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We address the problem of in-network distributed estimation for sparse vectors. In order to exploit the underlying sparsity of the vector of interest, we incorporate the - and -norm constraints into the cost function of the standard diffusion least-mean squares (LMS). This technique is equivalent to adding a zero-attracting term in the iteration of the LMS-based algorithm, which...
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