Terahertz Massive MIMO with Holographic Reconfigurable Intelligent Surfaces

Ziwei Wan, Zhen Gao, Feifei Gao, Marco Di Renzo, Mohamed-Slim Alouini

Research output: Contribution to journalArticlepeer-review

133 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)1-1
Number of pages1
JournalIEEE Transactions on Communications
StatePublished - 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-03-12
Acknowledgements: The work of Z. Gao was supported by the Beijing Municipal Natural Science Foundation under Grant L182024, National Natural Science Foundation of China under Grant 62071044, the Young Elite Scientists Sponsorship Program by CAST under no. YESS20180270, and in part by the Talent Innovation Project of BIT. The work of M. Di Renzo was supported in part by the European Commission through the H2020 ARIADNE project under grant agreement no. 871464 and through the H2020 RISE-6G project under grant agreement no. 101017011. The codes and some other materials about this work may be available at https://gaozhen16.github.io


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