Derivation and analysis of incremental augmented complex least mean square algorithm

Azam Khalili, Amir Rastegarnia, Wael M. Bazzi, Zhi Yang

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

In this paper the authors propose an adaptive estimation algorithm for in-network processing of complex signals over distributed networks. In the proposed algorithm, as the incremental augmented complex least mean square (IAC-LMS) algorithm, nodes of the network are allowed to collaborate via incremental cooperation mode to exploit the spatial dimension; while at the same time are equipped with LMS learning rules to endow the network with adaptation. The authors have extracted closed-form expressions that show how IAC-LMS algorithm performs in the steady-state. The authors further have derived the required conditions for mean and mean-square stability of the proposed algorithm. The authors use both synthetic benchmarks and real world non-circular data to evaluate the performance of the proposed algorithm. Simulation results also reveal that the IAC-LMS algorithm is able to estimate both second order circular (proper) and non-circular (improper) signals. Moreover, IAC-LMS algorithm outperforms the non-cooperative solution.

Original languageEnglish (US)
Pages (from-to)312-319
Number of pages8
JournalIET Signal Processing
Volume9
Issue number4
DOIs
StatePublished - Jan 1 2015

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