Real-World IQ Dataset for Automatic Radio Modulation Recognition under Multipath Channels

This dataset contains real-world complex baseband (IQ) radio signal samples for training and evaluating machine learning models in automatic modulation recognition (AMR).

It includes seven modulation types (BPSK, QPSK, QAM, GMSK, OFDM, NBFM, and WBFM) captured at 2.4 GHz under both clean (line-of-sight) and multipath propagation conditions across signal-to-noise ratio (SNR) levels from 20 dB to 30 dB.

Signals are segmented into fixed-length frames of 1024 IQ samples and stored in HDF5 format. Each frame is annotated with modulation type, channel condition, and SNR value. The dataset is suitable for benchmarking AMR performance, robustness analysis under realistic channel impairments, and reproducible research in wireless signal processing and cognitive radio.

A baseline convolutional neural network (CNN) is provided, achieving approximately 84% classification accuracy on the test set.