HybridDeepRx: Deep Learning Receiver for High-EVM Signals
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.
Keywords
Cite
@article{arxiv.2106.16079,
title = {HybridDeepRx: Deep Learning Receiver for High-EVM Signals},
author = {Jaakko Pihlajasalo and Dani Korpi and Mikko Honkala and Janne M. J. Huttunen and Taneli Riihonen and Jukka Talvitie and Alberto Brihuega and Mikko A. Uusitalo and Mikko Valkama},
journal= {arXiv preprint arXiv:2106.16079},
year = {2021}
}
Comments
To be presented in the 2021 IEEE International Symposium on Personal, Indoor and Mobile Radio Communications