Digital predistortion method combining memory polynomial and feed-forward neural network

A baseband digital predistortion (DPD) technique based on a feed-forward neural network (FFNN) is presented. The process of memory polynomial (MP) DPD is time consuming because of the large number of mathematical calculations. The FFNN is adopted to realise the mathematical calculations in MP DPD with direct learning architecture (DLA). The training samples of the FFNN are derived from MP DPD with DLA. It guarantees the accuracy of imitating the MP DPD. Although the training of the FFNN is time consuming, the trained FFNN DPD is less time consuming than MP DPD. This solution is validated based on a power amplifier (PA) ZFL-2500 driven by a wideband code division multiple access (WCDMA) signal with 3.84 MHz bandwidth. The experimental results show that the FFNN can mimic the behaviour of the MP DPD. The proposed DPD achieves a significant improvement in linearity and is stable.