Using Deep-Learning for 5G End-to-End Delay Estimation Based on Gaussian Mixture Models Article Swipe
Diyar Fadhil
,
Rodolfo Oliveira
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/info14120648
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/info14120648
Deep learning is used in various applications due to its advantages over traditional Machine Learning (ML) approaches in tasks encompassing complex pattern learning, automatic feature extraction, scalability, adaptability, and performance in general. This paper proposes an end-to-end (E2E) delay estimation method for 5G networks through deep learning (DL) techniques based on Gaussian Mixture Models (GMM). In the first step, the components of a GMM are estimated through the Expectation-Maximization (EM) algorithm and are subsequently used as labeled data in a supervised deep learning stage. A multi-layer neural network model is trained using the labeled data and assuming different numbers of E2E delay observations for each training sample. The accuracy and computation time of the proposed deep learning estimator based on the Gaussian Mixture Model (DLEGMM) are evaluated for different 5G network scenarios. The simulation results show that the DLEGMM outperforms the GMM method based on the EM algorithm, in terms of the accuracy of the E2E delay estimates, although requiring a higher computation time. The estimation method is characterized for different 5G scenarios, and when compared to GMM, DLEGMM reduces the mean squared error (MSE) obtained with GMM between 1.7 to 2.6 times.
Related Topics
Concepts
Mixture model
Computer science
Artificial intelligence
Estimator
Artificial neural network
Computation
Scalability
End-to-end principle
Deep learning
Pattern recognition (psychology)
Mean squared error
Gaussian
Expectation–maximization algorithm
Machine learning
Algorithm
Mathematics
Statistics
Maximum likelihood
Quantum mechanics
Physics
Database
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/info14120648
- https://www.mdpi.com/2078-2489/14/12/648/pdf?version=1701767393
- OA Status
- gold
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389336562
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389336562Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/info14120648Digital Object Identifier
- Title
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Using Deep-Learning for 5G End-to-End Delay Estimation Based on Gaussian Mixture ModelsWork 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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2023-12-05Full publication date if available
- Authors
-
Diyar Fadhil, Rodolfo OliveiraList of authors in order
- Landing page
-
https://doi.org/10.3390/info14120648Publisher landing page
- PDF URL
-
https://www.mdpi.com/2078-2489/14/12/648/pdf?version=1701767393Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2078-2489/14/12/648/pdf?version=1701767393Direct OA link when available
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Mixture model, Computer science, Artificial intelligence, Estimator, Artificial neural network, Computation, Scalability, End-to-end principle, Deep learning, Pattern recognition (psychology), Mean squared error, Gaussian, Expectation–maximization algorithm, Machine learning, Algorithm, Mathematics, Statistics, Maximum likelihood, Quantum mechanics, Physics, DatabaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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| publication_year | 2023 |
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