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.

本文提出了一种基于机器学习的物理层接收器解决方案，用于解调受到高非线性失真影响的OFDM信号。该方案采用卷积神经网络接收器，在时域和频域中都包含了层，可以可靠地解调和译码传输的比特，特别是当转移信号的误差向量幅度为高时，可以显著优于传统的线性接收器和现有的机器学习接收器方法。因此，建议采用该机器学习接收器来推动终端功率放大器系统进入饱和状态，从而提高终端功率效率、辐射功率和网络覆盖范围。

高EVM信号的深度学习接收器——HybridDeepRx