Deep Learning-Based Spectrum Sensing for Cognitive Radio Applications
Sara E Abdelbaset, Hossam M Kasem, Ashraf A Khalaf, Amr H Hussein, Ahmed A Kabeel, Jari Nurmi, Dimitrie Popescu · 2024
Read Deep Learning-Based Spectrum Sensing for Cognitive Radio Applications by Sara E Abdelbaset, Hossam M Kasem, Ashraf A Khalaf, Amr H Hussein, Ahmed A Kabeel, Jari Nurmi, Dimitrie Popescu (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11679419.txt.
Description
Sara Abdelbaset and a team of co-authors present a smarter way to hunt for free airwaves in this twenty twenty-four paper. Smart radios need to spot unused bands before they can use them. Older methods pull fixed traits from signals at set points and can miss or misread bands.
The team treats the task as sorting signals into classes and trains a deep learning model on many signal and noise types. The model learns the traits on its own and adapts to new signals. In tests it beat two older methods and stayed accurate even with heavy background noise. Better sensing means less wasted airwave space for wireless systems.
Catalog record
| CREATOR | Sara E Abdelbaset, Hossam M Kasem, Ashraf A Khalaf, Amr H Hussein, Ahmed A Kabeel, Jari Nurmi, Dimitrie Popescu |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 37 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |