University of Oulu

B. Banerjee, Z. Khan, J. Lehtomäki and M. Juntti, "Deep Learning Based Over-the-Air Channel Estimation Using a ZYNQ SDR Platform," in IEEE Access, doi: 10.1109/ACCESS.2022.3180352

Deep learning based over-the-air channel estimation using a ZYNQ SDR platform

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Author: Banerjee, Bhaskar1; Khan, Zaheer1; Lehtomäki, Janne1;
Organizations: 1Faculty of Information Technology and Electrical Engineering, University of Oulu
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.9 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2022061345982
Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2022-06-13
Description:

Abstract

Deep learning based channel estimation techniques have recently found an overwhelming interest owing to data-driven learning-based adaptability compared to conventional estimation techniques which rely on model-based approach. This paper exploits the availability of low cost software defined radio (SDR) devices to implement and test over-the-air (OTA) deep learning driven channel estimation solutions in realistic settings for 5G and beyond wireless communications. We have designed and implemented a ZYNQ SDR-based deep learning driven channel estimation platform which utilises real-world 5G new radio (NR) signals to develop and test the performance of a deep learning solution for wireless channel estimators. To this end, we have considered the time-frequency response of an OTA communication channel for single-input-single-output (SISO) and single-input-multiple-output (SIMO) networks under various line-of-sight and non-line-of-sight scenarios as a two-dimensional image. We have utilised convolutional deep learning technique for channel estimation. The main purpose is to use OTA samples obtained via the SDR platform to determine the unknown values of the channel response using known values at the pilot locations and evaluate performance compared to conventional technique. Our results show that the performance of the deep learning channel estimator using OTA data is as good as that of conventional methods with the flexibility to adapt based on data analytics of the time-varying nature of the channel.

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Series: IEEE access
ISSN: 2169-3536
ISSN-E: 2169-3536
ISSN-L: 2169-3536
Issue: Online first
DOI: 10.1109/access.2022.3180352
OADOI: https://oadoi.org/10.1109/access.2022.3180352
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: This work was supported in part by the Infotech Oulu through the framework of digital solutions in sensing and interactions, and in part by the Academy of Finland 6Genesis Flagship under Grant 318927.
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
Copyright information: © The Author(s) 2022. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
  https://creativecommons.org/licenses/by/4.0/