Analysis of the Signed Regressor LMMN Algorithm for complex-valued data

Mohammed Mujahid Ulla Faiz; Azzedine Zerguine

doi:10.1109/SSD49366.2020.9364145

Analysis of the Signed Regressor LMMN Algorithm for complex-valued data

Mohammed Mujahid Ulla Faiz, Azzedine Zerguine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This work reports expressions for different parameters constituting the main support for convergence of the signed regressor least mean mixed-norm (LMMN) algorithm for complex-valued data. The steady-state mean-square error, the optimum step-size, and the corresponding minimum value of the tracking mean-square error are all derived. Simulation results are conducted to corroborate the theoretical findings. Also, the convergence bahaviour of the signed regressor LMMN algorithm and that of the LMMN algorithm are compared.

Original language	English
Title of host publication	Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1017-1020
Number of pages	4
ISBN (Electronic)	9781728110806
DOIs	https://doi.org/10.1109/SSD49366.2020.9364145
State	Published - 20 Jul 2020

Publication series

Name	Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

Signed regressor LMS
signed regressor LMF
signed regressor LMMN

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Signal Processing
Electrical and Electronic Engineering
Instrumentation

Access to Document

10.1109/SSD49366.2020.9364145

Cite this

Faiz, M. M. U., & Zerguine, A. (2020). Analysis of the Signed Regressor LMMN Algorithm for complex-valued data. In Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020 (pp. 1017-1020). Article 9364145 (Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSD49366.2020.9364145

Faiz, Mohammed Mujahid Ulla ; Zerguine, Azzedine. / Analysis of the Signed Regressor LMMN Algorithm for complex-valued data. Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 1017-1020 (Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020).

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abstract = "This work reports expressions for different parameters constituting the main support for convergence of the signed regressor least mean mixed-norm (LMMN) algorithm for complex-valued data. The steady-state mean-square error, the optimum step-size, and the corresponding minimum value of the tracking mean-square error are all derived. Simulation results are conducted to corroborate the theoretical findings. Also, the convergence bahaviour of the signed regressor LMMN algorithm and that of the LMMN algorithm are compared.",

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Faiz, MMU & Zerguine, A 2020, Analysis of the Signed Regressor LMMN Algorithm for complex-valued data. in Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020., 9364145, Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020, Institute of Electrical and Electronics Engineers Inc., pp. 1017-1020. https://doi.org/10.1109/SSD49366.2020.9364145

Analysis of the Signed Regressor LMMN Algorithm for complex-valued data. / Faiz, Mohammed Mujahid Ulla; Zerguine, Azzedine.
Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 1017-1020 9364145 (Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - This work reports expressions for different parameters constituting the main support for convergence of the signed regressor least mean mixed-norm (LMMN) algorithm for complex-valued data. The steady-state mean-square error, the optimum step-size, and the corresponding minimum value of the tracking mean-square error are all derived. Simulation results are conducted to corroborate the theoretical findings. Also, the convergence bahaviour of the signed regressor LMMN algorithm and that of the LMMN algorithm are compared.

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Faiz MMU, Zerguine A. Analysis of the Signed Regressor LMMN Algorithm for complex-valued data. In Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 1017-1020. 9364145. (Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020). doi: 10.1109/SSD49366.2020.9364145

Analysis of the Signed Regressor LMMN Algorithm for complex-valued data

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

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Cite this