Research and application of VoLTE video call quality based on machine learning
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 an...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2020-03-01
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| Series: | Dianxin kexue |
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| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2020021/ |
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| _version_ | 1841530594125676544 |
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| author | Qizhu ZHONG |
| author_facet | Qizhu ZHONG |
| author_sort | Qizhu ZHONG |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6261bb48be80438994ffc29909892ca4 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2020-03-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-6261bb48be80438994ffc29909892ca42025-01-15T03:01:05ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012020-03-013615616559584525Research and application of VoLTE video call quality based on machine learningQizhu ZHONGTo 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.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2020021/machine learninggradient boosting decison treevoice over long-term evolutionvideo call quality |
| spellingShingle | Qizhu ZHONG Research and application of VoLTE video call quality based on machine learning Dianxin kexue machine learning gradient boosting decison tree voice over long-term evolution video call quality |
| title | Research and application of VoLTE video call quality based on machine learning |
| title_full | Research and application of VoLTE video call quality based on machine learning |
| title_fullStr | Research and application of VoLTE video call quality based on machine learning |
| title_full_unstemmed | Research and application of VoLTE video call quality based on machine learning |
| title_short | Research and application of VoLTE video call quality based on machine learning |
| title_sort | research and application of volte video call quality based on machine learning |
| topic | machine learning gradient boosting decison tree voice over long-term evolution video call quality |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2020021/ |
| work_keys_str_mv | AT qizhuzhong researchandapplicationofvoltevideocallqualitybasedonmachinelearning |