基于BPNN的OFDM系统的HPA预失真
doi: 10.3724/SP.J.1146.2007.01483
A NN Predistortion Method for Nonlinear HPAs in OFDM System
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摘要: 本文针对高功率放大器(HPA)的非线性失真导致OFDM(Orthogonal Frequency Division Multiplexing)系统传输性能下降问题，采用两个类似结构的单输入单输出BP神经网络串联后级联HPA实现其预失真，前一网络是HPA的AM-AM特性的逆模型，用来实现HPA的幅度预失真，后一网络是HPA的AM-PM特性模型，回避了其逆模型的建立，实现了HPA更高精度的相位预失真，提高了整体预失真效果。仿真结果显示了即使输入回退只有2.93dB，带外谱增长仍能降低约10dB，表明该方案能够方便高效地实现OFDM系统中HPA的自适应预失真，大大提高OFDM系统的传输性能。Abstract: To circumvent the transmission performance degradation of Orthogonal Frequency Division Multiplexing (OFDM) systems due to the nonlinear HPA, a new predistorter is presented to predistort HPA which consists of two similar single-input and single-output Neural Networks (NNs) in series. The former NN is the inverse model of HPAs AM-AM characteristics to compensate its amplitude and the latter the model of AM-PM characteristics to compensate its phase. The phase predistortion is implemented just by its model rather its inverse model, which ensures it of much higher precision. Simulation results show that even though input back-off is as low as 2.93dB, the spectral regrowth can be reduced about 10dB. The proposed scheme can adaptively predistort HPA effectively and simply, and improve transmission performance of OFDM system greatly.
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