Prediction of Packet Loss Rate in Non-Stationary Networks Based on Time-Varying Autoregressive Sequences
Currently, most of the existing link parameter prediction schemes assume that the link state remains constant during the measurement period, making it difficult to capture their time-varying characteristics. To solve this problem, this paper proposes a prediction problem for packet loss rate in a no...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/5/1103 |
| _version_ | 1797615539505332224 |
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| author | Xiaorui Wu Chunling Wu Pei Deng |
| author_facet | Xiaorui Wu Chunling Wu Pei Deng |
| author_sort | Xiaorui Wu |
| collection | DOAJ |
| description | Currently, most of the existing link parameter prediction schemes assume that the link state remains constant during the measurement period, making it difficult to capture their time-varying characteristics. To solve this problem, this paper proposes a prediction problem for packet loss rate in a non-stationary network environment. The measurement period is divided into several adjacent time windows, and the packet loss rates measured passively in each time window are regarded as non-stationary time sequences for real-time tracking to obtain the changes in link packet loss rate at a small cost. By analyzing time-varying autoregressive (TVAR) sequences, a scheme for estimating the time-varying coefficient was presented. In addition, a prediction scheme for the packet loss rate in a non-stationary network was proposed based on TVAR sequences. Finally, this paper conducts experiments based on a non-stationary network simulation environment established by the improved Gilbert–Elliot model and a small wireless multi-hop network experiment platform built in reality. Simulation and experimental results show that the prediction scheme of the packet loss rate based on the TVAR sequence can accurately predict the packet loss rate. |
| first_indexed | 2024-03-11T07:27:58Z |
| format | Article |
| id | doaj.art-c7e8844a89a44c0b83c28c50d555f01a |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T07:27:58Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-c7e8844a89a44c0b83c28c50d555f01a2023-11-17T07:31:43ZengMDPI AGElectronics2079-92922023-02-01125110310.3390/electronics12051103Prediction of Packet Loss Rate in Non-Stationary Networks Based on Time-Varying Autoregressive SequencesXiaorui Wu0Chunling Wu1Pei Deng2National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Artificial Intelligence and Big Data, Chongqing College of Electronic Engineering, Chongqing 401331, ChinaSchool of Artificial Intelligence and Big Data, Chongqing College of Electronic Engineering, Chongqing 401331, ChinaCurrently, most of the existing link parameter prediction schemes assume that the link state remains constant during the measurement period, making it difficult to capture their time-varying characteristics. To solve this problem, this paper proposes a prediction problem for packet loss rate in a non-stationary network environment. The measurement period is divided into several adjacent time windows, and the packet loss rates measured passively in each time window are regarded as non-stationary time sequences for real-time tracking to obtain the changes in link packet loss rate at a small cost. By analyzing time-varying autoregressive (TVAR) sequences, a scheme for estimating the time-varying coefficient was presented. In addition, a prediction scheme for the packet loss rate in a non-stationary network was proposed based on TVAR sequences. Finally, this paper conducts experiments based on a non-stationary network simulation environment established by the improved Gilbert–Elliot model and a small wireless multi-hop network experiment platform built in reality. Simulation and experimental results show that the prediction scheme of the packet loss rate based on the TVAR sequence can accurately predict the packet loss rate.https://www.mdpi.com/2079-9292/12/5/1103packet loss rate predictionnon-stationary networktime-varying autoregressive sequence |
| spellingShingle | Xiaorui Wu Chunling Wu Pei Deng Prediction of Packet Loss Rate in Non-Stationary Networks Based on Time-Varying Autoregressive Sequences Electronics packet loss rate prediction non-stationary network time-varying autoregressive sequence |
| title | Prediction of Packet Loss Rate in Non-Stationary Networks Based on Time-Varying Autoregressive Sequences |
| title_full | Prediction of Packet Loss Rate in Non-Stationary Networks Based on Time-Varying Autoregressive Sequences |
| title_fullStr | Prediction of Packet Loss Rate in Non-Stationary Networks Based on Time-Varying Autoregressive Sequences |
| title_full_unstemmed | Prediction of Packet Loss Rate in Non-Stationary Networks Based on Time-Varying Autoregressive Sequences |
| title_short | Prediction of Packet Loss Rate in Non-Stationary Networks Based on Time-Varying Autoregressive Sequences |
| title_sort | prediction of packet loss rate in non stationary networks based on time varying autoregressive sequences |
| topic | packet loss rate prediction non-stationary network time-varying autoregressive sequence |
| url | https://www.mdpi.com/2079-9292/12/5/1103 |
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