A Study on MIMO Channel Estimation by 2D and 3D Convolutional Neural Networks

Ben Marinberg, Ariel Cohen, Eilam Ben-Dror, Haim H. Permuter

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

In this paper we study the usage of Convolutional Neural Network (CNN) estimators for the task of Multiple-Input-Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) Channel Estimation (CE). Specifically, the CNN estimators interpolate the channel values of reference signals for estimating the channel of the full OFDM resource element (RE) matrix. We have designed a 2D CNN architecture based on U-net, and a 3D CNN architecture for handling spatial correlation. We investigate the performance of various CNN architectures for a diverse data set generated according to 5G NR standard, and in particular we investigate the influence of spatial correlation, Doppler and reference signal resource allocation. The CE CNN estimators are then integrated with MIMO detection algorithms for testing their influence on the system level Bit Error Rate (BER) performance.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2020
PublisherIEEE Computer Society
ISBN (Electronic)9781728192901
DOIs
StatePublished - 14 Dec 2020
Event2020 IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2020 - New Delhi, India
Duration: 14 Dec 202017 Dec 2020

Publication series

NameInternational Symposium on Advanced Networks and Telecommunication Systems, ANTS
Volume2020-December
ISSN (Print)2153-1684

Conference

Conference2020 IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2020
Country/TerritoryIndia
CityNew Delhi
Period14/12/2017/12/20

Keywords

  • 2D CNN
  • 3D CNN
  • Channel estimation
  • Deep learning
  • MIMO detection
  • Reference signal

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Communication

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