Learning-aided joint time-frequency channel estimation for 5G new radio

Nitin Jonathan Myers, Hyukjoon Kwon, Yacong Ding, Kee-Bong Song

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

In this paper, we propose a learning-aided signal processing solution for channel estimation in 5G new radio (NR). Channel estimation is an important algorithm for baseband modem design. In 5G NR, estimating the channel is challenging due to two reasons. First, the pilot signals are transmitted over a small fraction of the available time-frequency resources. Second, the real time nature of physical layer processing introduces a strict limitation on the computational complexity of channel estimation. To this end, we propose a channel estimation technique that integrates a small one hidden layer neural network between two linear minimum mean squared error (LMMSE) interpolation blocks. While the neural network leverages the advantages of offline data-driven learning, the LMMSE blocks exploit the second order online channel statistics along time and frequency dimensions. The training procedure tunes the weights of the neural network by back-propagating through the time domain LMMSE interpolation block. We derive bounds on the training loss with the proposed method and show that our approach can improve the channel estimate.
Original languageEnglish
Title of host publicationProceedings of the IEEE Global Communications Conference (GLOBECOM 2021)
PublisherIEEE
Number of pages6
ISBN (Electronic)978-1-7281-8104-2
ISBN (Print)978-1-7281-8105-9
DOIs
Publication statusPublished - 2021
Event 2021 IEEE Global Communications Conference (GLOBECOM) - Madrid, Spain
Duration: 7 Dec 202111 Dec 2021

Conference

Conference 2021 IEEE Global Communications Conference (GLOBECOM)
Country/TerritorySpain
CityMadrid
Period7/12/2111/12/21

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • 5G NR
  • deep learning
  • channel estimation

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