Linearization of Quadrature Digital Power Amplifiers by Neural Network of ULR_LSTM: Unsupervised Learning Residual LSTM

作者：Jiayu Yang, Luyi Guo, Yicheng Li, Wang Wang, Zixu Li, Manni Li, Zijian Huang, Yinyin Lin, Yun Yin, Hongtao Xu · 年份：2025 · DOI：10.23919/date64628.2025.10993212 · 被引用次数：3 · 研究领域：Advanced Power Amplifier Design、Blind Source Separation Techniques、Sensor Technology and Measurement Systems

For the first time, this paper presents an unsupervised learning residual long short-term memory (ULR_LSTM) neural network to develop a digital predistortion (DPD) method for the linearization of digital power amplifiers (DPAs). Our method eliminates the need for iterative learning control (ILC) to obtain the ideal input of the DPA required by state-of-the-arts (SOTAs), which leads to high computational complexity and extensive training time. We perform behavioral modeling of the DPA using the R_LSTM network. After determining the optimal behavioral model architecture, the corresponding DPD model is obtained through an inverse training process. A 15-bit transformer-based quadrature DPA chip incorporating Class-G and IQ-cell-sharing techniques was implemented in a 28nm CMOS process to validate our proposed method. Experimental results demonstrate outstanding linearization performance comparing to prior arts, achieving an error vector magnitude (EVM) of -40.4dB for the 802.11ax 40MHz 64QAM signal.