Network Bandwidth Prediction Method Based on Hidden Markov model in High-speed Railway

In the context of the full commercial use of 5G, high-speed rail passengers have higher and higher requirements for wireless network service quality. However, in the current high-speed rail 5G network streaming media transmission, due to the fast moving speed, the base station is frequently switched, and the user bandwidth does not match the streaming media bit rate, resulting in a poor user network experience and a poor streaming media experience. In view of the above problems, this paper focuses on the bandwidth prediction of network users in the high-speed rail environment, and proposes a bandwidth prediction algorithm High speed 5G Environment Bandwidth Predict(H5EBP) based on the hidden Markov model in different states of the high-speed rail. So as to improve the user's streaming media experience. After comparative evaluation with other existing bandwidth prediction algorithms, H5EBP can greatly improve the accuracy of bandwidth prediction, thereby improving the user's streaming media experience.