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- W2319847280 abstract "Recently, diffusion networks have been proposed to identify sparse linear systems which employ sparsity-aware algorithms like the zero-attracting LMS (ZA-LMS) at each node to exploit sparsity. In this brief, we show that the same optimum performance as reached by the aforementioned networks can also be achieved by a “heterogeneous” network with only a fraction of the nodes deploying ZA-LMS-based adaptation, provided that the ZA-LMS-based nodes are distributed over the network maintaining some “uniformity.” Reduction in the number of sparsity-aware nodes reduces the overall computational burden of the network. We show analytically and also by simulation studies that the only adjustment needed to achieve this reduction is a proportional increase in the value of the optimum zero attracting coefficient." @default.
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- W2319847280 date "2016-11-01" @default.
- W2319847280 modified "2023-09-26" @default.
- W2319847280 title "Sparse Distributed Estimation via Heterogeneous Diffusion Adaptive Networks" @default.
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- W2319847280 doi "https://doi.org/10.1109/tcsii.2016.2548182" @default.
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