@misc{liu_energy_2024,

title = {Energy Efficiency Comparison of THz and VCSEL-Based OWC for 6G},

author = {Mingqing Liu and Hossein Kazemi and Majid Safari and Iman Tavakkolnia and Harald Haas},

url = {https://www.repository.cam.ac.uk/handle/1810/375123},

doi = {10.17863/CAM.112916},

year  = {2024},

date = {2024-10-01},

urldate = {2024-10-30},

publisher = {Apollo - University of Cambridge Repository},

abstract = {Both optical wireless communications (OWC) and Terahertz (THz) communication systems are regarded as enablers for the 6th-generation (6G) networks. Facing the 6G requirements of ultra-dense and high-rate networks, vertical-cavity surface-emitting laser (VCSEL)-based OWC is considered as a strong contender for providing high data rates with ultra-small cells. Hence, this work quantitatively compares VCSEL-based OWC and THz communication in terms of energy efficiency. We derive the energy efficiency model for realistic systems by including non-linearity power conversion of VCSEL and area-bandwidth tradeoff of the photodiode (PD) for OWC, and gain and power loss in cascaded THz systems, respectively. Then, we formulate the similarity of embedding optical lenses into VCSEL-based system and high-directivity antenna into THz system to achieve high channel gain, i.e., leading to a gain-coverage tradeoff for both systems. Taking account of this performance tradeoff in analyzing two systems' energy efficiency, we show numerically the superiority of the VCSEL-based system, while the THz system is more robust in long-range scenarios due to a more relaxed relationship between pointing error and transmission distances. This study provides design guidelines for selecting appropriate technologies tailored to various 6G application scenarios from an energy efficiency perspective.},

keywords = {6G, Energy Efficiency, LRDC, Optical wireless communication, Vertical cavity surface emitting lasers},

pubstate = {published},

tppubtype = {misc}

}

@misc{yuri_leveraging_2024,

title = {Leveraging Time-domain Fingerprinting for Joint LiFi Position and Orientation Estimation},

author = {Jeon Yuri and Basu Amlan and Iman Tavakkolnia and Harald Haas},

url = {https://www.repository.cam.ac.uk/handle/1810/375328},

doi = {10.17863/CAM.113042},

year  = {2024},

date = {2024-10-01},

urldate = {2024-10-30},

publisher = {Apollo - University of Cambridge Repository},

abstract = {To support performance requirements for smart services in 6G, user positioning is a crucial component. Indoor user position and orientation estimation based on Light Fidelity (LiFi) system is considered as a promising technology, due to its high precision, along with its ease of installation. The main bottleneck of user position and orientation estimation in LiFi is a non-linearity between the metrics, such as the received signal strength (RSS), position and orientation. A deep learning-based estimation methodology holds promise for addressing this issue, because it can learn complex propagation features dependent on user position and orientation. To fully capitalize on this advantage in the time-domain, we propose utilizing both time-series RSS and its received time, i.e. time-of-arrival (ToA) fingerprints, along with a novel neural network architecture named Deep RSS-ToA Fusion Network (DRTFNet). Simulation results demonstrate that the proposed DRTFNet achieves positioning accuracy of less than 3 cm and orientation accuracy of less than 3 degrees, outperforming both the basic Convolutional Neural Network (CNN) architecture using only RSS data and other baseline systems with more light sources.},

keywords = {6G, LiFi, LRDC},

pubstate = {published},

tppubtype = {misc}

}