  Multi-input multi-output orthogonal frequency division multiplexing (MIMO
OFDM) is a key technology for mobile communication systems. However, due to the
issue of high peak-to-average power ratio (PAPR), the OFDM symbols may suffer
from nonlinear distortions of the power amplifier (PA) at the transmitters,
which degrades the channel estimation and detection performances of the
receivers. To mitigate the clipping distortions at the receivers end, we
leverage deep learning (DL) and devise a DL based receiver which is aided by
the traditional least square (LS) channel estimation and the zero-forcing (ZF)
equalization models. Moreover, a data driven DL based receiver without explicit
channel estimation is proposed and combined with the model aided DL based
receiver to further improve the performance. Simulation results showcase that
the proposed model aided DL based receiver has superior performance of bit
error rate and has robustness over different levels of clipping distortions.
