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Amir Rastegarnia

Amir Rastegarnia

Academic rank: Associate Professor
ORCID:
Education: PhD.
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HIndex:
Faculty: Technical Engineering
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Research

Title
Steady-state analysis of quantized distributed incremental LMS algorithm without Gaussian restriction
Type
JournalPaper
Keywords
Adaptive networks، Distributed estimation، Energy conservation، DILMS algorithm، Quantization
Year
2013
Journal Signal Image and Video Processing
DOI
Researchers Amir Rastegarnia ، Azam Khalili

Abstract

In this paper, we analyze the steady-state performance of the distributed incremental least mean-square (DILMS) algorithm when it is implemented in finite-precision arithmetic. Our analysis in this paper does not consider any distribution of input data. We first formulate the update equation for quantized DILMS algorithm, and then we use a spatial-temporal energy conservation argument to derive theoretical expressions that evaluate the steady-state performance of individual nodes in the network. We consider mean-square error, excess mean-square error, and mean-square deviation as the performance criteria. Simulation results are generated by using two types of signals, Gaussian and non-Gaussian distributed signals. As the simulation results show, there is a good match between the theory and simulation