Design of 5G Mobile Communication Signal Processing System Based on Neural Network Algorithm

In the current mobile communication field, 5G technology represents a revolutionary progress, providing users with higher data transmission speed, lower delay and better network connection quality. However, with the continuous development of 5G technology, the demand for signal processing system is increasing, and more efficient and intelligent methods are needed to process and optimize signals. The key goal of this study is to introduce neural network technology into 5G signal processing to improve the performance and intelligent adaptability of the system. By using deep learning technology, this paper chooses AlexNet CNN as the core of modulation recognition algorithm, and uses AlexNet CNN model for training and testing to realize automatic signal recognition, which can automatically learn and adapt to the signal processing requirements under different channel conditions. The simulation results show that at -20dB, the recognition effect is 80%, -10dB and above, and the classification accuracy of the model is close to 100%. The classification effect is far better than the traditional classification method. This innovative method is expected to make a major breakthrough in the field of 5G communication and improve the performance and user experience of the communication system.