| Titre : | Machine Learning Techniques for THz Channel Estimation |
| Auteurs : | KHADIR Fatma Hadil, Auteur ; SADOUN Daouia, Auteur ; TAMI Abdelkader, Auteur |
| Type de document : | texte manuscrit |
| Editeur : | [S.l.] : Université Saïda – Dr. Tahar Moulay – Faculté des Mathématiques, de l’Informatique et de Télécommunications, 2024/2025 |
| Format : | 93 ص |
| Accompagnement : | CD |
| Langues: | Anglais |
| Index. décimale : | BUC-M 003675 |
| Catégories : | |
| Mots-clés: | Keywords: 6G wireless communication: Terahertz (THz) frequency band: channel estimation algorithms: Logistic Regression (LR): Projected Gradient Ascent (PGA): Deep Neural Networks (DNN) |
| Résumé : |
Terahertz (THz) communications is considered one of the most
promising wireless technologies for the sixth generation (6G) and beyond. A fundamental challenge for the practical deployment of THz systems is accurate channel estimation, due to the unique propagation characteristics of THz frequencies. In this context, we address the problem of channel modeling and estimation by considering deterministic propagation and the physical characteristics specific to THz bands. We also explore the application of machine learning algorithms for THz channel estimation, including Neural Networks (NN), Logistic Regression (LR), and Projected Gradient Ascent (PGA). we provide a clear explanation of machine learning and deep learning, introducing the three main types of machine learning: supervised, unsupervised. The objective is to offer a comprehensive understanding of what machine learning truly is and why it is essential. Furthermore, In the final chapter, we present simulation results showcasing a recent class of radio channel estimation based on Deep Neural Networks (DNN), which differs fundamentally from the classical channel estimation algorithms previously discussed. |
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Documents numériques (1)
BUC-M 003675 Adobe Acrobat PDF |
