# KristynaPijackova/Radio-Modulation-Recognition-Networks

<https://repo.masters-masters.com/en/repos/KristynaPijackova/Radio-Modulation-Recognition-Networks>

[Home](https://repo.masters-masters.com/en) › [KristynaPijackova](https://repo.masters-masters.com/en/owners/KristynaPijackova) › [Radio-Modulation-Recognition-Networks](https://repo.masters-masters.com/en/repos/KristynaPijackova/Radio-Modulation-Recognition-Networks)

KristynaPijackova/Radio-Modulation-Recognition-Networks has 113 stars on GitHub. Its RepoMasters card: Prototype tier. Radio modulation recognition with CNN…

| Field | Value |
| --- | --- |
| Owner | [KristynaPijackova](https://repo.masters-masters.com/en/owners/KristynaPijackova) |
| Stars | 113 |
| Forks | 25 |
| Language | [Jupyter Notebook](https://repo.masters-masters.com/en/languages/jupyter-notebook) |
| License | MIT |
| Created | 2020 |
| Topics | modulation-classification, modulation-recognition, radio-modulation-classification, deepsig, rf-signals, radio-modulation-recognition |
| Tier | [Prototype (100 to 249 ★)](https://repo.masters-masters.com/en/ranks/rush) |
| View on GitHub | [KristynaPijackova/Radio-Modulation-Recognition-Networks](https://github.com/KristynaPijackova/Radio-Modulation-Recognition-Networks) |

## Repository

Radio modulation recognition with CNN, CLDNN, CGDNN and MCTransformer architectures. Best results were achieved with the CGDNN architecture, which has roughly 50,000 parameters, and the final model has a memory footprint of 636kB. More details can be found in my bachelor thesis linked in the readme…

The tier for repos with 100 to 249 ★. Published odds per card pulled: 70%. Rarity: Common.

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