Abstract: To circumvent the transmission performance degradation of the orthogonal frequency division multiplexing(OFDM) systems due to the nonlinear high power amplifiers(HPA), a new predistorter is presented which consists of two similar single-input and single-output BP neural networks(NN) in series. The former NN is the amplitude predistorter obtained by the improved indirect learning method which overcomes the shortcoming of the indirect method, and its phase predistortion based on the latter NN is implemented by the phase characteristic model rather than its inverse model. Simulation results show that the proposed predistorter can make about 15dB reduction of out-of-band spectral regrowth with fewer neurons even at 2.93dB IBO(input back-off) where another available can not work any longer, indicating this adaptive predistorter with a simpler structure outperforms other predistorters for the HPA employed in the OFDM systems.

Key words: orthogonal frequency division multiplexing, high power amplifiers, BP neural network, predistortion