Augmented radial basis function neural network predistorter for linearisation of wideband power amplifiers

作者：Hui Ming^*; Liu Taijun; Zhang Meng; Ye Yan; Shen Dongya; Ying Xiangyue

来源：Electronics Letters, 2014, 50(12): 877-878.

DOI：10.1049/el.2014.0667

An augmented radial basis function neural network (ARBFNN) is proposed for modelling and linearising a wideband Doherty power amplifier (DPA) with strong memory effects and static nonlinearity. To evaluate the performance of the ARBFNN, a 51 dBm DPA and a 25 MHz mixed test signal were used in modelling and linearisation measurement. Compared with the memory polynomial (MP) model and the real-valued time-delay neural network (RVTDNN), the ARBFNN is highly effective, leading to 3 and 5 dB improvements in the normalised mean square error. More importantly, the ARBFNN predistorter represents a significant improvement over the RVTDNN and MP in the suppression of the out-of-band spectral regrowth. In addition, the ARBFNN has a similar linearisation capability as the generalised MP model, but has much better numerical stability.

• 出版日期2014-6-5
• 单位云南大学; 南阳师范学院; 宁波大学