  In this paper, we propose a machine learning (ML) based physical layer
receiver solution for demodulating OFDM signals that are subject to a high
level of nonlinear distortion. Specifically, a novel deep learning based
convolutional neural network receiver is devised, containing layers in both
time- and frequency domains, allowing to demodulate and decode the transmitted
bits reliably despite the high error vector magnitude (EVM) in the transmit
signal. Extensive set of numerical results is provided, in the context of 5G NR
uplink incorporating also measured terminal power amplifier characteristics.
The obtained results show that the proposed receiver system is able to clearly
outperform classical linear receivers as well as existing ML receiver
approaches, especially when the EVM is high in comparison with modulation
order. The proposed ML receiver can thus facilitate pushing the terminal power
amplifier (PA) systems deeper into saturation, and thereon improve the terminal
power-efficiency, radiated power and network coverage.
