Modeling configuration-performance relation in a mobile network: a data-driven approach
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929415113408512 |
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| author | Panek, Michał Jabłoński, Ireneusz Woźniak, Michał |
| author_facet | Panek, Michał Jabłoński, Ireneusz Woźniak, Michał |
| contents | Mobile network performance modeling typically assumes either a fixed cell's configuration or only considers a limited number of parameters. This prohibits the exploration of multidimensional, diverse configuration space for, e.g., optimization purposes.
This paper presents a method for performance predictions based on a network cell's configuration and network conditions, which utilizes neural network architecture. We evaluate the idea by extensive experiments, with data from more than 50,000 5G cells. The assessment included a comparison of the proposed method against models developed for fixed configuration. Results show that combined configuration-performance modeling outperforms single-configuration models and allows for performance prediction of unknown configurations, i.e., it is not used for model training. A substantially lower mean absolute error was achieved (0.25 vs. 0.45 for fixed-configuration MLP-based models). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_06702 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Modeling configuration-performance relation in a mobile network: a data-driven approach Panek, Michał Jabłoński, Ireneusz Woźniak, Michał Networking and Internet Architecture Mobile network performance modeling typically assumes either a fixed cell's configuration or only considers a limited number of parameters. This prohibits the exploration of multidimensional, diverse configuration space for, e.g., optimization purposes. This paper presents a method for performance predictions based on a network cell's configuration and network conditions, which utilizes neural network architecture. We evaluate the idea by extensive experiments, with data from more than 50,000 5G cells. The assessment included a comparison of the proposed method against models developed for fixed configuration. Results show that combined configuration-performance modeling outperforms single-configuration models and allows for performance prediction of unknown configurations, i.e., it is not used for model training. A substantially lower mean absolute error was achieved (0.25 vs. 0.45 for fixed-configuration MLP-based models). |
| title | Modeling configuration-performance relation in a mobile network: a data-driven approach |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2407.06702 |