DocumentCode :
687955
Title :
Asynchronous hybrid maximum likelihood classification of linear modulations
Author :
Ozdemir, Onur ; Varshney, Pramod K. ; Wei Su
Author_Institution :
Boston Fusion Corp., Burlington, MA, USA
fYear :
2013
fDate :
9-13 Dec. 2013
Firstpage :
3235
Lastpage :
3240
Abstract :
In this paper, we consider the problem of linear modulation classification in the presence of unknown time offset, phase offset and received signal amplitude. We develop a novel hybrid maximum likelihood (HML) approach based on a Generalized Expectation Maximization (GEM) algorithm [1]. Our approach is applicable to all QAM and PSK modulations, and it does not require any assumptions on the received signal-to-noise ratio (SNR). The GEM algorithm provides a tractable procedure to obtain maximum likelihood (ML) estimates which are extremely hard to obtain otherwise. Moreover, our approach employs only a small number of samples (in the order of hundreds) to perform both time and phase synchronization, signal power estimation, followed by modulation classification. The proposed approach also enables maximum a posteriori (MAP) decoding of the unknown constellation symbol sequence as a by-product of the GEM algorithm. We provide simulation results that show that the proposed approach provides excellent classification performance.
Keywords :
decoding; expectation-maximisation algorithm; phase shift keying; quadrature amplitude modulation; synchronisation; GEM; MAP; PSK; QAM; SNR; asynchronous hybrid maximum likelihood classification; generalized expectation maximization algorithm; linear modulation classification; linear modulations; maximum a posteriori decoding; phase offset; phase shift keying; phase synchronization; quadrature amplitude modulation; signal power estimation; signal-to-noise ratio; time offset; time synchronization; Maximum likelihood decoding; Maximum likelihood estimation; Quadrature amplitude modulation; Receivers; Signal processing algorithms; Synchronization; Modulation classification; generalized expectation maximization; hybrid maximum likelihood; time offset;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Global Communications Conference (GLOBECOM), 2013 IEEE
Conference_Location :
Atlanta, GA
Type :
conf
DOI :
10.1109/GLOCOM.2013.6831570
Filename :
6831570
Link To Document :
بازگشت