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

• Published in: International 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
• Language: English
• 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
• ISSN: 2154-0225
• DOI: 10.1109/ISWCS61526.2024.10639168

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.