  Hybrid transceiver design in multiple-input multiple-output (MIMO) Tera-Hertz
(THz) systems relying on sparse channel state information (CSI) estimation
techniques is conceived. To begin with, a practical MIMO channel model is
developed for the THz band that incorporates its molecular absorption and
reflection losses, as well as its non-line-of-sight (NLoS) rays associated with
its diffused components. Subsequently, a novel CSI estimation model is derived
by exploiting the angular-sparsity of the THz MIMO channel. Then an orthogonal
matching pursuit (OMP)-based framework is conceived, followed by designing a
sophisticated Bayesian learning (BL)-based approach for efficient estimation of
the sparse THz MIMO channel. The Bayesian Cramer-Rao Lower Bound (BCRLB) is
also determined for benchmarking the performance of the CSI estimation
techniques developed. Finally, an optimal hybrid transmit precoder and receiver
combiner pair is designed, which directly relies on the beamspace domain CSI
estimates and only requires limited feedback. Finally, simulation results are
provided for quantifying the improved mean square error (MSE),
spectral-efficiency (SE) and bit-error rate (BER) performance for transmission
on practical THz MIMO channel obtained from the HIgh resolution TRANsmission
(HITRAN)-database.
