东南大学学报（英文版）2018，Vol.34Issue(2)：139-146,8.DOI:10.3969/j.issn.1003-7985.2018.02.001
基于实复值混合时延神经网络的宽带功放的建模和线性化
Modeling and linearizing broad-band power amplifier based on real and complex-valued hybrid time-delay neural network
摘要
Abstract
A new real and complex-valued hybrid time-delay neural network ( TDNN ) is proposed for modeling and linearizing the broad-band power amplifier ( BPA) . The neural network includes the generalized memory effect of input signals, complex-valued input signals and the fractional order of a complex-valued input signal module, and, thus, the modeling accuracy is improved significantly. A comparative study of the normalized mean square error ( NMSE) of the real and complex-valued hybrid TDNN for different spread constants, memory depths, node numbers, and order numbers is studied so as to establish an optimal TDNN as an effective baseband model, suitable for modeling strong nonlinearity of the BPA. A 51-dBm BPA with a 25-MHz bandwidth mixed test signal is used to verify the effectiveness of the proposed model. Compared with the memory polynomial ( MP) model and the real-valued TDNN, the real and complex-valued hybrid TDNN is highly effective, leading to an improvement of 5 dB in the NMSE. In addition, the real and complex-valued hybrid TDNN has an improvement of 0. 6 dB over the generalized MP model in the NMSE. Also, it has better numerical stability. Moreover, the proposed TDNN presents a significant improvement over the real-valued TDNN and the MP models in suppressing out-of-band spectral regrowth.关键词
功放/神经网络/线性化/建模Key words
power amplifier/neural network/linearization/modeling分类
信息技术与安全科学引用本文复制引用
惠明,张新刚,张萌,余超,朱晓维..基于实复值混合时延神经网络的宽带功放的建模和线性化[J].东南大学学报（英文版）,2018,34(2):139-146,8.基金项目
The National Natural Science Foundation of China ( No. 61561052, 61701262) , the Science and Technology Foundation of Henan Province( No. 182102410062, 182102210114) , the Science and Technology Foundation of Henan Educational Committee ( No. 17A510018) . （ No. 61561052, 61701262）
