DOI码：10.1109/TMTT.2023.3337939

所属单位：中国科学技术大学

教研室：信息科学技术学院电子工程与信息科学系

发表刊物：IEEE Transactions on Microwave Theory and Techniques (Early Access)

项目来源：国家自然科学基金: 61471333, 62371436

关键字：Bandpass feedback power amplifier (PA) behavioral model, behavioral modeling, digital predistortion (DPD), PAs, recurrent neural network (RNN)

摘要：In this article, a novel block-oriented recurrent neural network (RNN) model is proposed for behavioral modeling and digital predistortion (DPD) of radio frequency (RF) power amplifiers (PAs). This article provides an insightful discussion on the importance of input-end parallel finite impulse response (FIR) filters for performance enhancement and finds, for the first time, the unique linearization correction effect of each FIR filter in input-end parallel FIR filters at different frequencies, which is also the reason why block-oriented time-delay NN (BOTDNN) outperforms vector decomposition-based time-delay NN (VDTDNN) in terms of linearization performance. In order to retain the interaction information between nonlinearity and memory effects, the proposed model preserves the feedback path in feedback PA behavioral model. With a view to addressing the challenge of parameter extraction caused by the feedback structure, this article first demonstrates the potential relationship between RNN cells and feedback structures. Subsequently, considering the trade-off between complexity and performance, the Just Another NETwork (JANET) cell is chosen to construct the feedback structure to form the proposed block-oriented JANET (BO-JANET) model. The BO-JANET model is validated using two PAs with the center frequencies of 2.4 and 3.55 GHz, respectively. Experimental results demonstrate that the proposed model achieves further linearization performance improvements compared with other advanced models.

合写作者：Chengye Jiang,Guichen Yang,Renlong Han

第一作者：Qianqian Zhang (张牵牵)

论文类型：期刊论文

通讯作者：Falin Liu

论文编号：10.1109/TMTT.2023.3337939

卷号：online

期号：online

页面范围：1-11

是否译文：否

发表时间：2023-12-13

收录刊物：SCI