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Benefiting from the growth of the bandwidth, Terahertz (THz) communicationcan support the new application with explosive requirements of theultra-high-speed rates for future 6G wireless systems. In order to compensatefor the path loss of high frequency, massive multiple-input multiple-output(MIMO) can be utilized for high array gains by beamforming. However, since alarge number of analog phase shifters should be used to realize the analogbeamforming, the existing THz communication with massive MIMO has very highenergy consumption. To solve this problem, a reconfigurable intelligent surface(RIS)-based hybrid precoding architecture for THz communication is developed inthis paper, where the energy-hungry phased array is replaced by theenergy-efficient RIS to realize the analog beamforming of the hybrid precoding.Then, based on the proposed RIS-based architecture, a sum-rate maximizationproblem for hybrid precoding is investigated. Since the phase shiftsimplemented by RIS in practice are often discrete, this sum-rate maximizationproblem with a non-convex constraint is challenging. Next, the sum-ratemaximization problem is reformulated as a parallel deep neural network(DNN)-based classification problem, which can be solved by the proposedlow-complexity deep learning-based multiple discrete classification (DL-MDC)hybrid precoding scheme. Finally, we provide numerous simulation results toshow that the proposed DL-MDC scheme works well both in the theoreticalSaleh-Valenzuela channel model and practical 3GPP channel model. Compared withexisting iterative search algorithms, the can proposed DL-MDC scheme reducesthe runtime significantly with a negligible performance loss.

Reconfigurable Intelligent Surface Based Hybrid Precoding for THz Communications