The diffusion least mean square algorithm with variable q-gradient

To avoid the local minima in optimization for distributed adaptive filters, the q-gradient diffusion least mean square (q-DLMS) algorithm uses the q-gradient vector to estimate parameters of interest in distributed networks. However, the q value in q-DLMS is fixed and required to be determined beforehand. To this end, a novel variable q-DLMS (v-q-DLMS) algorithm is proposed in this paper to improve the performance of q-DLMS and avoid the selection issue of q, simultaneously. The theoretical results of the proposed v-q-DLMS algorithm regarding accuracy and convergence are provided for performance analysis. In addition, the variable q-version of combination rule is derived by minimizing mean square derivation. Simulation results on distributed networks validate the correctness of obtained theoretical results and illustrate the superiorities of the proposed v-q-DLMS algorithm from the aspects of accuracy, convergence rate, and robustness. (C)& nbsp;2022 Elsevier Inc. All rights reserved.