Telecommunication
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Item 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.Item Hyperspectral image classification a comparative analysis of machine learning and deep learning approaches(Université M’hamed Bougara de Boumerdes : Institut de Genie Electrique et Electronique, 2024) Rekia, Lotfi; Sandjak, Soumia; Daamouche, ,Abdelhamid ( Supervisor).In recent years, remote sensing has become a highly interesting field due to its diverse applications. Among these, hyperspectral imaging stands out because of the vast amount of data contained within its hundreds of spectral bands and spatial features, making it highly effective for remote object identification, which is the focus of this work. Given its utility and necessity across various applications, it has become inevitable to delve deeper into this technology. However, handling such a vast and complex amount of data presents significant challenges. That’s why we proposed several methods to process this data by extracting the spectral and spatial features contained in hyperspectral images for pixel classification and, consequently, object identification with the highest accuracy, which is our ultimate goal. Firstly, we explored traditional machine learning methods, specifically K-Nearest Neighbors and Support Vector Machines, and demonstrated their limitations in Hyperspectral image classification. We then turned to more advanced deep learning methods, particularly convolutional neural networks. These techniques act similarly to the human brain, which is a very powerful tool for managing large and complex data. Our first approach focused on models that handle only spectral information of the HSI, specifically 1D-CNN, along with two additional techniques which are FFT and the Atrous algorithm to improve the processing and, hence, the accuracy of the models using only spectral information. The second approach dealt with the spatial features of the HSI using a 2D-CNN. The final method combined the processing of both spectral and spatial features while using a 3D-CNN. All the models were tested on two datasets, namely the Indian Pines and Pavia University datasets. The classification and identification results of our models on these datasets were promising, where the best performer was 3D-CNN with accuracy of 99.88 .