  Bayesian learning aided massive antenna array based THz MIMO systems are
designed for spatial-wideband and frequency-wideband scenarios, collectively
termed as the dual-wideband channels. Essentially, numerous antenna modules of
the THz system result in a significant delay in the transmission/ reception of
signals in the time-domain across the antennas, which leads to
spatial-selectivity. As a further phenomenon, the wide bandwidth of THz
communication results in substantial variation of the effective angle of
arrival/ departure (AoA/ AoD) with respect to the subcarrier frequency. This is
termed as the beam squint effect, which renders the channel state information
(CSI) estimation challenging in such systems. To address this problem,
initially, a pilot-aided (PA) Bayesian learning (PA-BL) framework is derived
for the estimation of the Terahertz (THz) MIMO channel that relies exclusively
on the pilot beams transmitted. Since the framework designed can successfully
operate in an ill-posed model, it can verifiably lead to reduced pilot
transmissions in comparison to conventional methodologies. The above paradigm
is subsequently extended to additionally incorporate data symbols to derive a
Data-Aided (DA) BL approach that performs joint data detection and CSI
estimation. We will demonstrate that it is capable of improving the
dual-wideband channels estimate, despite further reducing the training
overhead. The Bayesian Cramer-Rao bounds (BCRLBs) are also obtained for
explicitly characterizing the lower bounds on the mean squared error (MSE) of
the PA-BL and DA-BL frameworks. Our simulation results show the improved
normalized MSE (NMSE) and bit-error rate (BER) performance of the proposed
estimation schemes and confirm that they approach their respective BCRLB
benchmarks.
