README.md

---
license: cc-by-nc-nd-4.0
task_categories:
- feature-extraction
language:
- en
---
# RadioModRec-1

<!-- Provide a quick summary of the dataset. -->

RadioModRec-1 is an Automatic Modulation Recognition (AMR) simulated dataset carefully curated for fifteen digital modulation schemes consisting of 4QAM, 16QAM, 64QAM, 256QAM, 8PSK, 16PSK, 32PSK, 64PSK, 128PSK, 256PSK, CPFSK, DBPSK, DQPSK, GFSK, and GMSK whose usefulness is predominantly found in modern wireless communication systems. RadioModRec-1 dataset caters for the Rayleigh and the Rician channel models under the Additive White Gaussian Noise (AWGN) from -20dB to +20dB at a step of +5dB.

### Dataset Description

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- **Curated by:** [Emmanuel Adetiba and Jamiu R. Olasina]
- **Funded by:** [Part Funding by Google Award for TensorFlow Outreaches in Colleges]
- **Language(s) (AMC):** [Automatic Modulation Recognition]
- **License:** [cc-by-nc-nd-4.0]

## Uses

RadioModRec-1 is a vital resource for state-of-the-art Automatic Modulation Recognition (AMR) research in Software Defined and Cognitive Radio Systems.<!-- Address questions around how the dataset is intended to be used. -->

## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
Emmanuel Adetiba and Jamiu R. Olasina, RadioModRec: A Dataset for Automatic Modulation Recognition in Software Defined and Cognitive Radio Research.


## Dataset Card Authors [optional]

Emmanuel Adetiba

## Dataset Card Contact

[email protected]
[email protected]