Etd
Enhancing Wireless Technologies with Machine Learning
Público DepositedContenido Descargable
open in viewerThis thesis explores ML applied to wireless technologies in two different contexts: 5G cellular networks and radar systems. In the case of 5G, classification ML models are used to identify fundamental scheduling algorithms that a simulated network is using based on a several types of data (UE performance metrics and spectrogram data). As for radar systems, a monostatic radar simulation was built, opening opportunities for future cognitive radar experiments involving adaptive NLFM waveforms via optimization. This thesis exemplifies the importance of ML applied to wireless emissions and sets up future research in the two domains considered.
- Creator
- Colaboradores
- Degree
- Unit
- Publisher
- Identifier
- etd-107931
- Palabra Clave
- Advisor
- Defense date
- Year
- 2023
- Sponsor
- Date created
- 2023-05-02
- Resource type
- Source
- etd-107931
- Rights statement
- Última modificación
- 2023-06-07
Las relaciones
- En Collection:
Elementos
Elementos
Miniatura | Título | Visibilidad | Embargo Release Date | Acciones |
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MS_Thesis__4_.pdf | Público | Descargar |
Permanent link to this page: https://digital.wpi.edu/show/c247dw39j