1.
Ihsan, Asim; Asif, Muhammad; Safi, Hossein; Tavakkolnia, Iman; Haas, Harald
Efficient Service Differentiation and Energy Management in Hybrid WiFi/LiFi Networks Journal Article
In: IEEE Transactions on Green Communications and Networking, vol. 10, pp. 1335–1351, 2026, ISSN: 2473-2400.
Links | BibTeX | Tags: LiFi, LRDC, Network management and orchestration
@article{ihsan_efficient_2026,
title = {Efficient Service Differentiation and Energy Management in Hybrid WiFi/LiFi Networks},
author = {Asim Ihsan and Muhammad Asif and Hossein Safi and Iman Tavakkolnia and Harald Haas},
url = {https://ieeexplore.ieee.org/document/11214543/},
doi = {10.1109/TGCN.2025.3624594},
issn = {2473-2400},
year = {2026},
date = {2026-01-01},
urldate = {2026-02-03},
journal = {IEEE Transactions on Green Communications and Networking},
volume = {10},
pages = {1335–1351},
keywords = {LiFi, LRDC, Network management and orchestration},
pubstate = {published},
tppubtype = {article}
}
2.
Zeng, Zhihong; Chen, Chen; Wu, Xiping; Savović, Svetislav; Soltani, Mohammad Dehghani; Safari, Majid; Haas, Harald
Interference mitigation using optimised angle diversity receiver in LiFi cellular network Journal Article
In: Optics Communications, vol. 574, pp. 131125, 2025, ISSN: 00304018.
Links | BibTeX | Tags: LiFi, LRDC
@article{zeng_interference_2025,
title = {Interference mitigation using optimised angle diversity receiver in LiFi cellular network},
author = {Zhihong Zeng and Chen Chen and Xiping Wu and Svetislav Savović and Mohammad Dehghani Soltani and Majid Safari and Harald Haas},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0030401824008629},
doi = {10.1016/j.optcom.2024.131125},
issn = {00304018},
year = {2025},
date = {2025-01-01},
urldate = {2024-10-30},
journal = {Optics Communications},
volume = {574},
pages = {131125},
keywords = {LiFi, LRDC},
pubstate = {published},
tppubtype = {article}
}
3.
Yuri, Jeon; Amlan, Basu; Tavakkolnia, Iman; Haas, Harald
Leveraging Time-domain Fingerprinting for Joint LiFi Position and Orientation Estimation Miscellaneous
2024.
Abstract | Links | BibTeX | Tags: 6G, LiFi, LRDC
@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}
}
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.
4.
Fonseca, Dayrene Frometa; Guzman, Borja Genoves; Martena, Giovanni Luca; Bian, Rui; Haas, Harald; Giustiniano, Domenico
Prediction-model-assisted reinforcement learning algorithm for handover decision-making in hybrid LiFi and WiFi networks Journal Article
In: Journal of Optical Communications and Networking, vol. 16, no. 2, pp. 159, 2024, ISSN: 1943-0620, 1943-0639.
Abstract | Links | BibTeX | Tags: LiFi, LRDC
@article{frometa_fonseca_prediction-model-assisted_2024,
title = {Prediction-model-assisted reinforcement learning algorithm for handover decision-making in hybrid LiFi and WiFi networks},
author = {Dayrene Frometa Fonseca and Borja Genoves Guzman and Giovanni Luca Martena and Rui Bian and Harald Haas and Domenico Giustiniano},
url = {https://opg.optica.org/abstract.cfm?URI=jocn-16-2-159},
doi = {10.1364/JOCN.495234},
issn = {1943-0620, 1943-0639},
year = {2024},
date = {2024-02-01},
urldate = {2024-10-30},
journal = {Journal of Optical Communications and Networking},
volume = {16},
number = {2},
pages = {159},
abstract = {The handover process in hybrid light fidelity (LiFi) and wireless fidelity (WiFi) networks (HLWNets) is very challenging due to the short area covered by LiFi access points and the coverage overlap between LiFi and WiFi networks, which introduce frequent horizontal and vertical handovers, respectively. Different handover schemes have been proposed to reduce the handover rate in HLWNets, among which handover skipping (HS) techniques stand out. However, existing solutions are still inefficient or require knowledge that is not available in practice, such as the exact user’s trajectory or the network topology. In this work, a novel machine learning-based handover scheme is proposed to overcome the limitations of previous HS works. Specifically, we have designed a classification model to predict the type of user’s trajectory and assist a reinforcement learning (RL) algorithm to make handover decisions that are automatically adapted to new network conditions. The proposed scheme is called RL-HO, and we compare its performance against the standard handover scheme of long-term evolution (STD-LTE) and the so-called smart handover (Smart HO) algorithm. We show that our proposed RL-HO scheme improves the network throughput by 146% and 59% compared to STD-LTE and Smart HO, respectively. We make our simulator code publicly available to the research community.},
keywords = {LiFi, LRDC},
pubstate = {published},
tppubtype = {article}
}
The handover process in hybrid light fidelity (LiFi) and wireless fidelity (WiFi) networks (HLWNets) is very challenging due to the short area covered by LiFi access points and the coverage overlap between LiFi and WiFi networks, which introduce frequent horizontal and vertical handovers, respectively. Different handover schemes have been proposed to reduce the handover rate in HLWNets, among which handover skipping (HS) techniques stand out. However, existing solutions are still inefficient or require knowledge that is not available in practice, such as the exact user’s trajectory or the network topology. In this work, a novel machine learning-based handover scheme is proposed to overcome the limitations of previous HS works. Specifically, we have designed a classification model to predict the type of user’s trajectory and assist a reinforcement learning (RL) algorithm to make handover decisions that are automatically adapted to new network conditions. The proposed scheme is called RL-HO, and we compare its performance against the standard handover scheme of long-term evolution (STD-LTE) and the so-called smart handover (Smart HO) algorithm. We show that our proposed RL-HO scheme improves the network throughput by 146% and 59% compared to STD-LTE and Smart HO, respectively. We make our simulator code publicly available to the research community.