An Efficient and Lightweight Model for Automatic Modulation Classification: A Hybrid Feature Extraction Network Combined with Attention Mechanism

Author:

Ma Zhao1,Fang Shengliang1,Fan Youchen1,Li Gaoxing1,Hu Haojie1

Affiliation:

1. School of Aerospace Information, Space Engineering University, Beijing 101400, China

Abstract

This paper proposes a hybrid feature extraction convolutional neural network combined with a channel attention mechanism (HFECNET-CA) for automatic modulation recognition (AMR). Firstly, we designed a hybrid feature extraction backbone network. Three different forms of convolution kernels were used to extract features from the original I/Q sequence on three branches, respectively, learn the spatiotemporal features of the original signal from different “perspectives” through the convolution kernels with different shapes, and perform channel fusion on the output feature maps of the three branches to obtain a multi-domain mixed feature map. Then, the deep features of the signal are extracted by connecting multiple convolution layers in the time domain. Secondly, a plug-and-play channel attention module is constructed, which can be embedded into any feature extraction layer to give higher weight to the more valuable channels in the output feature map to achieve the purpose of feature correction for the output feature map. The experimental results on the RadiomL2016.10A dataset show that the designed HFECNET-CA has higher recognition accuracy and fewer trainable parameters compared to other networks. Under 20 SNRs, the average recognition accuracy reached 63.92%, and the highest recognition accuracy reached 93.64%.

Funder

Key Basic Research Projects of the Basic Strengthening Program

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Modulation Recognition based on One-Dimensional Complex Convolutional Networks under Low SNR;2024 IEEE 6th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC);2024-05-24

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