A greedy sparsity-promoting LMS for distributed adaptive learning in diffusion networks

Symeon Chouvardas, Gerasimos Mileounis, Nicholas Kalouptsidis, Sergios Theodoridis

Abstract: In this paper, a distributed adaptive algorithm for sparsity-aware learning in diffusion networks is developed. The algorithm follows the greedy roadmap for sparsity along with the adapt-combine co-operation strategy, based on the LMS rationale for adaptivity. A bound on the error norm between the obtained estimates and the target vector is computed, and the algorithm is shown to converge in the mean under some general assumptions. Finally, comparative experiments with a recently developed sparsity-promoting diffusion LMS demonstrate the enhanced performance of the proposed algorithm.