Terahertz Massive MIMO with Holographic Reconfigurable Intelligent Surfaces
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Abstract (arXiv)
We propose a holographic version of a reconfigurable intelligent surface (RIS) and investigate its application to terahertz (THz) massive multiple-input multiple-output systems. Capitalizing on the miniaturization of THz electronic components, RISs can be implemented by densely packing sub-wavelength unit cells, so as to realize continuous or quasi-continuous apertures and to enable holographic communications. In this paper, in particular, we derive the beam pattern of a holographic RIS. Our analysis reveals that the beam pattern of an ideal holographic RIS can be well approximated by that of an ultra-dense RIS, which has a more practical hardware architecture. In addition, we propose a closed-loop channel estimation (CE) scheme to effectively estimate the broadband channels that characterize THz massive MIMO systems aided by holographic RISs. The proposed CE scheme includes a downlink coarse CE stage and an uplink finer-grained CE stage. The uplink pilot signals are judiciously designed for obtaining good CE performance. Moreover, to reduce the pilot overhead, we introduce a compressive sensing-based CE algorithm, which exploits the dual sparsity of THz MIMO channels in both the angular domain and delay domain. Simulation results demonstrate the superiority of holographic RISs over the non-holographic ones, and the effectiveness of the proposed CE scheme.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Sep 2020.
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Citing papers per year: 2021: 2; 2022: 3; 2023: 1; 2024: 1; 2025: 0; 2026: 1.
8 papers.
- 2022Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfacescited by 12
- 2021Time-Varying Channel Prediction for RIS-Assisted MU-MISO Networks via Deep Learningcited by 4
- 2022Deep Learning-Based Rate-Splitting Multiple Access for Reconfigurable Intelligent Surface-Aided Tera-Hertz Massive MIMOcited by 4
- 2024Cross-domain Learning Framework for Tracking Users in RIS-aided Multi-band ISAC Systems with Sparse Labeled Datacited by 4
- 2022Learning Optimal Phase-Shifts of Holographic Metasurface Transceiverscited by 1
- 2021Beyond 5G RIS mmWave Systems: Where Communication and Localization Meetcited by 0
- 2023HoloBeam: Learning Optimal Beamforming in Far-Field Holographic Metasurface Transceiverscited by 0
- 2026Hybrid-Field Sparse Channel Representation and Recovery for XL-RIS-Assisted mmWave MIMO Systemscited by 0
- 2026Hybrid-Field Sparse Channel Representation and Recovery for XL-RIS-Assisted mmWave MIMO Systems26 Jul 2026
- 2024Cross-domain Learning Framework for Tracking Users in RIS-aided Multi-band ISAC Systems with Sparse Labeled Data10 May 2024
- 2023HoloBeam: Learning Optimal Beamforming in Far-Field Holographic Metasurface Transceivers30 Dec 2023
- 2022Learning Optimal Phase-Shifts of Holographic Metasurface Transceivers12 Dec 2022
- 2022Deep Learning-Based Rate-Splitting Multiple Access for Reconfigurable Intelligent Surface-Aided Tera-Hertz Massive MIMO18 Sep 2022
- 2022Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces8 May 2022
- 2021Time-Varying Channel Prediction for RIS-Assisted MU-MISO Networks via Deep Learning9 Nov 2021
- 2021Beyond 5G RIS mmWave Systems: Where Communication and Localization Meet16 Sep 2021