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Network numerical analysis for the smoother and the lagged joint-process estimator

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Abstract

Motivated by the fact that the a priori least-squares-order-recursive lattice (LSORL) smoother is more robust than the LSORL joint-process estimator with lagged desired signals in the finite precision, we model numerical properties of the two algorithms by virtue of previous efforts. Then, we give the reason why the smoother is substantially more robust than the lagged joint-process estimator by providing the explicit analysis for the performance difference of the two algorithms.

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Acknowledgement

This work was supported by MKE/DDI [A2010D-D0004, Development of Green P2mP Wireless Backhaul System]

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Authors and Affiliations

  1. Division of Computer Engineering, Mokwon University, Daejeon, Korea

    Yang Sun Lee

  2. Electronics and Telecommunication Research Institute (ETRI), Daejeon, Korea

    Dong Kyoo Kim

  3. Department of Information and Communication Engineering, Fukuoka Institute of Technology (FIT), 3-30-1 Wajiro-Higashi, Higashi-Ku, Fukuoka, 811-0295, Japan

    Leonard Barolli

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  1. Yang Sun Lee
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  2. Dong Kyoo Kim
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Correspondence to Leonard Barolli.

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Lee, Y.S., Kim, D.K. & Barolli, L. Network numerical analysis for the smoother and the lagged joint-process estimator. J Supercomput 65, 1192–1204 (2013). https://doi.org/10.1007/s11227-012-0753-2

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  • DOI: https://doi.org/10.1007/s11227-012-0753-2

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