Browsing by Author "Bouazabia, Sarah"

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    A Binary Relevance Approach for Smart Antenna Selection in Massive MIMO Systems
    (Institute of Electrical and Electronics Engineers, 2025) Bouchibane, Fatima Zohra; Boutellaa, Elhocine; Tayakout, Hakim; Cherigui, Rahma; Bouazabia, Sarah
    Future transceivers are projected to incorporate massive antenna arrays, which could significantly increase power consumption. To mitigate this challenge, the antenna selection technique (AS) emerges as a viable solution. By strategically selecting a subset of antennas, the system power consumption can be significantly reduced without compromising the overall system performance. This paper proposes a novel AS approach for massive MIMO systems under real-world channel measurements. By employing the binary relevance technique (BR), a straightforward approach to multi-label (ML) learning that tackles the problem by treating each class label as an independent binary classification task, we formulate the AS problem as a ML classification task. We conducted simulations using SVM as the base learning algorithm to assess the performance of our proposed approach and compare results to the Multi Label convolutional neural network (ML-CNN) and convex relaxation based approaches (CVX). The binary relevance based SVM (SVM-BR) performance, while slightly below the suboptimal convex relaxation approach in terms of system capacity, remains competitive with the MLCNN under different antenna array configurations
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    Antenna selection in massive MIMO using machine learning
    (Université M’hamed Bougara de Boumerdes : Institut de Genie Electrique et Electronique, 2024) Cherigui, Rahma; Bouazabia, Sarah; Boutellaa, Elhocine (Supervisor)
    In massive MIMO (Multiple Input Multiple Output) systems the overall performance (bit/s/Hz/cell) is significantly improved by equipping the base stations with arrays of a hundred antennas; which becomes one of its most significant challenges; economically and technically due to the high power consumption. To solve this, Antenna selection (AS) is increasingly gaining more interest, as it strategically reduces the hardware complexity while maximizing efficiency and throughput by selecting a specific subset of antennas to activate in each transmission slot. In this report, we examine the application of multi-label learning (MLL) based algorithms in AS, such as problem transformation methods, including first order binary relevance; and high order chain classification. Additionally, we investigate the Deep neural networks (DNN) based algorithms, namely Multi-Label Convolutional Neural Networks (MLCNN) and Multi-Layer Perceptron (MLP) classifier, and multi-View based algorithm. These proposed methods are rigorously evaluated based on their maximum capacity, performance and the computation time across various scenarios. Our work concludes that, in comparison with the convex relaxation based method, the Multi-view MLL achieves comparable results.