DSpace at UMBB
The institutional repository of the University M'Hamed Bougara Of Boumerdes is a digital archive that contains the scientific output of the University. Dspace manages, preserves and provides access to the academic works of UMBB.
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Recent Submissions
Design, simulation, and performance assessment of MPLS core networks
(University M’hamed Bougara : Institute of Electrical and Electronic Engineering (IGEE), 2025) Baouali, Chems Eddine; Benzaoui, Messaouda
With the continuous growth of the Internet and the increasing diversity of user demands, service providers face the challenge of delivering fast, reliable, and secure communication across large-scale networks. Multi-Protocol Label Switching (MPLS) has become a cornerstone technology in modern backbone networks because it goes beyond traditional IP routing by combining flexibility, efficiency, and support for advanced services.
This project explores the design and simulation of an MPLS-based core network using GNS3, focusing on how Internet Service Providers (ISPs) can manage complex customer requirements while ensuring performance and scalability. Provider Edge (PE) routers are considered as the key control points, enabling customer isolation through Virtual Routing and Forwarding (VRF), resolving IP address overlaps with external BGP (eBGP), and ensuring seamless communication across multiple domains. While, Label Distribution Protocol (LDP) is used to establish label-switched paths.
The findings show that MPLS is not only a powerful forwarding technique but also a strategic enabler for ISPs to deliver advanced services with efficiency and reliability. Future work will emphasize the deeper integration of QoS and MPLS-TE, providing a more adaptive and intelligent backbone capable of meeting the ever-growing demands of
next-generation networks.
Advanced classification methods applied to ECG signals
(University M’hamed Bougara : Institute of Electrical and Electronic Engineering (IGEE), 2025) Merdji, Fadia; Belkadi, Mohamed Amine
This work presents an advanced automated ECG classificatio nsyste mtha taddresse scritical challenges in cardiac arrhythmia diagnosis by integrating sophisticated signal processing with state-of-the-art deep learning techniques. Beginning with a thorough examination of cardiac electrophysiology, ECG waveform characteristics, and noise artifacts, the research establishes a robust foundation for subsequent algorithmic development. The study systematically evaluates traditional machine learning methods (SVM, KNN, DNN) and modern deep learning archi-
tectures, culminating in an innovative multi-stage framework featuring optimized Butterworth filtering ,hybri dFourie rwavele tfeatur eextraction ,an d aspecialize d2 DCN Ndesign .Exten-sive validation on the MIT-BIH Arrhythmia Database demonstrates exceptional performance, achieving 98.60% classificatio naccurac yan d99 %precisio nfo rcritica larrhythmia swhil ere-vealing important insights about minority class recognition challenges. The work makes three key contributions: (1) a comprehensive theoretical and methodological framework for ECG analysis, (2) significan tperformanc eimprovement sthroug hmultimoda lfeatur efusion ,an d(3) practical guidelines for clinical implementation. Future research directions focus on real-time processing optimization, attention mechanism integration, and multi-modal data fusion to en-hance diagnostic capabilities across diverse healthcare environments, representing a substantial
advancement in intelligent cardiac monitoring technology with the potential for significant clinical impact.
Implementation and evaluation of conjugate gradient-based MIMO detectionin sionna for 5G and 6G-like scenarios
(University M’hamed Bougara : Institute of Electrical and Electronic Engineering (IGEE), 2025) Tahir, Imene; Saoudi, Amina; Smaili, Nessrine
As wireless communication systems progress toward higher spectral effi ciency and support for massive connectivity, robust detection techniques for Multiple-Input Multiple-Output systems have become increasingly important. While traditional linear detectors such as Zero-Forcing and Linear Minimum Mean Square Error are simple to implement, their
performance can degrade in large-scale scenarios due to high computational cost or limited accuracy. To overcome these challenges, the Conjugate Gradient algorithm has gained attention as an effi cient iterative method for solving linear systems without requiring matrix inversion, making it highly suitable for massive MIMO detection. In this work, we implement and evaluate a CG-based linear MIMO detector using Sionna , an open- source link-level simulation library developed on TensorFlow. The system is assessed using performance metrics such as Bit Error Rate versus Energy per Bit to Noise Power Spectral Density across different modulation schemes and channel conditions. The results confirm that CG provides competitive detection performance with reduced complexity, making it a promising candidate for scalable and machine learning-oriented wireless system design.
Performance study of hybrid OFDM/OTFS system over doubly selective channel
(University M’hamed Bougara : Institute of Electrical and Electronic Engineering (IGEE), 2025) Dihia, Amani; Razaoui, Feriel; Smaili, Nesrine
As the demand for fast and reliable wireless communication grows particularly in high-mobility environments, traditional modulation schemes are limited by Doppler shifts.
Orthogonal Frequency Division Multiplexing (OFDM) performs effectively in low mobility scenarios, but experiences performance degradation under dynamic conditions. On the other hand, Orthogonal Time Frequency Space (OTFS) modulation offers greater robustness in high mobility channels due to its operation in the Delay Doppler domain,
but it introduces greater complexity and does not outperform OFDM in low mobility channels. This work proposes a hybrid system that dynamically estimates user velocity and switches between OFDM and OTFS to maintain optimal performance. MATLAB simulations evaluating the Bit Error Rate (BER) versus Signal-to-Noise Ratio (SNR) show that OTFS outperforms OFDM at higher speeds, while OFDM remains more effi cient in static or low mobility settings. The adaptive solution ensures effective communication under various mobility conditions and supports the waveform fl exibility for future wirelessnetworks.
Etude de l’impact de la catégorie de site sur la réponse sismique d’un bâtiment (Sous-sol + RDC + 7 étages) en utilisant les règles parasismiques algériennes (RPA 2024)
(Université M'Hamed Bougara Boumerdes: Faculté de Technologie, 2025) Aliouane, Mohamed Imad; Assabane, Imed Eddine; Zarga, Djaloul(Promoteur)
Dans ce mémoire, on étudie l’impact de la catégorie de site sur la réponse sismique d’un bâtiment composé d’un sous-sol, d’un rez-de-chaussée et de sept étages. Cette étude, réalisée en utilisant le nouveau règlement de calcul RPA 2024 et les normes de vérification du béton armé (BAEL 91
modifié 99 et CBA 93), Elle débute par la description générale du projet, suivie de la présentation des caractéristiques des matériaux. Elle comprend également le pré-dimensionnement de la structure, la descente des charges, ainsi que le calcul des éléments principaux et secondaires
(poutrelles, escaliers, acrotère, balcon, planchers et ascenseur). L'étude dynamique de la structure a été réalisée avec le logiciel ETABS afin de déterminer les sollicitations générées par les différents chargements (charges permanentes, d'exploitation et charge sismique). Nous avons étudié l'effet des catégories de site sur l'effort tranchant dynamique ainsi que sur les moments. De plus, l'effort tranchant a été analysé selon les règlements RPA99 v2003 et RPA 2024, puis les résultats ont été comparés. Enfin, une conclusion générale termine le travail