Title :
Non-Data-Aided Parametric- and Nonparametric-Based Carrier Frequency Estimators for Burst GMSK Communication Systems
Author :
Magaña, Mario E. ; Kandukuri, Ajay
Author_Institution :
Sch. of Electr. Eng. & Comput. Sci., Oregon State Univ., Corvallis, OR, USA
fDate :
7/1/2010 12:00:00 AM
Abstract :
In this paper, we propose non-data-aided (NDA) parametric- and nonparametric-based methods for carrier frequency estimation of burst Gaussian minimum-shift keying (GMSK), which have improved performance over ad hoc methods such as delay and multiply and have higher resolution capability. Specifically, three methods are developed for burst GMSK data to improve carrier estimation performance, and their results are compared with the standard delay-and-multiply method. Two of them are parametric-based estimators, and one is a fast nonparametric-based estimator. Parametric-based estimators were studied in detail in this paper due to their high-resolution capabilities and proven performance. However, their computational complexities were found to be relatively high in comparison to nonparametric-based estimators such as the autocorrelation method. The tradeoffs involved with respect to computational load and performance are presented.
Keywords :
Gaussian processes; computational complexity; frequency estimation; minimum shift keying; autocorrelation method; burst GMSK communication systems; burst Gaussian minimum-shift keying; computational complexities; delay-and-multiply method; nondata-aided parametric; nonparametric-based carrier frequency estimators; Autocovariance method; Gaussian minimum-shift keying (GMSK) modulation; Tufts–Kumaresan (TK) method; carrier offset estimation; multiple-signal classification (MUSIC) method; non-data-aided (NDA) estimation; nonparametric estimation; parametric estimation;
Journal_Title :
Instrumentation and Measurement, IEEE Transactions on
DOI :
10.1109/TIM.2009.2030867