ML Detection with Symbol Estimation for Nonlinear Distortion of OFDM Signals

In this paper, a new technique of signal detection has been proposed for detecting the orthogonal frequencydivision multiplexing (OFDM) signals in the presence of nonlinear distortion. There are several advantages of OFDM communication systems. However, one of the existing problems is considered as the nonlinear distortion generated by high power amplifier at the transmitter end due to the large dynamic range of an OFDM signal. The proposed method is the maximum likelihood detection with the symbol estimation. During the training phase, the neural network has been used to learn the characteristic of received signals and to estimate the transmitted symbols, which are provided to the maximum likelihood detector. Resulting in the system performance, the nonlinear distortion of a traveling wave tube amplifier with OFDM signal is considered in this paper. Simulation results of the bit-error-rate performance are obtained.