随着OFDM、WCDMA等高峰均比调制方式在卫星通信中的应用,以及卫星通信的传输速度高速化,传统的功率放大器预失真技术已经不能适应下一代卫星通信.针对上述问题,提出了一种动态神经网络预失真方法.该方法通过对输入信号进行幅相分离,分别利用动态神经网络进行预失真处理,并给出了所设计预失真方法的数学推导.仿真结果表明,该方法能够有效减小计算量并提高预失真的性能,从而可以较好地消除功率放大器的非线性特性和记忆效应,比功率放大预失真方法更具有实用价值.%With OFDM, WCDMA and other high peak-to-average power ratio (PAPR) modulation technique applied in satellite communications, and the ever-incensing transmission speed of satellite communication, traditional power amplifier pre-distortion technology has revealed inefficiency to meet the requirement of satellite communication. Aiming at this problem, we present a dynamic neural network predistortion method. The amplitude and phase of the input signal are separated ahead, while the proposed dynamic neural network based predistortion is processed on this basis respectively. The computational complexity reduction and the performance pre-distortion improvement are proved through mathematical derivation. Simulation results and their analysis verify preliminarily that the proposed method can be more effective in suppressing the power amplifier's nonlinear characteristics and the memory effect; compared with third-order Volterra, it is more suitable for practical applications.
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