Title
Maximum likelihood scale parameter estimation: An application to gain estimation for QAM constellations
Abstract
In this paper we address the problem of scale parameter estimation, introducing a reduced complexity Maximum Likelihood (ML) estimation procedure. The estimator stems from the observation that, when the estimandum acts as a shift parameter on a multinomially distributed statistic, direct maximization of the likelihood function can be conducted by an efficient DFT based procedure. A suitable exponential warping of the observation's domain is known to transform a scale parameter problem into a shift estimation problem, thus allowing the afore mentioned reduced complexity ML estimation for shift parameter to be applied also in scale parameter estimation problems. As a case study, we analyze a gain estimator for general QAM constellations. Simulation results and theoretical performance analysis show that the herein presented estimator outperforms selected state of the art high order moments estimator, approaching the Cramér-Rao Lower Bound (CRLB) for a wide range of SNR.
Year
Venue
Keywords
2010
Aalborg
discrete fourier transforms,maximum likelihood estimation,quadrature amplitude modulation,crlb,cramér-rao lower bound,dft based procedure efficiency,ml estimation procedure,qam constellations,snr,complexity reduction,direct maximization,estimandum,exponential warping,gain estimation,high order moment estimator,likelihood function,maximum likelihood estimation procedure,multinomially distributed statistic,scale parameter estimation,shift parameter estimation problem,signal to noise ratio
Field
DocType
ISSN
Cramér–Rao bound,Mathematical optimization,Likelihood function,Upper and lower bounds,Minimum chi-square estimation,Algorithm,Estimation theory,Maximum likelihood sequence estimation,Mathematics,Scale parameter,Estimator
Conference
2219-5491
Citations 
PageRank 
References 
0
0.34
7
Authors
3
Name
Order
Citations
PageRank
Stefania Colonnese113726.43
Stefano Rinauro2508.72
Gaetano Scarano320931.32