Reconfigurable intelligent surface assisted grant-free massive access

Xingyu Zhou, Keke Ying, Shicong Liu, Malong Ke, Zhen Gao, Mohamed-Slim Alouini

Research output: Contribution to journalArticlepeer-review

Abstract

Massive machine-type communications (mMTC) is envisioned to be one of the pivotal scenarios in the fifth-generation (5G) wireless communication, where the explosively emerging Internet-of-Things (IoT) applications have triggered the demand for services with low-latency and high-reliability. To this end, grant-free random access paradigm has been proposed as a promising enabler in simplifying the connection procedure and significantly reducing access latency. In this paper, we propose to leverage the burgeoning reconfigurable intelligent surface (RIS) for grant-free massive access working at millimeter-wave (mmWave) frequency to further boost access reliability. By attaching independently controllable phase shifts, reconfiguring, and refracting the propagation of incident electromagnetic waves, the deployed RISs could provide additional diversity gain and enhance the access channel conditions. On this basis, to address the challenging active device detection (ADD) and channel estimation (CE) problem, we develop a joint-ADDCE (JADDCE) method by resorting to the existing approximate message passing (AMP) algorithm with expectation maximization (EM) to extract the structured common sparsity in traffic behaviors and cascaded channel matrices. Finally, simulations are carried out to demonstrate the superiority of our proposed scheme.
Original languageEnglish (US)
Pages (from-to)134-143
Number of pages10
JournalIntelligent and Converged Networks
Volume3
Issue number1
DOIs
StatePublished - Apr 29 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-05-10
Acknowledgements: Supported by the National Natural Science Foundation of China (NSFC) (No. 62071044), the Open Research Fund of National Mobile Communications Research Laboratory, Southeast University (No. 2022D09), Beijing Institute of Technology Research Fund Program for Young Scholars (No. XSQD-202121009), and Ensan Foundation (No. 2022006).

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