Invoking deep learning for joint estimation of indoor LiFi user position and orientation

Arfaoui, Mohamed Amine and Dehghani Soltani, Mohammad and Tavakkolnia, Iman and Ghrayeb, Ali and Assi, Chadi and Safari, Majid and Haas, Harald (2021) Invoking deep learning for joint estimation of indoor LiFi user position and orientation. IEEE Journal on Selected Areas in Communications, 39 (9). pp. 2890-2905. 9374979. ISSN 0733-8716 (https://doi.org/10.1109/JSAC.2021.3064637)

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Abstract

Light-fidelity (LiFi) is a fully-networked bidirectional optical wireless communication (OWC) technology that is considered as a promising solution for high-speed indoor connectivity. In this paper, the joint estimation of user 3D position and user equipment (UE) orientation in indoor LiFi systems with unknown emission power is investigated. Existing solutions for this problem assume either ideal LiFi system settings or perfect knowledge of the UE states, rendering them unsuitable for realistic LiFi systems. In addition, these solutions consider the non-line-of-sight (NLOS) links of the LiFi channel gain as a source of deterioration for the estimation performance instead of harnessing these components in improving the position and the orientation estimation performance. This is mainly due to the lack of appropriate estimation techniques that can extract the position and orientation information hidden in these components. In this paper, and against the above limitations, the UE is assumed to be connected with at least one access point (AP), i.e., at least one active LiFi link. Fingerprinting is employed as an estimation technique and the received signal-to-noise ratio (SNR) is used as an estimation metric, where both the line-of-sight (LOS) and NLOS components of the LiFi channel are considered. Motivated by the success of deep learning techniques in solving several complex estimation and prediction problems, we employ two deep artificial neural network (ANN) models, one based on the multilayer perceptron (MLP) and the second on the convolutional neural network (CNN), that can map efficiently the instantaneous received SNR with the user 3D position and the UE orientation. Through numerous examples, we investigate the performance of the proposed schemes in terms of the average estimation error, precision, computational time, and the bit error rate. We also compare this performance to that of the k-nearest neighbours (KNN) scheme, which is widely used in solving wireless localization problems. It is demonstrated that the proposed schemes achieve significant gains and are superior to the KNN scheme.

ORCID iDs

Arfaoui, Mohamed Amine, Dehghani Soltani, Mohammad, Tavakkolnia, Iman ORCID logoORCID: https://orcid.org/0000-0003-4736-1949, Ghrayeb, Ali, Assi, Chadi, Safari, Majid and Haas, Harald;
  • Item type:Article
    ID code:76247
    Dates:
    Date
    Event
    30 September 2021
    Published
    10 March 2021
    Published Online
    28 January 2021
    Accepted
    Notes:© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:28 Apr 2021 09:03
    Last modified:20 Nov 2024 19:35
    URI:https://strathprints.strath.ac.uk/id/eprint/76247
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