广义记忆型神经网络射频功放数字预失真器
Digital Predistorters Based on Generalized Memory Neural Networks for RF Power Amplifiers
  
DOI:
中文关键词:  广义记忆型神经网络,射频功放,数字预失真器,非线性模型
英文关键词:generalized memory neural network, radio frequency power amplifier, digital predistorter, nonlinear model
基金项目:国家自然科学基金项目(61571251,61501272);浙江省公益技术应用研究项目(2015C34004,2016C34003)
作者单位
尹思源1刘太君1叶焱1许高明1杨东旭2 1. 宁波大学信息科学与工程学院,宁波315211
2. 浙江旅游职业技术学院,杭州310013 
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中文摘要:
      提出了一种基于广义记忆型神经网络(GMNN)的数字预失真器非线性模型,以更好地抑制由于射频功放动态非线性导致的带内失真以及带外频谱扩展等问题。通过引入时间上的超前项,使得功放模型的记忆效应建模能力得以扩展,通过添加高阶非线性级数,使得功放非线性建模精度进一步提高。文中使用带宽为20 MHz 的4载波WCDMA 信号作为测试信号,对一个中心频率为460 MHz 的60W Doherty 射频功放进行数字预失真线性化实验。实验结果表明,广义记忆型神经网络数字预失真器的带外抑制可达19 dB,能更有效地抑制射频功放的带外频谱扩展,相比于其他几种预失真器展现出更好的线性化效果,验证了广义记忆型神经网络数字预失真器的有效性。
英文摘要:
      This paper proposes a digital predistorter nonlinear model based on a generalized memory neural network (GMNN) , so as to suppress in band distortion and out of band spectrum expansion caused by the dynamic nonlinearity of a radio frequency power amplifier (RFPA). By adding some leading terms in time domain, the memory effect modeling capabilities of the PA model is extended. By adding high order nonlinear series, the accuracy of the power amplifier nonlinear model is further improved. A 4 carrier Wideband Code Division Multiple Access (WCDMA) signal with 20 MHz bandwidth is applied to a 460MHz 60W Doherty RFPA for experimental validation of the digital predistorter. The experimental results illustrate that the out of band suppression of the GMNN predistorter can be up to 19dB. So the predistorter can more effectively suppress the out of band spectral spread of an RFPA and has better linearization performance than other digital predistorters.The experimental results verify the effectiveness of the GMNN digital predistorters.
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