Work

An Automatic Modulation Recognition Method Based on the Multimodal Kernel Harmonic Feature Fusion Network

Year: 2025

Type: article

Abstract: In increasingly complex electromagnetic environments, wireless communication systems face the severe challenge of non-Gaussian impulse noise. The moments of impulse noise tend toward infinity, reducing the distinguishability of signal features and thereby limiting improvements in signal modulation recognition rates. First, a time-frequency analysis method based on kernel space mapping is proposed to improve the distinguishability of time-frequency features in signals under impulse noise. On this basis, a multimodal kernel harmonic feature fusion network is constructed, combining convolutional neural networks and graph convolutional networks to extract and fuse kernel harmonic features from three modalities to achieve robust and accurate modulation recognition. The simulation results show a generalized signal-to-noise ratio of -2 dB, and the modulation recognition rate reaches 93.5%.

Source: Sensors

DOI: 10.3390/s25206352

Authors: Qiancheng Zhang, Hongbing Ji, Lin Li

Institution: Xidian University

Language: English

Topic: Wireless Signal Modulation Classification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funders: National Natural Science Foundation of China, Key Research and Development Projects of Shaanxi Province