Communications Internationales

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

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Author: search.filters.author.Meraihi, YassineHas files: Yes

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    Hybrid whale optimization algorithm with simulated annealing for the UAV placement problem
    (Springer Nature, 2024) Taleb, Sylia Mekhmoukh; Meraihi, Yassine; Yahia, Selma; Ramdane-Cherif, Amar; Gabis, Asma Benmessaoud; Acheli, Dalila
    This chapter suggests a hybrid algorithm based on the combination of whale optimization algorithm (WOA) with simulated annealing (SA), called WOA-SA, for solving the unmanned aerial vehicle (UAV) placement problem. WOA-SA combines WOA’s global search functionality with SA’s local search functionality. The main objective of our work is to determine the optimal position of the UAV in order to maximize the total throughput, depending on a given set of user locations and traffic demands. The WOA-SA algorithm is validated in terms of the total throughput using 18 distinct instances with various numbers of users, taking into account the effect of the distribution of user positions. The results of simulation using Matlab demonstrated that the WOA-SA algorithm obtains better results than WOA, SA, Particle Swam Optimization (PSO), Genetic Algorithm (GA), and Bat Algorithm (BA).
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    An Enhanced white shark optimization algorithm for unmanned aerial vehicles placement
    (Springer Nature, 2024) Saadi, Amylia Ait; Soukane, Assia; Meraihi, Yassine; Gabis, Asma Benmessaoud; Ramdane-Cherif, Amar; Yahia, Selma
    In this chapter, we propose an Elite Opposition-Based White Shark Optimization (ELWSO) Algorithm, for tackling the Unmanned Aerial Vehicles (UAVs) Placement problem in smart cities. The proposed EWSO scheme is based on the incorporation of the Elite opposition-based strategy to ameliorate the optimization efficiency of the original WSO. EWSO was assessed in terms of fitness, coverage, and connectivity metrics under 23 cases with different numbers of UAVs and users. The results of simulated experiments, conducted using MATLAB 2021b version, revealed that the EWSO algorithm outperforms the basic WSO, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Bat Algorithm (BA).
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    Speech analysis–synthesis using sinusoidal representations: A review
    (Springer Nature, 2024) Tabet, Youcef; Hina, Manolo Dulva; Meraihi, Yassine
    Various speech analysis–synthesis representations have been suggested in the literature, and the more well-known ones are explored in this chapter, specifically, sinusoidal representation, harmonic/noise representation, and adaptive sinusoidal representations. Hence, the main objective of this chapter is to give a tutorial review of speech analysis–synthesis representations, by highlighting major improvements over these representations. It would be a desirable representation of speech that is relatively simple, flexible, high quality, and robust in re-synthesis. Emphasis will be given in adaptive sinusoidal representations, since they seem to be more promising and robust representations of speech.
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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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    Multi-Directional Vehicle-To-Vehicle Visible Light Communication With Angular Diversity Technology
    (IEEE, 2020) Yahia, Selma; Meraihi, Yassine; Benmessaoud Gabis, Asma; Ramdane-Cherif, Amar
    In this paper, we investigate the performance of the multi-directional vehicle-to-vehicle (V2V) visible light communication (VLC) system by deploying the angle diversity technology. We consider a road with a multi-lanes configuration where multiple cars utilize their headlights to transmit the signals to a receiving car. Such car uses an angle diversity receiver consisting of three photodetectors (PDs) and oriented in different directions. The effect of the field-of-view angle (FoV) of the PD same as the semi-half angle of the transmitter on the received signal-to-noise-ratio (SNR) are investigated. We also investigate the impact of higher data rates on the system performance. The results show that the system can achieve an SNR higher than 13.6 dB at a transmission distance of 50 m. Such SNR value is required to achieve a reliable communication system with bit error rate (BER) less than 10 −3 .
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    Heuristic and learning method for obstacle avoidance with mobile robot
    (IEEE, 2020) Lachekhab, Fadhila; Acheli, Dalila; Tadjine, Mohamed; Meraihi, Yassine
    In this paper, a fuzzy controller obstacle avoidance of the mobile robot Pioneer II is proposed. The fuzzy inference system FIS of this controller is performed by two methods: heuristic and reinforcement learning. the manual tuning of the fuzzy control system can be long and difficult. In contrast, reinforcement learning has proven theoretically and practically its ability to automatically optimize some parameters of the FIS. For that, the Fuzzy Actor-Critic Learning algorithm allows the determination of the parameters of the conclusions among of an available set fixed by the operator. The proposed algorithm allows the automatic determination of the parameters of the conclusions of the fuzzy rules. The simulations show that the two controllers (heuristic, RL controller) are able to avoid the different shapes of obstacles contained in known environments, and they show exceptionally good robustness when changing the environment (shape of obstacles, location of obstacles in the environment