Navigation Signal Radio Frequency Channel Modeling and Predistortion Technology Based on Artificial Intelligence Technology and Neural Network

Abstract

Digital predistortion technology is widely used in wireless communication. As a vital part of wireless communication system, the predistortion technology of radio frequency power amplifier has always been a hot and difficult topic. This paper will research and explore the key technologies of radio frequency channel construction and predistortion of navigation information through artificial intelligence technology and neural network. The article first conducts a simple research on a new generation of artificial intelligence systems--artificial intelligence is an engineering technology scientific research that develops theories, methods, skills, and application systems for modeling, scaling, and amplifying collective human knowledge. Then, the article combines neural network algorithms. BP network is not only a part of synthetic neural network but also a multiple layer fed-forward network. Finally, a neural network method is proposed to model and predistort the load channel model. Then, the RF channel distortion model can be constructed, and the RF channel modeling simulation experiment and the RF channel predistortion simulation experiment are carried out. The experimental results of this paper showed that compared with the situation without predistortion, the two indicators of the zero-crossing slope distortion and the zero-crossing offset of the discriminant function of the output signal of the channel with predistortion had been greatly improved. Overall, the neural network-based model outperformed the RVTDNN model by 30% on these two metrics. It also indicated that the neural network model could model and predistort the cascade model well, and the new model had better modeling accuracy and predistortion effect than the RVTDNN model.