A Novel Automatic Modulation Classification Scheme Based on Multi-Scale Networks
This work addresses a specific challenge in wireless communication networks, offering an incremental improvement for modulation classification tasks.
The paper tackles the intra-class diversity problem in automatic modulation classification caused by dynamic wireless environments, proposing a multi-scale network with a novel loss function that achieves better classification accuracy than benchmark schemes.
Automatic modulation classification enables intelligent communications and it is of crucial importance in today's and future wireless communication networks. Although many automatic modulation classification schemes have been proposed, they cannot tackle the intra-class diversity problem caused by the dynamic changes of the wireless communication environment. In order to overcome this problem, inspired by face recognition, a novel automatic modulation classification scheme is proposed by using the multi-scale network in this paper. Moreover, a novel loss function that combines the center loss and the cross entropy loss is exploited to learn both discriminative and separable features in order to further improve the classification performance. Extensive simulation results demonstrate that our proposed automatic modulation classification scheme can achieve better performance than the benchmark schemes in terms of the classification accuracy. The influence of the network parameters and the loss function with the two-stage training strategy on the classification accuracy of our proposed scheme are investigated.