New variable regularized partial update affine projection algorithms for distributed estimation

Yijing Chu; Cheuk Ming Mak

doi:10.1109/ISPAN-FCST-ISCC.2017.19

New variable regularized partial update affine projection algorithms for distributed estimation

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

1 Citation (Scopus)

Abstract

This paper proposes new variable regularized (VR) partial update (PU) affine projection algorithms (APAs) for distributed estimation over adaptive networks. They extend the conventional diffuse PU-APAs (Diff-PU-APAs) by imposing a regularization parameter to mitigate possible impairments, such as modeling uncertainties and lacking of excitation, and to deal with sparse channel estimation problems. In addition, the mean and mean square convergence performance of the proposed algorithms is studied, which provides convergence conditions of the proposed algorithms. The performance of the Diff-VR-PU-APAs is demonstrated by several computer simulations in distributed networks.

Original language	English
Title of host publication	Proceedings - 14th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2017, 11th International Conference on Frontier of Computer Science and Technology, FCST 2017 and 3rd International Symposium of Creative Computing, ISCC 2017
Publisher	IEEE
Pages	325-330
Number of pages	6
Volume	2017-November
ISBN (Electronic)	9781538608401
DOIs	https://doi.org/10.1109/ISPAN-FCST-ISCC.2017.19
Publication status	Published - 27 Nov 2017
Event	14th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2017, 11th International Conference on Frontier of Computer Science and Technology, FCST 2017 and 3rd International Symposium of Creative Computing, ISCC 2017 - Exeter, Devon, United Kingdom Duration: 21 Jun 2017 → 23 Jun 2017

Conference

Conference	14th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2017, 11th International Conference on Frontier of Computer Science and Technology, FCST 2017 and 3rd International Symposium of Creative Computing, ISCC 2017
Country	United Kingdom
City	Exeter, Devon
Period	21/06/17 → 23/06/17

Keywords

Adaptive networks
Affine projection
Distributed estimation
Partial update
Variable regularization

ASJC Scopus subject areas

Industrial and Manufacturing Engineering
Control and Systems Engineering
Computer Science(all)
Electrical and Electronic Engineering

More information

10.1109/ISPAN-FCST-ISCC.2017.19

Cite this

Chu, Y., & Mak, C. M. (2017). New variable regularized partial update affine projection algorithms for distributed estimation. In Proceedings - 14th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2017, 11th International Conference on Frontier of Computer Science and Technology, FCST 2017 and 3rd International Symposium of Creative Computing, ISCC 2017 (Vol. 2017-November, pp. 325-330). IEEE. https://doi.org/10.1109/ISPAN-FCST-ISCC.2017.19

Chu, Yijing ; Mak, Cheuk Ming. / New variable regularized partial update affine projection algorithms for distributed estimation. Proceedings - 14th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2017, 11th International Conference on Frontier of Computer Science and Technology, FCST 2017 and 3rd International Symposium of Creative Computing, ISCC 2017. Vol. 2017-November IEEE, 2017. pp. 325-330

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abstract = "This paper proposes new variable regularized (VR) partial update (PU) affine projection algorithms (APAs) for distributed estimation over adaptive networks. They extend the conventional diffuse PU-APAs (Diff-PU-APAs) by imposing a regularization parameter to mitigate possible impairments, such as modeling uncertainties and lacking of excitation, and to deal with sparse channel estimation problems. In addition, the mean and mean square convergence performance of the proposed algorithms is studied, which provides convergence conditions of the proposed algorithms. The performance of the Diff-VR-PU-APAs is demonstrated by several computer simulations in distributed networks.",

keywords = "Adaptive networks, Affine projection, Distributed estimation, Partial update, Variable regularization",

author = "Yijing Chu and Mak, {Cheuk Ming}",

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doi = "10.1109/ISPAN-FCST-ISCC.2017.19",

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publisher = "IEEE",

address = "United States",

note = "14th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2017, 11th International Conference on Frontier of Computer Science and Technology, FCST 2017 and 3rd International Symposium of Creative Computing, ISCC 2017 ; Conference date: 21-06-2017 Through 23-06-2017",

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Chu, Y & Mak, CM 2017, New variable regularized partial update affine projection algorithms for distributed estimation. in Proceedings - 14th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2017, 11th International Conference on Frontier of Computer Science and Technology, FCST 2017 and 3rd International Symposium of Creative Computing, ISCC 2017. vol. 2017-November, IEEE, pp. 325-330, 14th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2017, 11th International Conference on Frontier of Computer Science and Technology, FCST 2017 and 3rd International Symposium of Creative Computing, ISCC 2017, Exeter, Devon, United Kingdom, 21/06/17. https://doi.org/10.1109/ISPAN-FCST-ISCC.2017.19

New variable regularized partial update affine projection algorithms for distributed estimation. / Chu, Yijing; Mak, Cheuk Ming.

Proceedings - 14th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2017, 11th International Conference on Frontier of Computer Science and Technology, FCST 2017 and 3rd International Symposium of Creative Computing, ISCC 2017. Vol. 2017-November IEEE, 2017. p. 325-330.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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T1 - New variable regularized partial update affine projection algorithms for distributed estimation

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AU - Mak, Cheuk Ming

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N2 - This paper proposes new variable regularized (VR) partial update (PU) affine projection algorithms (APAs) for distributed estimation over adaptive networks. They extend the conventional diffuse PU-APAs (Diff-PU-APAs) by imposing a regularization parameter to mitigate possible impairments, such as modeling uncertainties and lacking of excitation, and to deal with sparse channel estimation problems. In addition, the mean and mean square convergence performance of the proposed algorithms is studied, which provides convergence conditions of the proposed algorithms. The performance of the Diff-VR-PU-APAs is demonstrated by several computer simulations in distributed networks.

AB - This paper proposes new variable regularized (VR) partial update (PU) affine projection algorithms (APAs) for distributed estimation over adaptive networks. They extend the conventional diffuse PU-APAs (Diff-PU-APAs) by imposing a regularization parameter to mitigate possible impairments, such as modeling uncertainties and lacking of excitation, and to deal with sparse channel estimation problems. In addition, the mean and mean square convergence performance of the proposed algorithms is studied, which provides convergence conditions of the proposed algorithms. The performance of the Diff-VR-PU-APAs is demonstrated by several computer simulations in distributed networks.

KW - Adaptive networks

KW - Affine projection

KW - Distributed estimation

KW - Partial update

KW - Variable regularization

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Chu Y, Mak CM. New variable regularized partial update affine projection algorithms for distributed estimation. In Proceedings - 14th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2017, 11th International Conference on Frontier of Computer Science and Technology, FCST 2017 and 3rd International Symposium of Creative Computing, ISCC 2017. Vol. 2017-November. IEEE. 2017. p. 325-330 https://doi.org/10.1109/ISPAN-FCST-ISCC.2017.19