External Attention Mechanism-Based Modulation Classification. Issue 1 (1st February 2023) - Tao, Xueying; Shao, Huaizong; Li, Qiang; Pan, Ye; Fu, Zhongqi
Abstract: This paper considers the modulation classification of radio frequency (RF) signals. An external attention mechanism-based convolution neural network (EACNN) is proposed. Thanks to the external attention layers, the EACNN network can capture the potential correlations of different modulation data, which helps reduce computational consumption and memory costs efficiently during training. Moreover, to account for the variation of the signals induced by channel fading, we further propose a customized batch normalization (BN) layer in EACNN to improve the classification accuracy with less training time. Numerical experiments on RML2016.a dataset shows that the proposed method outperforms the baseline method CNN2 by 7% in terms of classification accuracy.