Modulation recognition method based on multimodal features

Introduction Automatic modulation recognition (AMR) plays a crucial role in modern communication systems for efficient signal processing and monitoring. However, existing modulation recognition methods often lack comprehensive feature extraction and suffer from recognition inaccuracies. Methods To overcome these challenges, we present a multi-task modulation recognition approach leveraging multimodal features. In this method, a network is proposed to differentiate between multi-domain features for temporal feature extraction. Simultaneously, a network capable of extracting features at multiple scales is utilized for image feature extraction. Subsequently, recognition is conducted by integrating the multimodal features. Due to the inherent differences between 1D signal features and 2D image features, recognizing them collectively may overlook the unique characteristics of each type. Results We examine the merit of the proposed multi-task modulation recognition method and validate their performance with experiments using a public datasets. With an SNR of 0 dB, the proposed algorithm achieves a recognition accuracy of 92.30% on the RadioML2016.10a dataset. Discussion Therefore, we propose a multi-task modulation recognition approach leveraging multimodal features to enhance accuracy. By integrating temporal and image-based feature extraction, our method outperforms existing techniques in recognition performance.