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Towards practical LiFi networks with hybrid radio-frequency and optical wireless communication technologies

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ThesisDayreneFrometaDeposit.pdf (18.67Mb)
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URI: https://hdl.handle.net/20.500.12761/1962
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Author(s)
Frómeta Fonseca, Dayrene
Supervisor(s)/Director(s)
Giustiniano, Domenico
Date
2024-06-28
Abstract
This thesis focuses on identifying, investigating, and addressing three main limitations of LiFi systems in a typical indoor scenario. First, this thesis addresses the backhaul connectivity of the LiFi Access Points (AP) to the Internet. Current backhaul solutions for LiFi networks face practical limitations, especially for battery-free IoT applications. To fill this gap, the thesis proposes LoW-Fi. This novel downlink backhaul solution reuses the existing lighting and WiFi infrastructure to ensure the scalability, flexibility, and low power consumption required by battery-free IoT deployments. Then, this thesis proposes a comprehensive and bi-directional system architecture, integrating LoW-Fi for downlink and PassiveLiFi RF backscatter system for uplink. This results in a novel network architecture suitable for applications like precision agriculture. The proposed solutions were implemented and tested in a real greenhouse applications scenario. Second, this thesis targets the dimming problem in LiFi networks. Domming is the functionality of LiFi systems that allows users to adapt the illumination conditions to their eye comfort without compromising the LiFi communication performance. Existing dimming schemes for LiFi networks have many drawbacks, including limited precision on the adjustment of the dimming level, constrained dimming range, and high complexity. The thesis presents a novel modulation and dimming scheme that addresses such limitations by operating in both VL and IR bands, maintaining good communication performance even under extreme illumination conditions. Additionally, the thesis tackles the handover decision-making problem in hybrid LiFi and WiFi networks, introducing RL-HO, a handover algorithm leveraging machine learning techniques to optimize handover rate and average throughput. Overall, this thesis contributes practical solutions to enhance the performance and applicability of LiFi technology in real scenarios, addressing critical challenges for its widespread adoption.
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Files
ThesisDayreneFrometaDeposit.pdf (18.67Mb)
Identifiers
URI: https://hdl.handle.net/20.500.12761/1962
Metadata
Show full item record

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