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Reconfigurable Intelligent Surface E...

Liu, Hang.

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Reconfigurable Intelligent Surface Empowered Wireless Communications: Challenges and Opportunities.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Reconfigurable Intelligent Surface Empowered Wireless Communications: Challenges and Opportunities./
Author:	Liu, Hang.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	163 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-06, Section: B.
Contained By:	Dissertations Abstracts International83-06B.
Subject:	Computer science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28940822
ISBN:	9798496515313

Reconfigurable Intelligent Surface Empowered Wireless Communications: Challenges and Opportunities.
Liu, Hang.

Reconfigurable Intelligent Surface Empowered Wireless Communications: Challenges and Opportunities. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 163 p.

Source: Dissertations Abstracts International, Volume: 83-06, Section: B.

Thesis (Ph.D.)--The Chinese University of Hong Kong (Hong Kong), 2021.

This item is not available from ProQuest Dissertations & Theses.

Reconfigurable intelligent surface (RIS) is envisioned to be one of the key enablers of sixth-generation (6G) wireless networks as it can artificially customize the propagation channels by introducing controllable and independent phase shifts on electromagnetic waves. With RIS, the wireless environment is no longer an uncontrollable entity, but rather can be configured to suit diverse system requirements. Therefore, integrating RIS into wireless networks brings great opportunities that cannot be achieved by the existing technologies. However, new challenges arise in the incorporation of RIS into wireless networks. Particularly, channel estimation in RIS-empowered systems is more challenging than that in traditional systems since the passive RIS has the minimal capability of receiving, processing, and transmitting incident signals.In this thesis, we study the challenges and research opportunities in RIS-empowered systems. First, we propose a low-overhead channel estimation algorithm for RIS-assisted multiuser communication systems by exploiting the slow-varying information of the RIS-receiver channel and the inherent sparsity of the transmitter-RIS channel. Second, we investigate an emerging application in 6G communications, namely federated learning (FL) over mobile edge networks, and discuss the opportunities brought by RIS in such a system. We leverage the advances of RIS to enhance the communication performance in FL. Finally, this thesis discusses open issues in RIS channel estimation and concludes with future research directions in RIS-enabled FL.

ISBN: 9798496515313Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Reconfigurable intelligent surface

Reconfigurable Intelligent Surface Empowered Wireless Communications: Challenges and Opportunities.
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300 $a 163 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-06, Section: B.
502 $a Thesis (Ph.D.)--The Chinese University of Hong Kong (Hong Kong), 2021.
506 $a This item is not available from ProQuest Dissertations & Theses.
520 $a Reconfigurable intelligent surface (RIS) is envisioned to be one of the key enablers of sixth-generation (6G) wireless networks as it can artificially customize the propagation channels by introducing controllable and independent phase shifts on electromagnetic waves. With RIS, the wireless environment is no longer an uncontrollable entity, but rather can be configured to suit diverse system requirements. Therefore, integrating RIS into wireless networks brings great opportunities that cannot be achieved by the existing technologies. However, new challenges arise in the incorporation of RIS into wireless networks. Particularly, channel estimation in RIS-empowered systems is more challenging than that in traditional systems since the passive RIS has the minimal capability of receiving, processing, and transmitting incident signals.In this thesis, we study the challenges and research opportunities in RIS-empowered systems. First, we propose a low-overhead channel estimation algorithm for RIS-assisted multiuser communication systems by exploiting the slow-varying information of the RIS-receiver channel and the inherent sparsity of the transmitter-RIS channel. Second, we investigate an emerging application in 6G communications, namely federated learning (FL) over mobile edge networks, and discuss the opportunities brought by RIS in such a system. We leverage the advances of RIS to enhance the communication performance in FL. Finally, this thesis discusses open issues in RIS channel estimation and concludes with future research directions in RIS-enabled FL.
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