Research on Modulation Signal Recognition Based on CLDNN Network
Binghang Zou, Xiaodong Zeng, Faquan Wang
Abstract
Modulated signal recognition and classification occupies an important position in electronic information warfare, intelligent wireless communication, and fast modulation and demodulation. To address the shortcomings of existing recognition methods, such as high manual involvement, few recognition types, and a low recognition rate under a low signal-to-noise ratio, we propose an attention mechanism short-link convolution long short-term memory deep neural networks (ASCLDNN) recognition model. The network is optimized for modulated signal recognition and incorporates an attention mechanism to achieve higher accuracy by adding weights to important signals. The experimental results show that ASCLDNN can recognize 11 signal modulations with high accuracy at a low signal-to-noise ratio and no confusion for specific signals.