  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 beam width. 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-split (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.
