# Deep Learning-Based Rate-Splitting Multiple Access for Reconfigurable Intelligent Surface-Aided Tera-Hertz Massive MIMO arXiv: https://arxiv.org/abs/2209.08456 ยท HTML: https://mlsanity.com/paper/2209.08456/ ## Key facts - Cited by 4 papers in the map (data through 14 Oct 2026). - Builds on 27 of its 59 references that are in the map. - First posted on arXiv 18 Sep 2022. ## Builds on (most cited first, 27 of 27) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [A Survey on Visual Transformer](https://mlsanity.com/paper/2012.12556/) (2020) - [Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems](https://mlsanity.com/paper/2006.08099/) (2020) - [Deep Learning for Distributed Channel Feedback and Multiuser Precoding in FDD Massive MIMO](https://mlsanity.com/paper/2007.06512/) (2020) - [Enabling Large Intelligent Surfaces with Compressive Sensing and Deep Learning](https://mlsanity.com/paper/1904.10136/) (2019) - [Compressive Sensing Based Adaptive Active User Detection and Channel Estimation: Massive Access Meets Massive MIMO](https://mlsanity.com/paper/1906.09867/) (2019) - [Deep Learning based Denoise Network for CSI Feedback in FDD Massive MIMO Systems](https://mlsanity.com/paper/2004.07576/) (2020) - [Data-Driven Deep Learning to Design Pilot and Channel Estimator For Massive MIMO](https://mlsanity.com/paper/2003.05875/) (2020) - [Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter-Wave Massive Hybrid MIMO Systems](https://mlsanity.com/paper/2104.11052/) (2021) - [Prospective Multiple Antenna Technologies for Beyond 5G](https://mlsanity.com/paper/1910.00092/) (2019) - [Rate-Splitting Multiple Access: Fundamentals, Survey, and Future Research Trends](https://mlsanity.com/paper/2201.03192/) (2022) - [Intelligent Reflecting Surface-Enhanced OFDM: Channel Estimation and Reflection Optimization](https://mlsanity.com/paper/1909.03272/) (2019) - [Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces](https://mlsanity.com/paper/2006.02201/) (2020) - [Channel Estimation for RIS-Aided mmWave MIMO Systems via Atomic Norm Minimization](https://mlsanity.com/paper/2007.08158/) (2020) - [Terahertz Massive MIMO with Holographic Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2009.10963/) (2020) - [Channel Estimation and Hybrid Combining for Wideband Terahertz Massive MIMO Systems](https://mlsanity.com/paper/2102.06772/) (2021) - [Beyond Dirty Paper Coding for Multi-Antenna Broadcast Channel with Partial CSIT: A Rate-Splitting Approach](https://mlsanity.com/paper/1912.05409/) (2019) - [Principal Component Analysis Based Broadband Hybrid Precoding for Millimeter-Wave Massive MIMO Systems](https://mlsanity.com/paper/1903.09074/) (2019) - [Network Massive MIMO Transmission Over Millimeter-Wave and Terahertz Bands: Mobility Enhancement and Blockage Mitigation](https://mlsanity.com/paper/2005.02650/) (2020) - [Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access](https://mlsanity.com/paper/2205.00849/) (2022) - [Channel Estimation Method and Phase Shift Design for Reconfigurable Intelligent Surface Assisted MIMO Networks](https://mlsanity.com/paper/1912.10671/) (2019) - [Smart and Reconfigurable Wireless Communications: From IRS Modeling to Algorithm Design](https://mlsanity.com/paper/2103.07046/) (2021) - [Rate-Splitting Multiple Access for 6G -- Part III: Interplay with Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2205.02036/) (2022) - [Max-min Fairness of K-user Cooperative Rate-Splitting in MISO Broadcast Channel with User Relaying](https://mlsanity.com/paper/1910.07843/) (2019) - [Energy-efficient Dynamic-subarray with Fixed True-time-delay Design for Terahertz Wideband Hybrid Beamforming](https://mlsanity.com/paper/2202.02965/) (2022) - [Globally Optimal Spectrum- and Energy-Efficient Beamforming for Rate Splitting Multiple Access](https://mlsanity.com/paper/2204.00273/) (2022) ## Built on by (most cited first, 4 of 4) - [Deep Joint Semantic Coding and Beamforming for Near-Space Airship-Borne Massive MIMO Network](https://mlsanity.com/paper/2405.19889/) (2024) - [Modulation and Coding for NOMA and RSMA](https://mlsanity.com/paper/2410.00239/) (2024) - [Multi-beam Beamforming in RIS-aided MIMO Subject to Reradiation Mask Constraints -- Optimization and Machine Learning Design](https://mlsanity.com/paper/2507.15367/) (2025) - [E2E Learning Massive MIMO for Multimodal Semantic Non-Orthogonal Transmission and Fusion](https://mlsanity.com/paper/2509.19312/) (2025) ## Abstract (arXiv) Reconfigurable intelligent surface (RIS) can significantly enhance the service coverage of Tera-Hertz massive multiple-input multiple-output (MIMO) communication systems. However, obtaining accurate high-dimensional channel state information (CSI) with limited pilot and feedback signaling overhead is challenging, severely degrading the performance of conventional spatial division 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 active precoding and the RSMA digital active precoding at the base station (BS). To realize the passive precoding at the RIS, we propose a Transformer-based data-driven RIS reflecting network (RRN). As for the analog active precoding at the BS, we propose a match-filter based analog precoding scheme considering that the BS and RIS adopt the LoS-MIMO antenna array architecture. As for the RSMA digital active precoding at the BS, we propose a low-complexity approximate weighted minimum mean square error (AWMMSE) digital precoding scheme. Furthermore, for better precoding performance as well as lower computational complexity, a model-driven deep unfolding active precoding network (DFAPN) is also designed by combining the proposed AWMMSE scheme with DL. Then, to acquire accurate CSI at the BS for the investigated RSMA precoding scheme to achieve higher spectral efficiency, we propose a CSI acquisition network (CAN) with low pilot and feedback signaling overhead, where the downlink pilot transmission, CSI feedback at the user equipments (UEs), and CSI reconstruction at the BS are modeled as an end-to-end neural network based on Transformer.