  Wireless communications and sensing at terahertz (THz) band are increasingly
investigated as promising short-range technologies because of the availability
of high operational bandwidth at THz. In order to address the extremely high
attenuation at THz, ultra-massive multiple-input multiple-output (UM-MIMO)
antenna systems have been proposed for THz communications to compensate
propagation losses. However, the cost and power associated with fully digital
beamformers of these huge antenna arrays are prohibitive. In this paper, we
develop THz hybrid beamformers based on both model-based and model-free
techniques for a new group-of-subarrays (GoSA) UM-MIMO structure. Further,
driven by the recent developments to save the spectrum, we propose beamformers
for a joint UM-MIMO radar-communications system, wherein the base station
serves multi-antenna user equipment (RX), and tracks radar targets by
generating multiple beams toward both RX and the targets. We formulate the GoSA
beamformer design as an optimization problem to provide a trade-off between the
unconstrained communications beamformers and the desired radar beamformers.
Additionally, our design also exploits second-order channel statistics so that
an infrequent channel feedback from the RX is achieved with less channel
overhead. To further decrease the UM-MIMO computational complexity and enhance
robustness, we also implement deep learning solutions to the proposed
model-based hybrid beamformers. Numerical experiments demonstrate that both
techniques outperform the conventional approaches in terms of spectral
efficiency and radar beampatterns, as well as exhibiting less hardware cost and
computation time.
