异构网络中的自适应扩散方案
Adaptive Diffusion Schemes for Heterogeneous Networks
AI总结：
本文针对异构扩散网络中的分布式估计问题，提出了一种自适应组合策略，通过解耦适应与组合阶段，实现局部估计，并通过最小化网络均方误差优化组合器，实验显示其优于现有技术。
摘要：
本文研究了异构网络中的扩散策略，提出了一种解耦适应与组合阶段的自适应方案，通过最小化网络均方误差优化组合器，与传统Adapt-then-Combine方案相比，实验表明该方法在异构网络中表现更优。
英文摘要：
In this paper, we deal with distributed estimation problems in diffusion networks with heterogeneous nodes, i.e., nodes that either implement different adaptive rules or differ in some other aspect such as the filter structure or length, or step size. Although such heterogeneous networks have been considered from the first works on diffusion networks, obtaining practical and robust schemes to adaptively adjust the combiners in different scenarios is still an open problem. In this paper, we study a diffusion strategy specially designed and suited to heterogeneous networks. Our approach is based on two key ingredients: 1) the adaptation and combination phases are completely decoupled, so that network nodes keep purely local estimations at all times; and 2) combiners are adapted to minimize estimates of the network mean-square-error. Our scheme is compared with the standard Adapt-then-Combine scheme and theoretically analyzed using energy conservation arguments. Several experiments involving networks with heterogeneous nodes show that the proposed decoupled Adapt-then-Combine approach with adaptive combiners outperforms other state-of-the-art techniques, becoming a competitive approach in these scenarios.
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