Browsing by Author "Bouchibane, Fatima Zohra"

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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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    Automatic modulation recognition in two- way relaying channel with the presence of relay transceiver hardware impairments
    (Springer Nature, 2023) Tayakout, Hakim; Bouchibane, Fatima Zohra; Boutellaa, Elhocine; Dayoub, Iyad
    Modulation recognition (MR) is well-known as one of the enabling tools, which aims to further afford efficient and secure communica- tions in cognitive radio (CR) context for 5G and beyond networks. In this Paper, we propose a robust MR approach designed for two-way MIMO cooperative relaying network by specifically taking into account two crucial constraints: hardware impairments at relay transceiver and co-channel interference. To mitigate the effect of these impairments, feature-based artificial neural network (ANN) recognizer combined with a nonlinear design of the relay processing matrix with dirty paper cod- ing (DPC) is investigated in this work. Simulations using two receive criterions, namely DPC-ZF and DPC-MMSE, have been carried out to validate the effectiveness of the proposed approach. It has been