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.