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.

本数据集包含真实场景下的复杂基带（IQ）无线电信号样本，用于训练和评估自动调制识别（Automatic Modulation Recognition, AMR）任务中的机器学习模型。
该数据集涵盖7种调制制式：BPSK、QPSK、QAM、GMSK、OFDM、NBFM及WBFM，采集于2.4 GHz频段，包含无干扰（视距）与多径传播两类信道场景，信噪比（Signal-to-Noise Ratio, SNR）范围覆盖20 dB至30 dB。
信号被分割为包含1024个IQ样本的固定长度帧，并以HDF5格式存储。每个帧均标注有调制类型、信道条件及信噪比数值。本数据集适用于自动调制识别性能基准测试、真实信道损伤下的鲁棒性分析，以及无线信号处理与认知无线电领域的可复现研究。
本数据集附带一个基准卷积神经网络（Convolutional Neural Network, CNN），其在测试集上的分类准确率约为84%。