed way. Simulation results show that the CWVSS-DLMS algorithm can yield satisfying performance in sparsely distributed estimation regardless of the degree of sparsity in the real parameter.
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