Syed Ali Abbas
Machine Learning in 5G/6G Networks: Assessing Deep Neural Network Performance for Sustainable Mobile Communication.
Rel. Carla Fabiana Chiasserini. Politecnico di Torino, Master of science program in Data Science And Engineering, 2023
|
Preview |
PDF (Tesi_di_laurea)
- Thesis
Licence: Creative Commons Attribution Non-commercial No Derivatives. Download (501kB) | Preview |
Abstract
The advancement of telecommunication networks, marked by the transition from 5G to 6G technologies, indicates a significant transformation in how we connect, communicate, and spread information. In this thesis we investigate the deep integration of machine learning (ML) and deep neural networks (DNN) within this technological change, outlining their pivotal role in strengthening modern communication networks. The research offers a detailed overview of the strides in 5G/6G technologies and emphasises the critical role of ML in enhancing communication infrastructures to meet escalating data traffic needs. A specific focus is placed on NVIDIA's contributions, mainly through their Sionna library. This tool not only serves as a vital link in connecting 5G Physical layer simulations with advanced ML toolkits but also showcases Nvidia’s commitment to leading innovations in telecommunications.
An integral component of this research is the exploration of pruning techniques within neural networks
Relators
Academic year
Publication type
Number of Pages
Course of studies
Classe di laurea
Aziende collaboratrici
URI
![]() |
Modify record (reserved for operators) |
