计算机科学
特征提取
计算复杂性理论
编码器
人工智能
模式识别(心理学)
卷积(计算机科学)
残余物
算法
人工神经网络
操作系统
作者
Mutian Hu,Jitong Ma,Zhengyan Yang,Jie Wang,Jingjing Lu,Zhanjun Wu
标识
DOI:10.1109/lcomm.2023.3298941
摘要
Automatic Modulation Recognition (AMR) is becoming increasingly important due to its key role in wireless communications. In order to enrich the feature information and reduce computational complexity, a novel feature fusion convolution-aided transformer (FCAformer) approach is proposed in this paper. To enhance the differences between samples, we adopt Markov Transformation Field (MTF), which can extract potential correlation features between signal sequences and combine them with I/Q sequences to generate more information rich data as input for the transformer. We propose a novel patchify module that reduces invalid features and computational complexity by performing feature extraction. We also propose a convolution-aided encoder module consisting of an improved attention mechanism and a convolutional residual connection to improve performance through local-global feature fusion and further reduce the parameters. Experimental results on RadioRML2016.10a show that the proposed AMR method has both higher recognition accuracy and lower parameters than other contrastive methods.
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