Abstract:

To overcome the shortcomings of current methods for evaluating VoLTE video call quality,a method for evaluating VoLTE video call quality without reference based on machine learning and network index parameters was proposed.Firstly,the network parameters of the decoding core network were collected and preprocessed; then,the key features for VoLTE video call quality assessment were selected,and a reference-free evaluation model for VoLTE video quality assessment was constructed by comparing and selecting appropriate machine learning algorithms,so as to achieve real-time VoLTE video call quality assessment independent of the test environment and the original video.By researching the preprocessing of feature index data extracted from XDR data,the standardization of feature index was solved,and the evaluation model of feature input was convenient; the key features of VoLTE video call were selected and evaluated by feature engineering,which reduced the feature dimension and the complexity of the algorithm; at the same time,advanced machine learning technology was adopted to ensure and enhance the algorithm assessment accuracy.

Key words: machine learning, gradient boosting decison tree, voice over long-term evolution, video call quality