Deep Learning OFDM Receivers for Improved Power Efficiency and Coverage

Jaakko Pihlajasalo, Dani Korpi, Mikko Honkala, Janne M.J. Huttunen, Taneli Riihonen, Jukka Talvitie, Alberto Brihuega, Mikko A. Uusitalo, Mikko Valkama

Research output: Contribution to journalArticleScientificpeer-review

2 Downloads (Pure)

Abstract

In this article, we propose multiple machine learning (ML) based physical-layer receiver solutions for demodulating orthogonal frequency-division multiplexing (OFDM) signals that are subject to high level of nonlinear distortion. Specifically, three novel deep learning based convolutional neural network receivers are devised, containing layers in time- and/or frequency-domains, allowing to demodulate and decode the transmitted bits reliably despite the high error vector magnitude (EVM) in the transmit signal. Applicable training procedures are also described, such that the learned layers in the receiver processing properly generalize over different nonlinear distortion and multipath channel characteristics. Extensive set of numerical results is provided, in the context of 5G NR uplink (UL) incorporating also measured terminal power amplifier (PA) characteristics. The obtained results show that the proposed receiver systems are able to clearly outperform the classical linear minimum mean-squared error (LMMSE) receiver as well as the existing ML receiver approaches, especially when the EVM is high compared to modulation order. This is particularly so when the devised ML receiver is of hybrid nature with layers both in time and frequency. The proposed ML receivers can thus facilitate pushing the terminal PA systems deeper into saturation, and thereon improve the terminal power-efficiency, radiated power and network coverage. Through combining the obtained radio link performance results with link budget calculations, all carried out at the 28 GHz mmWave band, it is shown that the proposed ML receivers can enhance the network coverage in terms of maximum UL link distances by close to 100%, when compared to classical LMMSE receiver based networks.

Original languageEnglish
Pages (from-to)5518-5535
JournalIEEE Transactions on Wireless Communications
Volume22
Issue number8
DOIs
Publication statusPublished - Aug 2023
Publication typeA1 Journal article-refereed

Keywords

  • 5G NR
  • 6G
  • coverage
  • deep learning
  • digital post-distortion
  • EVM
  • LMMSE
  • machine learning
  • mmWave
  • nonlinear distortion
  • OFDM
  • power amplifier
  • power-efficiency
  • receiver

Publication forum classification

  • Publication forum level 3

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

Fingerprint

Dive into the research topics of 'Deep Learning OFDM Receivers for Improved Power Efficiency and Coverage'. Together they form a unique fingerprint.

Cite this