Context-Aware Mobile Network Performance Prediction Using Network & Remote Sensing Data
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911861139570688 |
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| author | Shibli, Ali Zanouda, Tahar |
| author_facet | Shibli, Ali Zanouda, Tahar |
| contents | Accurate estimation of Network Performance is crucial for several tasks in telecom networks. Telecom networks regularly serve a vast number of radio nodes. Each radio node provides services to end-users in the associated coverage areas. The task of predicting Network Performance for telecom networks necessitates considering complex spatio-temporal interactions and incorporating geospatial information where the radio nodes are deployed. Instead of relying on historical data alone, our approach augments network historical performance datasets with satellite imagery data. Our comprehensive experiments, using real-world data collected from multiple different regions of an operational network, show that the model is robust and can generalize across different scenarios. The results indicate that the model, utilizing satellite imagery, performs very well across the tested regions. Additionally, the model demonstrates a robust approach to the cold-start problem, offering a promising alternative for initial performance estimation in newly deployed sites. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_00220 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Context-Aware Mobile Network Performance Prediction Using Network & Remote Sensing Data Shibli, Ali Zanouda, Tahar Machine Learning Networking and Internet Architecture Accurate estimation of Network Performance is crucial for several tasks in telecom networks. Telecom networks regularly serve a vast number of radio nodes. Each radio node provides services to end-users in the associated coverage areas. The task of predicting Network Performance for telecom networks necessitates considering complex spatio-temporal interactions and incorporating geospatial information where the radio nodes are deployed. Instead of relying on historical data alone, our approach augments network historical performance datasets with satellite imagery data. Our comprehensive experiments, using real-world data collected from multiple different regions of an operational network, show that the model is robust and can generalize across different scenarios. The results indicate that the model, utilizing satellite imagery, performs very well across the tested regions. Additionally, the model demonstrates a robust approach to the cold-start problem, offering a promising alternative for initial performance estimation in newly deployed sites. |
| title | Context-Aware Mobile Network Performance Prediction Using Network & Remote Sensing Data |
| topic | Machine Learning Networking and Internet Architecture |
| url | https://arxiv.org/abs/2405.00220 |