Sub-Decimeter VLC 3D Indoor Localization With Handover Probability Analysis

Seyed Mohammad Sheikholeslami,Fahimeh Fazel,Konstantinos N Plataniotis,Jamshid Abouei

doi:10.1109/access.2021.3108173

Seyed Mohammad Sheikholeslami, Fahimeh Fazel + Show 2 more

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Journal: IEEE Access	Publication Date: Jan 1, 2021
Citations: 4	License type: CC BY 4.0

Affiliation: University of Tehran, University of Toronto

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This paper addresses the problem of Visible Light Communication (VLC)-based indoor localization and handover, where mobile users communicate with hybrid VLC/mmWave Access Points (APs). The VLC system consists of multiple Light-Emitting Diodes (LEDs) treated as VLC transmitters, multiple Photodiodes (PDs) on the user’s smart device, and multiple mmWave Radio Frequency (RF) transmitters used as complementary APs for the VLC system in the case of blockage. We propose a Convolutional Neural Network (CNN)-based algorithm consisting of offline and online modes. In the offline mode, we gather a data set by dividing the environment into fixed-sized elements where the received VLC signal along with the data attained from the smart device at each element represent a sample to train a CNN model for indoor localization. In the online mode, users employ the received VLC signals to estimate their locations. We then propose a virtual soft handover process according to the Coordinated Multiple Point (CoMP) transmission, where the HandOver Margin (HOM) and Waiting Time (WT) are dynamically set based on the change in Signal-to-Noise-Ratio (SNR) values in consecutive time slots. We derive a closed-form expression for the average effective throughput during the handover process, which shows the algorithm’s superior performance compared to conventional soft and hard handovers. Simulation results show an average positioning error of 4.31 centimeters for the proposed localization algorithm in a $5\times 4\times 3\,\,\text {m}^{3}$ smart environment.

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H IGHLY precise and robust user localization has become an inseparable part of 6G and beyond wireless networks to support various location-based services in many emerging applications such as smart homes and automatic factories [1]
PROPOSED Convolutional Neural Network (CNN)-BASED INDOOR LOCALIZATION ALGORITHM we develop a Deep Learning (DL)-based fingerprinting algorithm for user localization by employing the received Visible Light Communication (VLC) signals and the data attained from available sensors on the smart devices
To analyze the performance of the proposed handover algorithm based on the VLC indoor positioning, we derive a closed-form expression for Average Effective Throughput (AET), and we investigate the dynamical adjustment of HandOver Margin (HOM) and Waiting Time (WT) to prevent the ping-pong effect caused by the user’s speed during its movement

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H IGHLY precise and robust user localization has become an inseparable part of 6G and beyond wireless networks to support various location-based services in many emerging applications such as smart homes and automatic factories [1]. VLC yields more benefits in comparison with the RF technology, such as higher data rate, tremendous bandwidth, and higher security with fewer health risks [4]. It is more energy-efficient than RF and can be implemented using the commonly-used Light-Emitting Diodes (LEDs)

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