MIGOU-MOD: A dataset of modulated radio signals acquired with MIGOU, a low-power IoT experimental platform
Description
This dataset is provided as supplementary material of the work entitled "Gated Recurrent Unit Neural Networks for Automatic Modulation Classification with Resource-Constrained End-Devices", published in IEEE Access Journal. This dataset includes over-the-air measurements of real radio signals modulated with 11 different modulations. These signals were generated by a transmitter formed by a USRP B210 connected to a computer with GNU Radio. In order to implement the different transmitters, we used the source code and same data sources with which RadioML2016.10a was generated. It should be noted that an error with AM modulations, solved in later versions of the RadioML dataset, was also corrected for our dataset generation. On the receiver side, we used the MIGOU platform to record the signals. This is a low-power experimental platform with Software-Defined Radio (SDR) capabilities that has been specifically designed to address the hardware architectural constraints that limit Cognitive Radio (CR) research and experimentation with low-power end-devices. This platform was configured to sense a communication channel and send the raw I/Q samples to a computer that properly stores them. All measurements were carried out indoors, in an office environment. Specifically, measurements were taken at two distances from the transmitter, 1 and 6 meters, which corresponds to average Signal-to-Noise Ratios (SNRs) of 37 dB and 22 dB respectively. All these recorded I/Q signals were divided and formatted into 128×2 vectors, which were individually normalized. Finally, 400,000 normalized vectors were included for each modulation-SNR (MOD-SNR) pair in the dataset, resulting in a total of 8.8 million vectors. For more details of the configuration parameters, the normalization method and the materials used, please refer to the work mentioned in the first paragraph.
Files
Steps to reproduce
The GNU Radio source files of the different transmitters used to generate the dataset, and an example of the dataset usage are included.