Behavioral Modeling and Linearization of Wideband RF Power Amplifiers Using BiLSTM Networks for 5G Wireless Systems
Behavioral Modeling and Linearization of Wideband RF Power Amplifiers Using BiLSTM Networks for 5G Wireless Systems
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DOI：
10.1109/tvt.2019.2925562
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发表时间：
2019-06
影响因子：
6.8
通讯作者：
Jinlong Sun;Wenjuan Shi;Zhutian Yang;Jie Yang;Guan Gui
中科院分区：
文献类型：
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作者：
Jinlong Sun;Wenjuan Shi;Zhutian Yang;Jie Yang;Guan Gui
射频功率放大器（pa）的特性和线性化是第五代无线通信系统的关键问题，特别是在引入高峰均比波形时。近年来，深度学习方法在包括无线物理层在内的许多领域取得了巨大的成功。然而，在使用深度学习进行PAs行为建模和线性化方面的工作有限。在本文中，我们在非线性PAs的记忆效应和双向长短期记忆（BiLSTM）神经网络的记忆之间架起了一座桥梁。然后，我们通过协调非因果关系问题，构建了基于bilstm的行为建模体系结构及其伴随的数字预失真（DPD）模型。其次，本文提出了一个额外的模型，以减轻被测PA在转换阶段时的不确定性。实验结果证明了该方案的有效性，其中经过充分训练的网络能够表征PA，并且在考虑被测PA固有不可预测性时，基于人工智能的DPD具有良好的线性化性能。
Characterization and linearization of RF power amplifiers (PAs) are key issues of fifth-generation wireless communication systems, especially when high peak-to-average ratio waveforms are introduced. Recently, deep learning methods have achieved great success in numerous domains including wireless physical-layer. However, there has been limited work in using deep learning for PAs behavioral modeling and linearization. In this paper, we make a bridge between memory effects of the nonlinear PAs and memory of bidirectional long short-term memory (BiLSTM) neural networks. We then build a BiLSTM-based behavioral modeling architecture and its accompanying digital predistortion (DPD) model by reconciling a non causality concern. Next, an additional model is proposed in this paper to mitigate uncertainty of the tested PA when transforming phases. The experimental results demonstrate the effectiveness of the proposed scheme, in which the adequately trained networks are capable of characterizing the PA, and the artificial intelligence-based DPD shows promising linearization performance when considering the tested PAs inherent unpredictability.