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


Yücek T., ARSLAN H.

2004 IEEE Wireless Communications and Networking Conference, WCNC 2004, Atlanta, GA, United States Of America, 21 - 25 March 2004, vol.2, pp.739-744 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 2
  • Doi Number: 10.1109/wcnc.2004.1311278
  • City: Atlanta, GA
  • Country: United States Of America
  • Page Numbers: pp.739-744
  • Istanbul Medipol University Affiliated: Yes

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 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.