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.

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Nikolaos Athanasiadis

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@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},
   }
   
   

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Last modified: 2024-05-04