Browsing by Subject "Level-crossing quantization"

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    Communication efficient distributed estimation
    (IEEE, 2016) Utlu, İhsan; Kozat, Süleyman Serdar
    In this paper, we consider the problem of distributed estimation over adaptive networks with reduced load on communication resources. Novel diffusion strategies are presented that achieve up to three orders-of-magnitude reduction in the communication load on the network, while matching the state-of-the-art in performance. Specifically, the information transfer between the nodes of the network is conditioned on the level-crossings of the diffused parameter. We perform the mean stability analysis of the proposed algorithm, and provide numerical examples to verify the theoretical results.
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    Event-triggered distributed estimation with reduced communication load
    (2017-01) Utlu, İhsan
    We propose a novel algorithm for distributed processing applications constrained by the available communication resources using diffusion strategies that achieves up to a 103 fold reduction in the communication load over the network, while delivering a comparable performance with respect to the state of the art. After the computation of the local estimates, the information is diffused among the processing elements (or nodes) non-uniformly in time by conditioning the information transfer on level-crossings of the diffused parameter, resulting in a greatly reduced communication requirement. We provide the mean and meansquare stability analyses of the proposed algorithm, and illustrate the gain in communication efficiency compared to other reduced-communication distributed estimation schemes.
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    ItemOpen Access
    Resource-aware event triggered distributed estimation over adaptive networks
    (Elsevier Inc., 2017) Utlu, I.; Kilic, O. F.; Kozat S. S.
    We propose a novel algorithm for distributed processing applications constrained by the available communication resources using diffusion strategies that achieves up to a 103 fold reduction in the communication load over the network, while delivering a comparable performance with respect to the state of the art. After computation of local estimates, the information is diffused among the processing elements (or nodes) non-uniformly in time by conditioning the information transfer on level-crossings of the diffused parameter, resulting in a greatly reduced communication requirement. We provide the mean and mean-square stability analyses of our algorithms, and illustrate the gain in communication efficiency compared to other reduced-communication distributed estimation schemes.