Jin, DanqiChen, JieRichard, CedricChen, JingdongSayed, Ali H.2020-05-142020-05-142020-05-142020-01-0110.1109/TSP.2020.2975346https://infoscience.epfl.ch/handle/20.500.14299/168736WOS:000529866500001Diffusion adaptation is a powerful strategy for distributed estimation and learning over networks. Motivated by the concept of combining adaptive filters, this work proposes a combination framework that aggregates the operation of multiple diffusion strategies for enhanced performance. By assigning a combination coefficient to each node, and using an adaptation mechanism to minimize the network error, we obtain a combined diffusion strategy that benefits from the best characteristics of all component strategies simultaneously in terms of excess-mean-square error (EMSE). Analyses of the universality are provided to show the superior performance of affine combination scheme and to characterize its behavior in the mean and mean-square sense. Simulation results are presented to demonstrate the effectiveness of the proposed strategies, as well as the accuracy of theoretical findings.Engineering, Electrical & ElectronicEngineeringindexesadaptive systemselectronic mailsignal processing algorithmsestimationaggregatessimulationdistributed optimizationdiffusion strategyaffine combinationadaptive fusionstochastic performanceconvex combinationlmsperformanceadaptationtransienttext::journal::journal article::research article