功率放大器(PA)是无线通信中的重要的非线性器件。PA的记忆非线性恶化了通信系统的传输性能。采用预失真技术可以降低PA的记忆非线性带来的系统性能损失。本文对PA记忆非线性自适应预失真技术进行研究，建立基于全内核Volterra级数的自适应预失真模型，借助LS、RLS、LMS和Kalman滤波算法对全内核Volterra预失真模型的内核进行参数辨识，并以最小均方误差(MSE)作为指标对四种算法进行深度优化。仿真结果表明，Kalman滤波算法具有最优的参数辨识精度，在噪声环境下，基于Kalman滤波自适应预失真器依然能够有效地补偿PA的记忆非线性，本文的研究为进一步理解和研究无线通信系统的自适应预失真技术提供参考。
The power amplifier (PA) is one of the most important nonlinear devices in wireless communication field. The memory-nonlinearity of the PA deteriorates the transmission performance of the communication system. The use of pre-distortion technology can reduce the system performance loss caused by the memory-nonlinearity of the PA. In this paper, the adaptive predistortion tech-nique of PA’s memory-nonlinearity is studied and an adaptive predistortion model based on full kernel Volterra series is established. The parameters of the full-core Volterra predistortion model are identified by LS, RLS, LMS and Kalman filtering algorithms. The four algorithms are deeply optimized with the minimum mean square error (MSE) as an indicator. The simulation results show that the Kalman filtering algorithm has the best parameter identification accuracy. In the noise environment, the adaptive predistorter based on Kalman filter can still effectively compensate the memory-nonlinearity of PA. The research in this paper provides a reference for further understanding and research on adaptive predistortion technology of wireless communication systems.
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