Al Najjar Reem, Hammi Oualid
Department of Electrical Engineering, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates.
Sensors (Basel). 2025 Jun 30;25(13):4099. doi: 10.3390/s25134099.
Neural networks are increasingly attractive for digital predistortion applications due to their demonstrated superior performance. This is mainly attributed to their ability to capture the intrinsic traits of nonlinear systems. This paper presents a novel hybrid predistorter labeled as the look-up table assisted bidirectional long-short term memory (BiLSTM) neural network (LUT-A-BiNN) that combines a neural network cascaded with a look-up table in a manner that both sub-models complement each other. The main motivation in using this two-box arrangement is to eliminate the highly nonlinear static distortions of the PA with the look-up table, allowing the neural network to focus on the compensation of the dynamic distortions. The proposed predistorter is experimentally validated using 5G test signals. The results demonstrate the ability of the proposed predistorter to achieve a 5 dB enhancement in the adjacent channel leakage ratio when compared to its single-box counterpart (BiLSTM neural network predistorter) while maintaining the signal-agnostic performance of the BiLSTM predistorter.
由于神经网络在数字预失真应用中表现出卓越的性能，它们越来越具有吸引力。这主要归功于它们捕捉非线性系统内在特性的能力。本文提出了一种新型混合预失真器，称为查找表辅助双向长短期记忆（BiLSTM）神经网络（LUT-A-BiNN），它以一种两个子模型相互补充的方式，将神经网络与查找表级联在一起。采用这种双盒结构的主要动机是利用查找表消除功率放大器的高度非线性静态失真，使神经网络专注于动态失真的补偿。所提出的预失真器通过5G测试信号进行了实验验证。结果表明，与单盒对应物（BiLSTM神经网络预失真器）相比，所提出的预失真器能够在保持BiLSTM预失真器与信号无关性能的同时，使相邻信道泄漏比提高5dB。
