A novel sub-optimum maximum-likelihood modulation classification algorithm for adaptive OFDM systems

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 classifier, 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 cases.