  Terahertz (THz) communication has been considered as a promising technology
to provide ultra-high-speed rates for future 6G wireless systems. To alleviate
the severe propagation attenuation in THz communication systems, massive
multiple-input multiple-output (MIMO) with hybrid precoding can be used for
beamforming to provide high array gains. In this paper, we propose a
reconfigurable intelligent surface (RIS)-based hybrid precoding architecture
for THz communication, where the energy-efficient RIS rather than the
energy-hungry phased array is used to realize the analog beamforming of the
hybrid precoding. Then, we investigate the hybrid precoding problem to maximize
the sum-rate for the proposed RIS-based hybrid precoding architecture. Due to
the non-convex constraint of discrete phase shifts by considering the practical
hardware implementation of RIS, this sum-rate maximization problem is
challenging to solve. Thus, we reformulate it as a parallel deep neural network
(DNN)-based classification problem, which can be solved by the proposed deep
learning-based multiple discrete classification (DL-MDC) hybrid precoding
scheme with low complexity. Simulation results show that the proposed scheme
works well both in theoretical Saleh-Valenzuela channel model and practical
3GPP channel model, and it can reduce the runtime significantly with a
negligible performance loss compared with existing iterative search algorithms.
