ShuffleFormer: An efficient shuffle meta framework for automatic modulation classification

Abstract

Automatic modulation classification (AMC) plays an essential and fundamental part in wireless communication systems, which can greatly enhance the efficiency of spectrum utilization. With the rapid development of internet of things, it has been an urgent task to effectively deploy AMC method in edge devices which usually have limited computing power. Hence, there is a pressing need for a high-performance AMC method, especially in terms of high accuracy and low computation complexity in modulation recognition. Although numerous AMC methods have been proposed, it is still a challenge to develop a novel AMC method with both high recognition accuracy and limited parameters. Recently, MetaFormer, as the promising technology, has achieved outstanding performance in processing long sequences. Inspired by MetaFormer, in this paper, an efficient end-to-end deep ShuffleFormer AMC approach is proposed to realize high performance modulation recognition. In the proposed approach, firstly, the PreBlock is proposed to extract deep information from input signals, in which both the channel and temporal attention are utilized to enrich the useful features. Then, bidirectional gated recurrent unit (BiGRU) is employed to further refine the abundant information for feature extraction. Furthermore, ShuffleFormer module is proposed, which innovatively fuse shuffle blocks into MetaFormer to further enhance its both local and global feature extraction ability with less parameters. Experimental results demonstrate that the ShuffleFormer AMC approach has more superior recognition performance than other contrastive methods, i.e., at least 5.97% average accuracy improvement with almost the same number of parameters, and less 41.4% parameters with a competitive average accuracy.