Recurrent neural network technique for behavioral modeling of power amplifier with memory effects. Issue 4 (28th October 2014)

• Record Type: Journal Article
• Authors: Yan, Shuxia; Zhang, Chuan; Zhang, Qi‐Jun
• Is Part Of: International journal of RF and microwave computer-aided engineering. Volume 25:Issue 4(2015)
• Page Start: 289
• Page End: 298
• Publication Date: 2014-10-28
• Subjects: behavioral modeling -- memory effects -- power amplifier -- recurrent neural networks -- simulation; Microwave devices -- Computer-aided design -- Periodicals; Computer-aided engineering -- Periodicals; 621.3813
• DOI: 10.1002/mmce.20861
• Languages: English
• ISSNs: 1096-4290

Abstract

ABSTRACT: A new technique for behavioral modeling of power amplifier (PA) with short‐ and long‐term memory effects is presented here using recurrent neural networks (RNNs). RNN can be trained directly with only the input–output data without having to know the internal details of the circuit. The trained models can reflect the behavior of nonlinear circuits. In our proposed technique, we extract slow‐changing signals from the inputs and outputs of the PA and use these signals as extra inputs of RNN model to effectively represent long‐term memory effects. The methodology using the proposed RNN for modeling short‐term and long‐term memory effects is discussed. Examples of behavioral modeling of PAs with short‐ and long‐term memory using both the existing dynamic neural networks and the proposed RNNs techniques are shown. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:289–298, 2015.