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Beyond 5G RIS mmWave Systems: Where Communication and Localization Meet
Technology Innovation Institute, Abu Dhabi, United Arab Emirates; University of Oulu, Oulu, Finland.ORCID iD: 0000-0002-6227-2138
Chalmers University of Technology, Gothenburg, Sweden.ORCID iD: 0000-0003-0598-0178
Chalmers University of Technology, Gothenburg, Sweden.ORCID iD: 0000-0001-5044-1814
University of Oulu, Oulu, Finland.ORCID iD: 0000-0003-1077-3608
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2022 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 10, p. 68075-68084Article in journal (Refereed) Published
Abstract [en]

Upcoming beyond fifth generation (5G) communications systems aim at further enhancing key performance indicators and fully supporting brand-new use cases by embracing emerging techniques, e.g., reconfigurable intelligent surface (RIS), integrated communication, localization, and sensing, and mmWave/THz communications. The wireless intelligence empowered by state-of-the-art artificial intelligence techniques has been widely considered at the transceivers, and now the paradigm is deemed to be shifted to the smart control of radio propagation environment by virtue of RISs. In this paper, we argue that to harness the full potential of RISs, localization and communication must be tightly coupled. This is in sharp contrast to 5G and earlier generations, where localization was a minor additional service. To support this, we first introduce the fundamentals of RIS mmWave channel modeling, followed by RIS channel state information acquisition and link establishment. Then, we deal with the connection between localization and communications, from a separate and joint perspective. © 2013 IEEE

Place, publisher, year, edition, pages
Piscataway: IEEE, 2022. Vol. 10, p. 68075-68084
Keywords [en]
Channel modeling, millimeter wave, radio localization, reconfigurable intelligent surface, simultaneous localization and communications
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:hh:diva-49155DOI: 10.1109/access.2022.3186510ISI: 000819824500001Scopus ID: 2-s2.0-85133789028OAI: oai:DiVA.org:hh-49155DiVA, id: diva2:1725131
Funder
Academy of Finland, 318927Swedish Research Council, 2018-03701
Note

Funding text: This work was supported in part by the Horizon 2020, European Union's Framework Programme for Research and Innovation, ARIADNE, under Agreement 871464; in part by the Academy of Finland 6Genesis Flagship under Grant 318927; in part by the Swedish Research Council under Grant 2018-03701; and in part by the EU H2020 RISE-6G Project under Grant 101017011.

Available from: 2023-01-10 Created: 2023-01-10 Last updated: 2023-02-15Bibliographically approved

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