Behavioral modeling of RF power amplifiers with time-delay feed-forward neural networks  

射频功率放大器前馈输入延迟神经网络模型(英文)

作　　者：翟建锋[1] 周健义[1] 赵嘉宁[1] 张雷[1] 洪伟[1] 

机构地区：[1]东南大学毫米波国家重点实验室,南京210096

出　　处：《Journal of Southeast University(English Edition)》2008年第1期6-9,共4页东南大学学报（英文版）

基　　金：The National Natural Science Foundation of China(No.60621002);the National High Technology Research and Development Pro-gram of China(863 Program)(No.2007AA01Z2B4).

摘　　要：A novel behavioral model using three-layer time-delay feed-forward neural networks （TDFFNN）is adopted to model radio frequency （RF）power amplifiers exhibiting memory nonlinearities. In order to extract the parameters, the back- propagation algorithm is applied to train the proposed neural networks. The proposed model is verified by the typical odd- order-only memory polynomial model in simulation, and the performance is compared with different numbers of taped delay lines（TDLs） and perceptrons of the hidden layer. For validating the TDFFNN model by experiments, a digital test bench is set up to collect input and output data of power amplifiers at a 60 × 10^6 sample/s sampling rate. The 3.75 MHz 16-QAM signal generated in the vector signal generator（VSG） is chosen as the input signal, when measuring the dynamic AM/AM and AM/PM characteristics of power amplifiers. By comparisons and analyses, the presented model provides a good performance in convergence, accuracy and efficiency, which is approved by simulation results and experimental results in the time domain and frequency domain.提出一种三层前馈输入延迟神经网络模型用于建立射频功率放大器有记忆的非线性行为模型.反向传播算法用来训练神经网络以提取神经网络模型参数.在仿真中,提出的模型可以通过典型的偶次多项式有记忆功率放大器模型来证明,并且比较了不同数目的延迟单元和隐层神经感知器的模型结构下的性能.为了用实验验证模型的有效性,建立了能达到60×106sample/s采样率数字测试平台用于采集功率放大器的输入和输出数据.选用矢量信号源产生的3·75MHz16-QAM信号作为功放输入信号来测试功放的动态AM/AM和AM/PM特性.通过分析比较时域和频域仿真结果和实验测试结果,模型在收敛性、精度和执行效率方面都达到很好的效果.

关 键 词：behavioral model power amplifier time-delay feed- forward neural network（TDFFNN） 

分 类 号：TN830.6[电子电信—信息与通信工程]