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aspmirlab/RadioModRec-1

RadioModRec-1 is a simulated dataset for Automatic Modulation Recognition (AMR) comprising fifteen digital modulation schemes, including 4QAM, 16QAM, 64QAM, 256QAM, 8PSK, 16PSK, 32PSK, 64PSK, 128PSK, 256PSK, CPFSK, DBPSK, DQPSK, GFSK and GMSK, which are widely used in modern wireless communication systems. The dataset supports Rayleigh and Rician channel models and additive white Gaussian noise (AWGN) conditions ranging from –20 dB to +20 dB in 5 dB steps. It was curated by Emmanuel Adetiba and Jamiu R. Olasina, with partial funding from Google’s TensorFlow Outreaches in Colleges program.

Updated 1/13/2024
hugging_face

Description

RadioModRec-1 Dataset Overview

Dataset Introduction

RadioModRec-1 is a simulated dataset for Automatic Modulation Recognition (AMR) comprising fifteen digital modulation schemes, including 4QAM, 16QAM, 64QAM, 256QAM, 8PSK, 16PSK, 32PSK, 64PSK, 128PSK, 256PSK, CPFSK, DBPSK, DQPSK, GFSK and GMSK. These modulation schemes play a critical role in modern wireless communication systems. The dataset is applicable to Rayleigh and Rician channel models and operates under additive white Gaussian noise (AWGN) conditions with signal‑to‑noise ratios from -20 dB to +20 dB in 5 dB increments.

Dataset Description

  • Curators: Emmanuel Adetiba and Jamiu R. Olasina
  • Funding: Partially funded by Google’s TensorFlow Colleges Outreach award
  • Language (AMC): Automatic Modulation Recognition
  • License: cc-by-nc-nd-4.0

Use Cases

RadioModRec-1 is a key resource for research on Automatic Modulation Recognition in software‑defined and cognitive radio systems.

Citation

Emmanuel Adetiba and Jamiu R. Olasina, RadioModRec: A Dataset for Automatic Modulation Recognition in Software Defined and Cognitive Radio Research.

Dataset Card Authors

Emmanuel Adetiba

Contact Information

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Topics

Automatic Modulation Recognition
Wireless Communications

Source

Organization: hugging_face

Created: Unknown

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