Article
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Machine Learning Based Indoor Localization Under Shadowing Condition for P-NOMA VLC Systems
Version 1
: Received: 4 May 2023 / Approved: 5 May 2023 / Online: 5 May 2023 (10:10:47 CEST)
A peer-reviewed article of this Preprint also exists.
Affan, A.; Asif, H.M.; Tarhuni, N. Machine-Learning-Based Indoor Localization under Shadowing Condition for P-NOMA VLC Systems. Sensors 2023, 23, 5319. Affan, A.; Asif, H.M.; Tarhuni, N. Machine-Learning-Based Indoor Localization under Shadowing Condition for P-NOMA VLC Systems. Sensors 2023, 23, 5319.
Abstract
The communication link between the base station and the mobile sensor networks, such as multi-agent systems for collaborative tasks which include ground mobile robots and drones, is crucial for localization and success of tasks in indoor environments. The Power-domain Non-Orthogonal Multiple Access (P-NOMA) is an emerging multiplexing technique that enables the base station to accumulate signals for different agents using the same time-frequency channel. The environment information such as distance from the base station is required at the base station to calculate communication channel gains and allocate suitable signal power to each agent. The accurate estimate of the position for power allocation of P-NOMA in a dynamic environment is challenging due to changing location of the end-agent and shadowing. In this paper, we take advantage of the two-way Visible Light Communication (VLC) link to: (1) estimate the position of the end-agent in a real-time indoor environment based on the signal power received at the base station and (2) allocate resources using the Simplified Gain Ratio Power Allocation (S-GRPA) scheme with the look-up table method. In addition, we use the Euclidean Distance Matrix (EDM) to estimate the location of the end-agent whose signal was lost due to shadowing.
Keywords
Visible Light Communication; Machine Learning; SIC; NOMA; Localization; Shadowing
Subject
Engineering, Electrical and Electronic Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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