FAE-MSLKNet: Multi-Scale Large Kernel Network With Fourier Adaptive Enhancement for Automatic Modulation Recognition

作     者：Lin, Xiaobing Zhang, Jiashu Wang, Hao Zhang, Heying 

作者机构：Southwest Jiaotong University Sichuan Province Key Lab of Signal and Information Processing School of Computing and Artificial Intelligence Chengdu611756 China Southwest Jiaotong University Sichuan Province Key Lab of Signal and Information Processing Chengdu611756 China 

出 版 物：《IEEE Transactions on Cognitive Communications and Networking》 (IEEE Trans. Cogn. Commun. Netw.)

年 卷 期：2024年

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0701[理学-数学] 

主　　题：Frequency domain analysis 

摘      要：Automatic Modulation Recognition (AMR) is vital in wireless communication systems. Recently, a number of deep learning(DL) architectures have been developed for AMR. However, existing methods have limitations in frequency analysis and long-range temporal dependencies extraction. Moreover, most models have high computational costs and large model sizes. To overcome these limitations, this paper proposes FAE-MSLKNet, a Multi-Scale Large Kernel Network with Fourier Adaptive Enhancement. First, we introduced a Fourier Adaptive Enhancement (FAE) module to adaptively enhance features and model long-term temporal dependencies in the frequency-domain. Furthermore, a Multi-Scale Large Kernel (MSLK) module is employed to extract local features with small convolution kernels and capture long-range temporal dependencies with large convolution kernels in the time-domain. Experiments on RML2016.10a, RML2016.10b, and RML2018.01a datasets demonstrate that FAE-MSLKNet achieves state-of-the-art performance with improved parameter efficiency and reduced computational complexity, highlighting its potential for practical wireless communication applications. © 2024 IEEE.