Channel modelling to ensure high link reliability
This study explores the application of RadioUNet, a Convolutional Neural Network (CNN) architecture, for high-reliability channel estimation. With an emphasis on high-reliability wireless communication, this research examines the usage of RadioUNet, a CNN architecture, for channel estimation. Rad...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/176399 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | This study explores the application of RadioUNet, a Convolutional Neural Network (CNN)
architecture, for high-reliability channel estimation. With an emphasis on high-reliability
wireless communication, this research examines the usage of RadioUNet, a CNN architecture,
for channel estimation. RadioUNet was selected for its creative approach to radio map
generation and signal strength estimates, utilising UNet's upsampling and downsampling for
picture segmentation.
The main objective of the project is to gather Received Signal Strength (RSS) data from several
transmitters and receivers located across NTU, train RadioUNet with simulated scenarios, and
then use that data to create precise signal strength images on NTU's map. The objective is to
increase the model's accuracy under practical circumstances, hence enhancing the
dependability of wireless channel estimation. The output of the model will be validated against
real data to create a framework for effective and trustworthy channel modelling in comparable
circumstances. |
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