Radio map estimation (RME) is the task of predicting radio power at all locations in a two-dimensional area and at all frequencies in a given band. This thesis explores four deep learning approaches to RME: dual path autoencoders, skip connection autoencoders, diffusion, and joint learning with transmitter localization.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc2179342 |
| Date | 07 1900 |
| Creators | Locke IV, William Alexander |
| Contributors | Huang, Yan, Li, Xinrong, Fan, Heng |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | Text |
| Rights | Public, Locke IV, William Alexander, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved. |
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