HybridDeepRx: Deep Learning Receiver for High-EVM Signals

TL;DR

This paper introduces a deep learning-based receiver for OFDM signals with high EVM, outperforming classical and existing ML methods, enabling more efficient power amplifier operation in 5G systems.

Contribution

A novel convolutional neural network receiver that processes both time and frequency domain data to reliably demodulate high-EVM signals in 5G uplink.

Findings

Outperforms classical linear receivers in high-EVM scenarios

Outperforms existing ML receivers in high-EVM scenarios

Enables deeper saturation of power amplifiers for better efficiency

Abstract

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.