Learning distributed jointly sparse systems by collaborative LMS

In the proposed model of adaptive filtering network, distributed learning algorithm works cooperatively to identify separated unknown systems, which have different impulse responses. Specifically, JS-CoLMS algorithm is proposed to iteratively learn the unknown systems and the joint sparsity, based on a stochastic gradient approach and a subdifferentiable sparse-inducing penalty approximating the l_2,0 norm. The superior performance of the proposed algorithm and its relation to l₀-LMS and Leaky LMS are briefly discussed and verified by numerical experiments.