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.

This dataset contains real-world complex baseband (IQ) radio signal samples intended for training and evaluation of machine learning and deep learning models for automatic modulation recognition (AMR).The dataset includes seven modulation types: BPSK, QPSK, QAM, GMSK, OFDM, NBFM, and WBFM. Signals were captured under both clean (line-of-sight) and multipath propagation conditions and generated across multiple signal-to-noise ratio (SNR) levels ranging from 20 dB to 30 dB.All 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 (clean or multipath), and SNR value. The dataset is suitable for benchmarking modulation classification performance, robustness analysis under channel impairments, and reproducible research in wireless signal processing and cognitive radio.Baseline deep learning experiments using a convolutional neural network (CNN) are provided, demonstrating classification accuracy of approximately 83% on the test set.

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.