Channel and Hardware Impairment Data Augmentation for Robust Modulation Classification

Deep learning has achieved remarkable results in modulation classification under two assumptions: a large amount of labeled class-balanced data is available, and the test data and training data follow the same distribution. However, due to channel and hardware impairments, it is implausible that these assumptions hold in practice. This paper proposes Model-based Data Augmentation for Deep learning-based Modulation Classification (MDA-DMC), to build a high-quality dataset from a small amount of labeled seed data. MDA-DMC leverages two well-known augmentation methods: adding Gaussian noise to, and rotation of the seed signal constellations. Furthermore, we develop two novel augmentation methods to combat channel and hardware impairments: radial shift and stretching of the signal constellations. We are the first to investigate the correlation between these augmentation methods and the channel/hardware impairments, demonstrating the adverse effect of the rotation and stretching of signal constellations on classifier performance. Consequently, the dataset must incorporate both augmentations to counterbalance performance degradation. MDA-DMC compensates for hardware impairments when training and test data channel models are identical. It also addresses fading impairments with a few AWGN seed data for low-order modulation formats. However, classifiers trained on the augmented dataset struggle to generalize channel impairments effectively with higher-order modulation formats.