NDLI: Derivation and analysis of incremental augmented complex least mean square algorithm

Content Provider	IET Digital Library
Author	Khalili, Azam Rastegarnia, Amir Bazzi, Wael M. Yang, Zhi
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.
Starting Page	312
Ending Page	319
Page Count	8
ISSN	17519675
Volume Number	9
e-ISSN	17519683
Issue Number	Issue 4, Jun (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/9/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2014.0188
Journal	IET Signal Processing
Publisher Date	2015-05-26
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Estimation Adaptive Estimation Algorithm IAC-LMS Algorithm In-network Processing Incremental Augmented Complex Least Mean Square Algorithm Incremental Cooperation Mode Interpolation And Function Approximation Least Mean Squares Method LMS Learning Rules Mean-square Stability Numerical Analysis Signal Processing Signal Processing And Detection Signal Processing Theory Statistics
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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