496 research outputs found
Terahertz-Band Channel and Beam Split Estimation via Array Perturbation Model
For the demonstration of ultra-wideband bandwidth and pencil-beamforming, the
terahertz (THz)-band has been envisioned as one of the key enabling
technologies for the sixth generation networks. However, the acquisition of the
THz channel entails several unique challenges such as severe path loss and
beam-split. Prior works usually employ ultra-massive arrays and additional
hardware components comprised of time-delayers to compensate for these loses.
In order to provide a cost-effective solution, this paper introduces a
sparse-Bayesian-learning (SBL) technique for joint channel and beam-split
estimation. Specifically, we first model the beam-split as an array
perturbation inspired from array signal processing. Next, a low-complexity
approach is developed by exploiting the line-of-sight-dominant feature of THz
channel to reduce the computational complexity involved in the proposed SBL
technique for channel estimation (SBCE). Additionally, based on
federated-learning, we implement a model-free technique to the proposed
model-based SBCE solution. Further to that, we examine the near-field
considerations of THz channel, and introduce the range-dependent near-field
beam-split. The theoretical performance bounds, i.e., Cram\'er-Rao lower
bounds, are derived both for near- and far-field parameters, e.g., user
directions, beam-split and ranges. Numerical simulations demonstrate that SBCE
outperforms the existing approaches and exhibits lower hardware cost.Comment: Accepted Paper in IEEE Open Journal of Communications Societ
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