Adaptive Deep Learning Aided Digital Predistorter Considering Dynamic Envelope
preprintposted on 04.02.2020, 20:33 by Jinlong Sun, Juan Wang, Liang Guo, Jie Yang, G G
Memory effects of radio frequency power amplifiers (PAs) can interact with dynamic transmitting signals, dynamic operations, and dynamic environment, resulting in complicated nonlinear problems of the PAs. Recently, deep learning based schemes have been proposed to deal with the memory effects. Although these schemes are powerful in constructing complex nonlinear structures, they are still direct learning-based and are relatively static. In this paper, we propose an adaptive deep learning aided digital predistortion (DL-DPD) model by optimizing a deep regression neural network. Thanks to the sequence structure of the proposed DL-DPD, we then make the linearization architecture more adaptive by using multiple sub-DPD modules and an ensemble predicting process. The results show the effectiveness of the proposed adaptive DL-DPD, and reveals that the online system handovers the sub-DPD modules more frequently than expected.
Email Address of Submitting Authorguiguan@njupt.edu.cn
Submitting Author's InstitutionNanjing University of Posts and Telecommunications
Submitting Author's CountryChina
Read the peer-reviewed publication
in IEEE Transactions on Vehicular Technology