Signal modulation pattern recognition technology based on deep learning

This paper presents a deep learning-based signal modulation recognition framework designed for adaptive wireless communication systems in complex electromagnetic environments. Traditional modulation recognition approaches rely on manually extracting signal features, which often struggle with accuracy and scalability in dynamic settings. This article's proposed model incorporates a combination of convolutional neural networks (CNN) and Transformer architectures to efficiently classify modulation schemes by converting signals into two-dimensional images and extracting spatiotemporal features. The experiments validate the efficacy of the proposed model on modulation modes such as BPSK, QPSK, QAM16, and QAM64, demonstrating a recognition accuracy exceeding 75% under moderate signal-to-noise ratio conditions. Compared to conventional methods, the proposed framework significantly enhances recognition accuracy and computational efficiency, especially in low signal-to-noise ratio conditions. This study highlights the potential of integrating deep learning models in modulation recognition, providing a robust approach to overcoming the challenges of real-time adaptive modulation in wireless communication.