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A Novel Sub-optimum Maximum-Likelihood Modulation Classification Algorithm for Adaptive
 

Summary: A Novel Sub-optimum Maximum-Likelihood
Modulation Classification Algorithm for Adaptive
OFDM Systems
Tevfik Y¨ucek and H¨useyin Arslan
Department of Electrical Engineering, University of South Florida
4202 E. Fowler Avenue, ENB-118, Tampa, FL, 33620
Phone : (813) 974 0759 and (813) 974 3940
E-mail : yucek@eng.usf.edu and arslan@eng.usf.edu
Abstract-- Adaptive modulation is an effective method to
increase the spectral efficiency of OFDM based high-speed
wireless data transmission systems in time-dispersive (frequency-
selective) channels. Blind modulation classification schemes play
an important role in adaptive modulation systems to eliminate
the need for transmitting the modulation information, thereby
increasing spectral efficiency. In this paper, a maximum-likelihood
(ML) modulation classifier which has the optimum performance
in the presence of white noise is presented. A sub-optimum clas-
sifier, which greatly reduces the complexity, is derived from the
optimum ML classifier. The performances of proposed classifiers
are tested using Monte-Carlo simulations for ideal and non-ideal

  

Source: Arslan, Hüseyin - Department of Electrical Engineering, University of South Florida

 

Collections: Engineering