Blind performance prediction for deep learning based ultra-massive MIMO channel estimation

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

2 Scopus Citations
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Author(s)

  • Wentao Yu
  • Hengtao He
  • Shenghui Song
  • Jun Zhang
  • Khaled B. Letaief

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationICC 2023 - IEEE International Conference on Communications
EditorsMichele Zorzi, Meixia Tao, Walid Saad
PublisherInstitute of Electrical and Electronics Engineers, Inc.
Pages2613-2618
ISBN (electronic)978-1-5386-7462-8
ISBN (print)978-1-5386-7463-5
Publication statusPublished - 2023

Publication series

Name
ISSN (electronic)1938-1883

Conference

Title58th IEEE International Conference on Communications (ICC 2023)
LocationLa Nuvola Convention Center
PlaceItaly
CityRome
Period28 May - 1 June 2023

Abstract

Reliability is of paramount importance for the physical layer of wireless systems due to its decisive impact on end-to-end performance. However, the uncertainty of prevailing deep learning (DL)-based physical layer algorithms is hard to quantify due to the black-box nature of neural networks. This limitation is a major obstacle that hinders their practical deployment. In this paper, we attempt to quantify the uncertainty of an important category of DL-based channel estimators. An efficient statistical method is proposed to make blind predictions for the mean squared error of the DL-estimated channel solely based on received pilots, without knowledge of the ground-truth channel, the prior distribution of the channel, or the noise statistics. The complexity of the blind performance prediction is low and scales only linearly with the number of antennas. Simulation results for ultra-massive multiple-input multiple-output (UM-MIMO) channel estimation with a mixture of far-field and near-field paths are provided to verify the accuracy and efficiency of the proposed method. © 2023 IEEE.

Bibliographic Note

Information for this record is supplemented by the author(s) concerned.

Citation Format(s)

Blind performance prediction for deep learning based ultra-massive MIMO channel estimation. / Yu, Wentao; He, Hengtao; Yu, Xianghao et al.
ICC 2023 - IEEE International Conference on Communications. ed. / Michele Zorzi; Meixia Tao; Walid Saad. Institute of Electrical and Electronics Engineers, Inc., 2023. p. 2613-2618.

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review