Multimodal Machine Learning Algorithm for Enhanced Signal Modulation Recognition in Wireless Communication Systems

Automatic modulation identification plays an important role in wireless communication systems. With the development of deep learning technology, more and more researches have begun to adopt deep learning methods for modulation identification. However, traditional deep learning methods face interference from nonlinearity, noise, and time-varying nature of the signal when processing the signal, resulting in degradation of classification performance. Meanwhile, single modal features are difficult to fully capture the time and spatial domain information of the signal, which is important for accurately identifying the signal modulation type. To overcome these limitations, this study proposes a multi-modal deep learning based signal modulation recognition scheme. The scheme improves the modulation recognition performance by fusing features from different modalities, including time, frequency and spatial domain features. Experimental results show that the proposed method achieves significant performance improvement in the signal modulation recognition task. This provides a more robust and accurate modulation identification capability for wireless communication systems.