Multivariate Time Series Characterization and Forecasting of VoIP Traffic in Real Mobile Networks Article Swipe
Mario Di Mauro
,
Giovanni Galatro
,
F. Postiglione
,
Wei Song
,
Antonio Liotta
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/tnsm.2023.3295748
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/tnsm.2023.3295748
Predicting the behavior of real-time traffic (e.g., VoIP) in mobility\nscenarios could help the operators to better plan their network infrastructures\nand to optimize the allocation of resources. Accordingly, in this work the\nauthors propose a forecasting analysis of crucial QoS/QoE descriptors (some of\nwhich neglected in the technical literature) of VoIP traffic in a real mobile\nenvironment. The problem is formulated in terms of a multivariate time series\nanalysis. Such a formalization allows to discover and model the temporal\nrelationships among various descriptors and to forecast their behaviors for\nfuture periods. Techniques such as Vector Autoregressive models and machine\nlearning (deep-based and tree-based) approaches are employed and compared in\nterms of performance and time complexity, by reframing the multivariate time\nseries problem into a supervised learning one. Moreover, a series of auxiliary\nanalyses (stationarity, orthogonal impulse responses, etc.) are performed to\ndiscover the analytical structure of the time series and to provide deep\ninsights about their relationships. The whole theoretical analysis has an\nexperimental counterpart since a set of trials across a real-world LTE-Advanced\nenvironment has been performed to collect, post-process and analyze about\n600,000 voice packets, organized per flow and differentiated per codec.\n
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tnsm.2023.3295748
- https://ieeexplore.ieee.org/ielx7/4275028/5699970/10184084.pdf
- OA Status
- hybrid
- Cited By
- 14
- References
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- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4384304539
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- OpenAlex ID
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https://openalex.org/W4384304539Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tnsm.2023.3295748Digital Object Identifier
- Title
-
Multivariate Time Series Characterization and Forecasting of VoIP Traffic in Real Mobile NetworksWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-07-14Full publication date if available
- Authors
-
Mario Di Mauro, Giovanni Galatro, F. Postiglione, Wei Song, Antonio LiottaList of authors in order
- Landing page
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https://doi.org/10.1109/tnsm.2023.3295748Publisher landing page
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https://ieeexplore.ieee.org/ielx7/4275028/5699970/10184084.pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://ieeexplore.ieee.org/ielx7/4275028/5699970/10184084.pdfDirect OA link when available
- Concepts
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Computer science, Voice over IP, Time series, Autoregressive model, Multivariate statistics, Data mining, Network packet, Machine learning, Artificial intelligence, Real-time computing, Computer network, The Internet, Economics, World Wide Web, EconometricsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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14Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5, 2024: 9Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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