Abstract
Terahertz (THz) communication has been regarded
as one promising technology to enhance the transmission capacity
of future internet-of-things (IoT) users due to its ultra-wide
bandwidth. Nonetheless, one major obstacle that prevents the
actual deployment of THz lies in its inherent huge attenuation.
Intelligent reflecting surface (IRS) and multiple-input multipleoutput
(MIMO) represent two effective solutions for compensating
the large pathloss in THz systems. In this paper, we consider
an IRS-aided multi-user THz MIMO system with orthogonal
frequency division multiple access, where the sparse radio frequency
chain antenna structure is adopted for reducing the power
consumption. The objective is to maximize the weighted sum rate
via jointly optimizing the hybrid analog/digital beamforming at
the base station and reflection matrix at the IRS. Since the analog
beamforming and reflection matrix need to cater all users and
subcarriers, it is difficult to directly solve the formulated problem,
and thus, an alternatively iterative optimization algorithm
is proposed. Specifically, the analog beamforming is designed
by solving a MIMO capacity maximization problem, while the
digital beamforming and reflection matrix optimization are both
tackled using semidefinite relaxation technique. Considering that
obtaining perfect channel state information (CSI) is a challenging
task in IRS-based systems, we further explore the case with the
imperfect CSI for the channels from the IRS to users. Under
this setup, we propose a robust beamforming and reflection
matrix design scheme for the originally formulated non-convex
optimization problem. Finally, simulation results are presented to
demonstrate the effectiveness of the proposed algorithms.