Literature Database Entry

athanasiadis2024multi-irs-assisted


Nikolaos Athanasiadis, "Multi-IRS-Assisted Multi-Operator Networks," Bachelor Thesis, School of Electrical Engineering and Computer Science (EECS), TU Berlin (TUB), March 2024. (Advisors: Joana Angjo and Anatolij Zubow; Referees: Falko Dressler and Thomas Sikora)


Abstract

Intelligent Reconfigurable Surfaces (IRSs) are one of the definitive efficient and low cost technologies aiming to improve wireless communications in 6th Generation (6G) wireless networks. Their deployment - particularly in the context of multi-operator environments - introduces complex challenges regarding network coexistence, undermining their potential benefits by increased interference and reduced network efficiency. In this thesis, a study on the IRS-IRS link in a non-cooperative multi-IRS environment is conducted by examining IRS geometry, beamforming and path loss, establishing that the interaction between IRSs can be disregarded under certain conditions. The results are further confirmed in a MATLAB ray tracing simulation by modeling uniform rectangular antenna arrays with patterns resembling the beamforming patterns of the IRS using MATLAB's Communications ToolboxTM and Antenna Toolbox. Lastly, a MATLAB coexistence simulation is performed which consists of multiple randomly placed Base Stations (BSs) and User Equipments (UEs) in an environment of 2-10 Operators and 2-10 IRSs. Performance metrics such as Sum Rate (SR) and Jain's Fairness Index (JFI) show that splitting the IRSs in sub-IRS surfaces and dynamically allocating them to optimally selected operators can lead to increased network fairness and efficiency. This strategic allocation effectively mitigates unwanted reflections, a critical factor in optimizing the coexistence of multiple operators within IRS-assisted networks. This thesis studies the technical challenges posed by multi-IRS deployment and proposes viable strategies for realizing the full potential of the technology. Several directions for future research, including the exploration of more sophisticated algorithms for dynamic sub-IRS allocation, the integration of machine learning for predictive network management, and the investigation of IRS deployment strategies in different environmental contexts are proposed as well.

Quick access

BibTeX BibTeX

Contact

Nikolaos Athanasiadis

BibTeX reference

@phdthesis{athanasiadis2024multi-irs-assisted,
    author = {Athanasiadis, Nikolaos},
    title = {{Multi-IRS-Assisted Multi-Operator Networks}},
    advisor = {Angjo, Joana and Zubow, Anatolij},
    institution = {School of Electrical Engineering and Computer Science (EECS)},
    location = {Berlin, Germany},
    month = {3},
    referee = {Dressler, Falko and Sikora, Thomas},
    school = {TU Berlin (TUB)},
    type = {Bachelor Thesis},
    year = {2024},
   }
   
   

Copyright notice

Links to final or draft versions of papers are presented here to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted or distributed for commercial purposes without the explicit permission of the copyright holder.

The following applies to all papers listed above that have IEEE copyrights: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

The following applies to all papers listed above that are in submission to IEEE conference/workshop proceedings or journals: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

The following applies to all papers listed above that have ACM copyrights: ACM COPYRIGHT NOTICE. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept., ACM, Inc., fax +1 (212) 869-0481, or permissions@acm.org.

The following applies to all SpringerLink papers listed above that have Springer Science+Business Media copyrights: The original publication is available at www.springerlink.com.

This page was automatically generated using BibDB and bib2web.

Last modified: 2024-10-14