LMS Quasi-Newton and RLS Algorithms with Sparsity-Aware Updates applied to Communications

In this paper, constrained optimization methods with a sparsity-aware Newton-type direction update are applied to adaptive filtering. Different versions of the proposed algorithms, which include a sparsity-aware RLS and a CG algorithm, present lower computational complexity than traditional algorith...

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Bibliographic Details
Published inInternational Symposium on Wireless Communication Systems pp. 1 - 6
Main Authors Ferreira, Tadeu N., Lima, Markus V. S., de Campos, Marcello L. R., Diniz, Paulo S. R.
Format Conference Proceeding
LanguageEnglish
Published IEEE 14.07.2024
Subjects
Acoustics
Adaptive filter
Benchmark testing
Echo cancellers
Filtering algorithms
LMS-Newton
Optimization methods
RLS
sparsity
Time-varying channels
Wireless communication
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ISSN2154-0225
DOI10.1109/ISWCS61526.2024.10639168

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Summary:In this paper, constrained optimization methods with a sparsity-aware Newton-type direction update are applied to adaptive filtering. Different versions of the proposed algorithms, which include a sparsity-aware RLS and a CG algorithm, present lower computational complexity than traditional algorithms based on LMS-Newton, since the proposed algorithms require the use of a smaller Hessian estimate. The proposed algorithms are tested in acoustic echo cancellation and time-varying channel identification. Some of them present a computational reduction of 25% of that of the traditional LMS-Newton in the best case scenario. Most of the proposed algorithms present faster convergence than the benchmark algorithms in several scenarios.
ISSN:2154-0225
DOI:10.1109/ISWCS61526.2024.10639168