Chromosomal Mutation-Inspired Radio Augmentation for Enhanced Automatic Modulation Classification

As communication environments grow increasingly complex, efficient modulation scheme identification is critical. Automatic Modulation Classification (AMC) with deep learning has proven effective under diverse and noisy conditions, but its success hinges on the quality and diversity of training data. This paper tackles the challenge of acquiring diverse training datasets through an innovative data augmentation method inspired by chromosomal mutations from genetic algorithms. Designed for I/Q modulation signals, the method introduces six radio augmentations: Interstitial Deletion, Terminal Deletion, Inversion, Breakage, Ring, and Translocation. These augmentations enrich and diversify training data, enhancing the adaptability of AMC models. Experiments show a ninefold expansion of the training sample space, significantly boosting the benchmark AMC models’ performance. Notably, our method achieves state-of-the-art results with CLDNN on RML2016.10A and RML2016.10B, with mean accuracies of 67.11% and 69.02%, respectively. It also improves the Transformer-based TRN model’s mean accuracy on RML2016.10B by 9.55%. Our approach effectively addresses data scarcity in deep learning-based AMC and offers promising avenues for future communication systems. The source code is available.