Communications Internationales

Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/11

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Author: search.filters.author.Meraihi, YassineSubject: search.filters.subject.Visible Light Communications

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    An enhanced aquila-based resource allocation for efficient indoor IoT visible light communication
    (Institute of Electrical and Electronics Engineers Inc., 2023) Yahia, Selma; Meraihi, Yassine; Taleb, Sylia Mekhmoukh; Mirjalili, Seyedali; Ramdane-Cherif, Amar; Ho, Tu Dac; Eldeeb, Hossien B.; Muhaidat, Sami
    Visible light communication (VLC) is a rapidly growing wireless communication technology for the Internet of Things (IoT) that offers high data rates and low latency, making it ideal for massive connectivity. Efficient resource allocation is essential in VLC networks to minimize inter-symbol and cochannel interferences, which can greatly improve network performance and user satisfaction. This paper focuses on an indoor IoT-based VLC system that utilizes photodetectors (PDs) on users' cell phones as receivers, with the goal of maximizing system performances and reducing power consumption by selectively activating some PDs while deactivating others. However, this objective presents a challenge due to the inherent non-convex nature of the multi-objective optimization problem, which cannot be solved by analytical means. To address this, we propose an enhanced Aquila optimization (EAO) scheme that improves upon the Aquila Optimizer (AO) by incorporating a fitness distance balance (FDB) function. We evaluate our proposed EAO in various scenarios under different settings, considering both capacity and fairness metrics. Through simulations, we demonstrate the effectiveness of our approach and its superiority over classical algorithms such as Aquila Optimizer (AO), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO) in finding the optimal solution. Our results confirm that the proposed EAO algorithm can efficiently optimize the system capacity and ensure fairness among all users, providing a promising solution for indoor VLC systems
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    Manta ray foraging optimization algorithm for solving the LEDs placement problem in indoor VLC systems
    (IEEE, 2022) Yahia, Selma; Meraihi, Yassine; Sadeki, Nesrine; Tellache, Mohamed; Mekhmoukh Taleb, Sylia; Refas, Souad; Benmessaoud Gabis, Asma
    Visible light communication is an excellent alternative to traditional Radio Frequency (RF) technology where many problems have been detected including saturation of the RF spectrum, making wireless communication unable to support the high demand of wireless connections. VLC allows communication between users using Light Emitting Diodes (LEDs) as transmitters and Photo-detectors (PDs) as receivers. The deployment of LEDs in indoor VLC Systems is an important issue that affects the coverage of the network. In this paper, we applied the Manta Rays Foraging Optimization (MRFO) algorithm for tackling the LEDs placement problem in indoor VLC systems. We formulate an optimization problem with the goal of maximizing coverage, throughput while ensuring the network requirements. The performance of MRFO was validated in terms of user coverage and throughput compared to Whale Optimization Algorithm (WOA), Bat Algorithm (BAT), and Particle Swarm Optimization (PSO). The simulation results demonstrated that MRFO is more effective than WOA, BAT, and PSO in determining optimal placement of LEDs