Publication Details

Robust Incremental Least Mean Square Algorithm With Dynamic Combiner

QADRI Syed Safi Uddin, ARIF Muhammad, NASEEM Imran and MOINUDDIN Muhammad. Robust Incremental Least Mean Square Algorithm With Dynamic Combiner. IEEE Access, vol. 10, no. 10, 2022, pp. 75135-75143. ISSN 2169-3536. Available from: https://ieeexplore.ieee.org/document/9832595
Czech title
Robustní inkrementální algoritmus nejmenších čtverců s dynamickým slučovačem
Type
journal article
Language
english
Authors
Qadri Syed Safi Uddin (KIET)
Arif Muhammad, Ph.D. (DCSY FIT BUT)
Naseem Imran (UniWA)
Moinuddin Muhammad (KAU)
URL
Keywords

Distributed networks, incremental least mean squares algorithm, decentralized estimation, steady-state analysis, noisy link

Abstract

In distributed wireless networks, the adaptation process depends on the information being shared between various nodes. The global minimum, is therefore, likely to be affected when the information shared between the nodes gets corrupted. This could happen due to several reasons namely link failure, noisy environment and erroneous data etc. In this research, we propose a computationally efficient robust incremental least mean square (RILMS) algorithm to resolve the aforementioned issues. Essentially, a fusion step is introduced in the framework of the incremental least mean square (ILMS). Prior to adaptation at a node, the information shared by the neighbouring node is fused with the temporally preceding information of the node using an efficient combiner. An adaptive fusion strategy is proposed resulting in dynamic weight assignment for the fusion step. Closed form expression for the steady-state excess mean square error (EMSE) is derived and the performance of the proposed algorithm is evaluated for the noisy link environments and compared to the existing algorithms. Extensive experiments show the efficacy of the proposed approach compared to the contemporary methods. The proposed algorithm is found to be robust against the link failure and local node divergence problems. The improved performance of the proposed RILMS algorithm comes with a significant reduction in computational complexity compared to the convex combination based ILMS (CILMS) approach.

Published
2022
Pages
75135-75143
Journal
IEEE Access, vol. 10, no. 10, ISSN 2169-3536
Publisher
Institute of Electrical and Electronics Engineers
DOI
UT WoS
000829198000001
EID Scopus
BibTeX
@ARTICLE{FITPUB12820,
   author = "Uddin Safi Syed Qadri and Muhammad Arif and Imran Naseem and Muhammad Moinuddin",
   title = "Robust Incremental Least Mean Square Algorithm With Dynamic Combiner",
   pages = "75135--75143",
   journal = "IEEE Access",
   volume = 10,
   number = 10,
   year = 2022,
   ISSN = "2169-3536",
   doi = "10.1109/ACCESS.2022.3192018",
   language = "english",
   url = "https://www.fit.vut.cz/research/publication/12820"
}
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