.png){kind=icon}
Return
Sparse Distributed Estimation via Heterogeneous Diffusion Adaptive Networks
.png){kind=icon}
PRE
AI
DOI：10.1109/TCSII.2016.2548182.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Adaptive network
diffusion LMS
network mean square deviation (NMSD)
l(1) norm
Journal
I
IF:
4.9
Papers:
8.8K
Citations:
2.5W
Organization
Cited Papers
.png){kind=icon}
ShareSave no more
no more