Adaptive predistortion algorithm based on the multilinear multiplier fusion: Adaptive Predistortion Algorithm Based on the Multilinear Multiplier Fusion

Aiming at solving the problem of the insufficiency in the predistortion structure and low accuracy of the memory nonlinear power amplifier in the wireless communication system, an adaptive predistortion method for dual loop learning structure based on the fusion of multilinear multiplier model recognition is put forward. On the basis of the real number delay multilinear multiplier model, an improved quantum behaved optimization (QBO) algorithm is adopted in this method to carry out indirect learning structure off-line training of the multilinear multiplier, so as to determine the model parameters as an initial value of the predistorter. And then, the least square method (LSM) algorithm is used to carry out direct learning structure on-line micro adjustment of the parameters of the predistorter and to conduct fitting on the nonlinearity and memory effect of the power amplifier. The method has the advantages of simple structure, fast convergence, and high precision, which has avoided the local optimum. The experimental results show that the adjacent channel power in this scheme is improved by approximately 7 dB compared with that of the classical dual loop structure predistortion method, and the linearization performance of the amplifier has been significantly improved, thus its feasibility has been verified.