Maximum likelihood estimate of parameters of Nakagami-m distribution

Rangeet Mitra*, Amit Kumar Mishra, Tarun Choubisa

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

Abstract

Nakagami-m distribution is well known for its ability to model a number of probability density functions, be it symmetric or asymmetric. Many Maximum Likelihood parameter estimation techniques for this distribution have been proposed that use estimated higher order moments of the data. However, the required large amount of data may not always be available. This is a drawback of using moments based approaches. In this work we propose a Maximum Likelihood parameter estimation technique for Nakagami-m distribution by giving a closed form expression for it. We demonstrate the performance of the proposed approach using certain test cases and compare the same to conventional algorithms using moments. We show that the new algorithm can model those pdfs better which may be deviating slightly/morderately from Gaussian shape and hence alleviating the need for extra mixture components.

Original languageEnglish
Title of host publicationProceedings of the 2012 International Conference on Communications, Devices and Intelligent Systems, CODIS 2012
PublisherIEEE Press
Pages9-12
Number of pages4
ISBN (Electronic)978-1-4673-4700-6
ISBN (Print)978-1-4673-4699-3
DOIs
Publication statusPublished - 28 Dec 2012
Externally publishedYes
Event2012 International Conference on Communications, Devices and Intelligent Systems, CODIS 2012 - Kolkata, India
Duration: 28 Dec 201229 Dec 2012

Publication series

NameProceedings of the 2012 International Conference on Communications, Devices and Intelligent Systems, CODIS 2012

Conference

Conference2012 International Conference on Communications, Devices and Intelligent Systems, CODIS 2012
Country/TerritoryIndia
CityKolkata
Period28 Dec 201229 Dec 2012

Keywords

  • Maximum-Likelihood
  • Nakagami-m distribution

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