Utilizing Lighting Design Software for Simulation and Planning of Machine Learning Based Angle-of-Arrival (AOA) Visible Light Positioning (VLP) Systems

Hei Man Chan; Chi Wai Chow; Li Sheng Hsu; Yang Liu; Ching Wei Peng; Yin He Jian; Chien Hung Yeh

doi:10.1109/JPHOT.2022.3212628

Utilizing Lighting Design Software for Simulation and Planning of Machine Learning Based Angle-of-Arrival (AOA) Visible Light Positioning (VLP) Systems

Hei Man Chan, Chi Wai Chow, Li Sheng Hsu, Yang Liu, Ching Wei Peng, Yin He Jian, Chien Hung Yeh

Department of Photonics

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

Abstract

We propose to utilize a commercially available DIALux lighting design software for simulation and planning of machine learning (ML) based angle-of-arrival (AOA) visible light positioning (VLP) systems. Here, different ML models, for example, second order linear regression (LR), artificial neural-network (ANN), and convolutional neural-network (CNN) are employed. The proposed VLP simulator works well with different ML algorithms. The results show that the proposed scheme can acts as an effective indoor VLP planning and design tool. Besides, it may also alleviate the training data collection in ML based VLP systems.

Original language	English
Pages (from-to)	1-7
Number of pages	7
Journal	IEEE Photonics Journal
DOIs	https://doi.org/10.1109/JPHOT.2022.3212628
State	Accepted/In press - 2022

Keywords

Angle-of-Arrival (AOA)
LED lamps
Light emitting diode (LED)
Lighting
Optical wireless communication (OWC)
Planning
Radio frequency
Software
Testing
Training
Visible Light Communication
Visible light positioning (VLP)

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10.1109/JPHOT.2022.3212628

Cite this

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title = "Utilizing Lighting Design Software for Simulation and Planning of Machine Learning Based Angle-of-Arrival (AOA) Visible Light Positioning (VLP) Systems",

abstract = "We propose to utilize a commercially available DIALux lighting design software for simulation and planning of machine learning (ML) based angle-of-arrival (AOA) visible light positioning (VLP) systems. Here, different ML models, for example, second order linear regression (LR), artificial neural-network (ANN), and convolutional neural-network (CNN) are employed. The proposed VLP simulator works well with different ML algorithms. The results show that the proposed scheme can acts as an effective indoor VLP planning and design tool. Besides, it may also alleviate the training data collection in ML based VLP systems.",

keywords = "Angle-of-Arrival (AOA), LED lamps, Light emitting diode (LED), Lighting, Optical wireless communication (OWC), Planning, Radio frequency, Software, Testing, Training, Visible Light Communication, Visible light positioning (VLP)",

author = "Chan, {Hei Man} and Chow, {Chi Wai} and Hsu, {Li Sheng} and Yang Liu and Peng, {Ching Wei} and Jian, {Yin He} and Yeh, {Chien Hung}",

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year = "2022",

doi = "10.1109/JPHOT.2022.3212628",

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pages = "1--7",

journal = "IEEE Photonics Journal",

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T1 - Utilizing Lighting Design Software for Simulation and Planning of Machine Learning Based Angle-of-Arrival (AOA) Visible Light Positioning (VLP) Systems

AU - Chan, Hei Man

AU - Chow, Chi Wai

AU - Hsu, Li Sheng

AU - Liu, Yang

AU - Peng, Ching Wei

AU - Jian, Yin He

AU - Yeh, Chien Hung

N1 - Publisher Copyright: Author

PY - 2022

Y1 - 2022

N2 - We propose to utilize a commercially available DIALux lighting design software for simulation and planning of machine learning (ML) based angle-of-arrival (AOA) visible light positioning (VLP) systems. Here, different ML models, for example, second order linear regression (LR), artificial neural-network (ANN), and convolutional neural-network (CNN) are employed. The proposed VLP simulator works well with different ML algorithms. The results show that the proposed scheme can acts as an effective indoor VLP planning and design tool. Besides, it may also alleviate the training data collection in ML based VLP systems.

AB - We propose to utilize a commercially available DIALux lighting design software for simulation and planning of machine learning (ML) based angle-of-arrival (AOA) visible light positioning (VLP) systems. Here, different ML models, for example, second order linear regression (LR), artificial neural-network (ANN), and convolutional neural-network (CNN) are employed. The proposed VLP simulator works well with different ML algorithms. The results show that the proposed scheme can acts as an effective indoor VLP planning and design tool. Besides, it may also alleviate the training data collection in ML based VLP systems.

KW - Angle-of-Arrival (AOA)

KW - LED lamps

KW - Light emitting diode (LED)

KW - Lighting

KW - Optical wireless communication (OWC)

KW - Planning

KW - Radio frequency

KW - Software

KW - Testing

KW - Training

KW - Visible Light Communication

KW - Visible light positioning (VLP)

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JO - IEEE Photonics Journal

JF - IEEE Photonics Journal

ER -

Utilizing Lighting Design Software for Simulation and Planning of Machine Learning Based Angle-of-Arrival (AOA) Visible Light Positioning (VLP) Systems

Abstract

Keywords

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