Terahertz (THz) band is expected to be one of the key enabling technologies of the sixth generation (6G) wireless networks because of its abundant available bandwidth and very narrow beamwidth. Due to high frequency operations, electrically small array apertures are employed, and the signal wavefront becomes spherical in the near-field. Therefore, near-field signal model should be considered for channel acquisition in THz systems. Unlike prior works which mostly ignore the impact of near-field beam-squint (NB) and consider either narrowband scenario or far-field models, this paper introduces both a model-based and a model-free techniques for wideband THz channel estimation in the presence of NB. The model-based approach is based on orthogonal matching pursuit (OMP) algorithm, for which we design an NB-aware dictionary. The key idea is to exploit the angular and range deviations due to the NB. We then employ the OMP algorithm, which accounts for the deviations thereby ipso facto mitigating the effect of NB. We further introduce a federated learning (FL)-based approach as a model-free solution for channel estimation in a multi-user scenario to achieve reduced complexity and training overhead. Through numerical simulations, we demonstrate the effectiveness of the proposed channel estimation techniques for wideband THz systems in comparison with the existing state-of-the-art techniques.

Original languageEnglish
Article number3266297
Pages (from-to)36409-36420
Number of pages12
JournalIEEE Access
Early online date11 Apr 2023
Publication statusE-pub ahead of print - 11 Apr 2023


  • Beamsquint
  • Channel estimation
  • channel estimation
  • Estimation
  • federated learning
  • machine learning
  • Matching pursuit algorithms
  • MIMO communication
  • near-field
  • orthogonal matching pursuit
  • Radio frequency
  • Solid modeling
  • sparse recovery
  • terahertz
  • Wideband


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