Maximum likelihood detection with self organized clustering for TWTA nonlinear distortion of OFDM signal

Summary form only given. In this paper, a new technique of signal detection has been proposed for detecting the OFDM signal in the presence of nonlinear distortion. One of the existing problems with OFDM systems is the nonlinear distortion generated by the high-power-amplifier at the transmitter end due to the large dynamic range of the signal. The proposed method is maximum likelihood detection with symbol estimation. When the training data are available, the neural network has been used to learn the characteristics of the received signal and to estimate the new positions of the transmitted symbol which are provided to the maximum likelihood detector. The estimated symbols are obtained with a self-organized clustering network to which the received signal and the priori training data at the receiver are provided. The nonlinear distortions of a traveling wave tube amplifier, with OFDM signal, are considered in this paper. Simulation results of the bit-error-rate performance are obtained with QPSK and 16-QAM OFDM communication systems.