Low-Complexity Digital Predistortion of RF Power Amplifiers Based on FastGRNN

被引：1

作者：

Watanabe, Taishi ^{[1
]}

Ohseki, Takeo ^{[1
]}

Kanno, Issei ^{[1
]}

Amano, Yoshiaki ^{[1
]}

机构：

[1] KDDI Res Inc, Wireless Technol Div, Saitama, Japan

来源：

2023 IEEE 98TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2023-FALL | 2023年

关键词：

Deep learning; Digital predistortion; Neural networks; Power amplifier; Machine learning; MODEL;

D O I：

10.1109/VTC2023-Fall60731.2023.10333659

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we propose low-complexity digital predistortion (DPD) schemes based on FastGRNN to compensate for the nonlinearity of RF power amplifiers. Conventionally, high-precision recurrent neural network (RNN) models, such as long short-term memory (LSTM) and gated recurrent unit (GRU), have been used to model the behavior of amplifiers, and their excellent compensation performance has been shown in terms of error vector magnitude (EVM) and adjacent channel power ratio (ACPR) has been demonstrated. However, their complex structures result in high computational complexity. To solve this issue, the proposed method is designed to significantly reduce the complexity without significant performance degradation by appropriately applying the FastGRNN models to the DPD. Complexity analysis and experiments using a power amplifier in the 2.0 GHz frequency band showed that the proposed method achieved comparable EVM performance to LSTM with 29.2% floating point operations (FLOPs) and 27.1% trainable parameters.

引用

页数：5

共 20 条

[1] LEARNING LONG-TERM DEPENDENCIES WITH GRADIENT DESCENT IS DIFFICULT [J].