Variants of partial update augmented CLMS algorithm and their performance analysis

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Vahidpour, V., Rastegarnia, A., Khalili, A., Bazzi, W.M. and Sanei, S. ORCID: 0000-0002-3437-2801, 2020. Variants of partial update augmented CLMS algorithm and their performance analysis. IEEE Transactions on Signal Processing, 68, pp. 3146-3157. ISSN 1053-587X

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Official URL: https://doi.org/10.1109/tsp.2020.2993938

Abstract

Naturally complex-valued information or those presented in complex domain are effectively processed by an augmented complex least-mean-square (ACLMS) algorithm. In some applications, the ACLMS algorithm may be too computationally and memory-intensive to implement. In this paper, a new algorithm, termed partial-update ACLMS (PU-ACLMS) algorithm is proposed, where only a fraction of the coefficient set is selected to update at each iteration. Doing so, two types of partial update schemes are presented referred to as the sequential and stochastic partial-updates, to reduce computational load and power consumption in the corresponding adaptive filter. The computational cost for full-update PU-ACLMS and its partial update implementations are discussed. Next, the steady-state mean and mean-square performance of PU-ACLMS for noncircular complex signals are analyzed and closed-form expressions of the steady-state excess mean-square error (EMSE) and mean-square deviation (MSD) are given. Then, employing the weighted energy-conservation relation, the EMSE and MSD learning curves are derived. The simulation results are verified and compared with those of theoretical predictions through numerical examples.

Item Type:

Journal article

Publication Title:

IEEE Transactions on Signal Processing

Creators:

Vahidpour, V., Rastegarnia, A., Khalili, A., Bazzi, W.M. and Sanei, S.

Publisher:

Institute of Electrical and Electronics Engineers

Date:

14 May 2020

Volume:

ISSN: