Neural-Network-Based Predistortion Method for High-Power Amplifiers With Memory

摘要：

This paper presents a novel predistorter architecture based on Generalized Radial Basis Function (GRBF) neural network for high-power amplifier (HPA) with memory in an orthogonal frequency division multiplexing (OFDM) system. The predistorter is implemented using an indirect learning architecture. An efficient algorithm to update the neural network weight matrices is derived. Simulation results show that the proposed neural network predistorter can effectively reduce the nonlinear distortion of HPA and produce a faster convergence speed than the conventional backpropagation algorithm.

关键词： High power amplifier nonlinear distortion OFDM predistortion neural network.

作者: Jiantao Yang Jun Gao Shuhong Guo Xiaotao Deng

作者单位: Dept of Communication Engineering,Naval University of Engineering,China

会议类型: 国际会议

会议名称: The IET 2nd International Conference on Wireless,Mobile & Multimedia Networks(第二届IET国际无线移动多媒体网络会议)

会议地点: 北京

会议语种:英文

在线出版日期: 2008-10-12（万方平台首次上网日期，不代表论文的发表时间）