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Reconfigurable intelligent surface (RIS) can significantly enhance theservice coverage of Tera-Hertz massive multiple-input multiple-output (MIMO)communication systems. However, obtaining accurate high-dimensional channelstate information (CSI) with limited pilot and feedback signaling overhead ischallenging, severely degrading the performance of conventional spatialdivision multiple access. To improve the robustness against CSI imperfection,this paper proposes a deep learning (DL)-based rate-splitting multiple access(RSMA) scheme for RIS-aided Tera-Hertz multi-user MIMO systems. Specifically,we first propose a hybrid data-model driven DL-based RSMA precoding scheme,including the passive precoding at the RIS as well as the analog activeprecoding and the RSMA digital active precoding at the base station (BS). Torealize the passive precoding at the RIS, we propose a Transformer-baseddata-driven RIS reflecting network (RRN). As for the analog active precoding atthe BS, we propose a match-filter based analog precoding scheme consideringthat the BS and RIS adopt the LoS-MIMO antenna array architecture. As for theRSMA digital active precoding at the BS, we propose a low-complexityapproximate weighted minimum mean square error (AWMMSE) digital precodingscheme. Furthermore, for better precoding performance as well as lowercomputational complexity, a model-driven deep unfolding active precodingnetwork (DFAPN) is also designed by combining the proposed AWMMSE scheme withDL. Then, to acquire accurate CSI at the BS for the investigated RSMA precodingscheme to achieve higher spectral efficiency, we propose a CSI acquisitionnetwork (CAN) with low pilot and feedback signaling overhead, where thedownlink pilot transmission, CSI feedback at the user equipments (UEs), and CSIreconstruction at the BS are modeled as an end-to-end neural network based onTransformer.

Deep Learning-Based Rate-Splitting Multiple Access for Reconfigurable Intelligent Surface-Aided Tera-Hertz Massive MIMO