Project Details

Description

This project aims at deriving new communication techniques based on deep learning to improve the overall performance of satellite communication systems. The special focus is on the reduction of peak-to-average power ratio (PAPR) and nonlinear distortion of multi-subcarrier signals to gain high power efficiency in the emerging large-scale low Earth orbit (LEO) satellite constellations. Autoencoder architectures using deep neural networks (DNNs) will be adopted as the main solution to achieve the goal.
StatusActive
Effective start/end date15/09/202014/09/2023

Fingerprint

Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.