Invoking Deep Learning for Joint Estimation of Indoor LiFi User Position and Orientation

Arfaoui, Mohamed Amine; Soltani, Mohammad; Tavakkolnia, Iman; Ghrayeb, Ali; Assi, Chadi; Safari, Majid; Haas, Harald

doi:10.1109/jsac.2021.3064637

Cited by 48 publications

(43 citation statements)

References 54 publications

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“…Where the blue color represents the LED location and the red color represents the user location that was calculated by RSS -Triangulation, while the green color shows the user location that was as the noise equations were explained in section 2.4, Eqs. ( 15), ( 16) and (17). Fig.…”

Section: Simulation Results Of Rss-triangulation Methodsmentioning

confidence: 97%

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Indoor Positioning Systems Based on Li-Fi Technology Using RSS-Triangulation With Assisted by DNN

2022

IJIES

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Due to the rapid growth in mobile data traffic, academia and industry are considering Li-Fi (Light Fidelity) as an alternative to Wi-Fi (Wireless Fidelity). Li-Fi is a high-speed, two-way wireless network system that has several advantages over traditional radio frequency systems. To fully exploit the potential of Li-Fi systems, users must be provided with a seamless connection, which must be achieved by carefully studying user mobility. Nowadays, most wireless Li-Fi network applications focus their design on solving some unspecific user-location problems that may fully achieve some preferred results in the domain they serve. For example, locating an unknown user in an indoor environment is considered a kind of solution for maintaining the QoS (Quality of Service) and delivering the signal to the user without distraction. Where the main research problem lies in the signal interference, loss, and scattering in the Li-Fi network, it is a big problem because it reduces the network performance and coverage. Therefore, to solve this problem, this research investigates the locating of the mobile user within the Li-Fi equipped room to obtain the best possible coverage of the user's service. The aim of the research was divided into two stages. In the first stage, the method used in our work was presented by showing the triangulation method in simulating user location data (sensor simulation), the first stage was not affected by noise, as it serves as proof of the correctness of our work and depends in the second stage when it is under the influence the noise. In the second stage, the method of RSS -triangulation (Receiving Signal Strength -Triangulation) was adopted to simulate the user's location data (a simulation of the sensor) and under the influence of noise and implemented on a deep learning algorithm to determine the accuracy of the user's location and how to deal with noise. In the simulation results for the data in the first stage, our work was validated using the triangulation method in data simulation. The average error for the X-Axis was 2.17344 × 10 −14 cm; Y-Axis was 6.44762 × 10 −14 cm and Z-Axis 4.65690 × 10 −11 cm the results obtained from the triangulation simulation were close to the real results. In the second stage, when adopting the RSS-Triangulation method under effect the noise, the average error was achieved on the X-axis 2.05612 × 10 −3 cm, Y-Axis was 4.56249 × 10 −3 cm, and Z-Axis 5.10474 × 10 −3 cm where The DNN (Deep Neural Network) showed how to deal with noise, the highest error was obtained on the X-axis 2.51535 cm, the Y-axis 2.25903 cm, and the Z-axis 4.22898 cm for an indoor environment of 5m x 5m x 3m

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Section: Simulation Results Of Rss-triangulation Methodsmentioning

confidence: 97%

“…Figure 17. The actual user location and the estimated user location using DNN for testing destines data…”

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confidence: 99%

Indoor Positioning Systems Based on Li-Fi Technology Using RSS-Triangulation With Assisted by DNN

2022

IJIES

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“…Unlike in conventional radio frequency (RF) wireless systems, the OWC channel is not isotropic, meaning that the orientation of both optical transmitters and receivers affects the wireless channel gain significantly [18]. Due to this, in the context of LiFi technology, the joint knowledge of UE position and orientation is a crucial factor for channel estimation and resource management tasks [19]. For this purpose, novel and accurate position and orientation estimation solutions were recently proposed in the literature [19]- [21].…”

Section: B Recent Advances and Limitationsmentioning

confidence: 99%

“…Due to this, in the context of LiFi technology, the joint knowledge of UE position and orientation is a crucial factor for channel estimation and resource management tasks [19]. For this purpose, novel and accurate position and orientation estimation solutions were recently proposed in the literature [19]- [21]. An inertial measurement unit (IMU) was required in [20] to measure the UE tilt angle for position estimation.…”

Section: B Recent Advances and Limitationsmentioning

confidence: 99%

“…This approach is based on the received signal strength (RSS) technique where an iterative algorithm for jointly estimating the UE position and orientation was developed. In [19], a deep learning (DL) based joint position and orientation estimation technique was proposed. Specifically, two different artificial neutral networks (ANNs), which are a multiple layer perceptron (MLP) and a convolution neural network, were designed and optimized to estimate the 3D position and orientation of a LiFi UE based on its RSS values.…”

Section: B Recent Advances and Limitationsmentioning

confidence: 99%

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Deep Learning Based Proactive Optimization for Indoor LiFi Systems with Channel Aging

Arfaoui¹,

Ghrayeb²,

Assi³

et al. 2021

Preprint

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This paper investigates the channel aging problem of light-fidelity (LiFi) systems. In the LiFi physical layer, the majority of the optimization problems for mobile users are nonconvex and require the use of dual decomposition or heuristics techniques. Such techniques are based on iterative algorithms, and often, cause a high processing time at the physical layer. Hence, the obtained solutions are no longer optimal since the LiFi channels are evolving. In this paper, a proactive-optimization approach that can alleviate the LiFi channel aging problem is proposed. The core idea is to design a long-short-termmemory (LSTM) network that is capable of predicting posterior positions and orientations of mobile users, which can be then used to predict their channel coefficients. Consequently, the obtained channel coefficients can be exploited for deriving nearoptimal transmission-schemes prior to the intended service-time, which enables real-time service. Through various simulations, the performance of the designed LSTM model is evaluated in terms of prediction accuracy and time. Finally, the performance of the proposed PO approach is investigated in the sum rate maximization problem of multiuser cell-free LiFi systems with quality-of-service constraints, where a performance gap of less than 7% is achieved, while eliminating up to 100% of the online processing-time.

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Art Image Generation System Based on Artificial Intelligence

Cheng

2024

Lecture Notes in Electrical Engineering

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Invoking Deep Learning for Joint Estimation of Indoor LiFi User Position and Orientation

Cited by 48 publications

References 54 publications

Indoor Positioning Systems Based on Li-Fi Technology Using RSS-Triangulation With Assisted by DNN

Indoor Positioning Systems Based on Li-Fi Technology Using RSS-Triangulation With Assisted by DNN

Deep Learning Based Proactive Optimization for Indoor LiFi Systems with Channel Aging

Art Image Generation System Based on Artificial Intelligence

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