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In this paper, we propose a machine learning (ML) based physical layerreceiver solution for demodulating OFDM signals that are subject to a highlevel of nonlinear distortion. Specifically, a novel deep learning basedconvolutional neural network receiver is devised, containing layers in bothtime- and frequency domains, allowing to demodulate and decode the transmittedbits reliably despite the high error vector magnitude (EVM) in the transmitsignal. Extensive set of numerical results is provided, in the context of 5G NRuplink incorporating also measured terminal power amplifier characteristics.The obtained results show that the proposed receiver system is able to clearlyoutperform classical linear receivers as well as existing ML receiverapproaches, especially when the EVM is high in comparison with modulationorder. The proposed ML receiver can thus facilitate pushing the terminal poweramplifier (PA) systems deeper into saturation, and thereon improve the terminalpower-efficiency, radiated power and network coverage.

HybridDeepRx: Deep Learning Receiver for High-EVM Signals