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To exploit the sparsity of the considered system, the diffusionproportionate-type least mean square (PtLMS) algorithms assign different gainsto each tap in the convergence stage while the diffusion sparsity-constrainedLMS (ScLMS) algorithms pull the components towards zeros in the steady-statestage. In this paper, by minimizing a differentiable cost function thatutilizes the Riemannian distance between the updated and previous weightvectors as well as the L0 norm of the weighted updated weight vector, wepropose a diffusion L0-norm constraint improved proportionate LMS (L0-IPLMS)algorithm, which combines the benefits of the diffusion PtLMS and diffusionScLMS algorithms and performs the best performance among them. Simulations in asystem identification context confirm the improvement of the proposedalgorithm.

Diffusion L0-norm constraint improved proportionate LMS algorithm for sparse distributed estimation