Research on CNN-Based Satellite Communication Modulation Mode Recognition Technology

The identification of modulation mode constitutes a pivotal element within satellite communication systems. Its utilization is pervasive, manifesting in domains such as signal demodulation, resource allocation, and communication quality assessment. However, traditional methods of modulation mode identification are dependent on manual feature extraction, which is both time-consuming and less adaptable in complex environments. Recent advancements in deep learning technology, particularly Convolutional Neural Network (CNN), have introduced novel approaches and methodologies for the identification of radio signal modulation modes. This paper focuses on how deep learning techniques, particularly CNN, can improve the accuracy and efficiency of modulation mode recognition during satellite communications. It summarize key advances in dataset construction, network model design, training methods and optimization techniques. This paper also explores the potential application prospects of CNN technology in 6G communications, emphasizing its critical role in enhancing communication efficiency and service quality. The findings indicate that CNN has significant advantages in satellite communication modulation mode recognition, especially in improving recognition accuracy and robustness.