Hybrid Far- and Near-Field Channel Estimation for THz Ultra-Massive MIMO via Fixed Point Networks

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

9 Scopus Citations
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Author(s)

  • Wentao Yu
  • Yifei Shen
  • Hengtao He
  • Jun Zhang
  • Khaled B. Letaief

Detail(s)

Original languageEnglish
Title of host publication2022 IEEE Global Communications Conference (GLOBECOM)
Subtitle of host publicationProceedings
PublisherInstitute of Electrical and Electronics Engineers, Inc.
Pages5384-5389
ISBN (electronic)9781665435406
ISBN (print)978-1-6654-3541-3
Publication statusPublished - Dec 2022
Externally publishedYes

Publication series

NameIEEE Global Communications Conference, GLOBECOM - Proceedings

Conference

Title2022 IEEE Global Communications Conference (GLOBECOM 2022)
LocationWindsor Barra Hotel (Hybrid)
PlaceBrazil
CityRio de Janeiro
Period4 - 8 December 2022

Abstract

Terahertz ultra-massive multiple-input multiple-output (THz UM-MIMO) is envisioned as one of the key enablers of 6G wireless systems. Due to the joint effect of its large array aperture and small wavelength, the near-field region of THz UM-MIMO is greatly enlarged. The high-dimensional channel of such systems thus consists of a stochastic mixture of far and near fields, which renders channel estimation extremely challenging. Previous works based on uni-field assumptions cannot capture the hybrid far- and near-field features, thus suffering significant performance loss. This motivates us to consider hybrid-field channel estimation. We draw inspirations from fixed point theory to develop an efficient deep learning based channel estimator with adaptive complexity and linear convergence guarantee. Built upon classic orthogonal approximate message passing, we transform each iteration into a contractive mapping, comprising a closed-form linear estimator and a neural network based non-linear estimator. A major algorithmic innovation involves applying fixed point iteration to compute the channel estimate while modeling neural networks with arbitrary depth and adapting to the hybrid-field channel conditions. Simulation results verify our theoretical analysis and show significant performance gains over state-of-the-art approaches in the estimation accuracy and convergence rate.

Citation Format(s)

Hybrid Far- and Near-Field Channel Estimation for THz Ultra-Massive MIMO via Fixed Point Networks. / Yu, Wentao; Shen, Yifei; He, Hengtao et al.
2022 IEEE Global Communications Conference (GLOBECOM): Proceedings. Institute of Electrical and Electronics Engineers, Inc., 2022. p. 5384-5389 (IEEE Global Communications Conference, GLOBECOM - Proceedings).

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