Avaliação de Algoritmos de Aprendizado de Máquina para Predição de QoE em Redes 6G
Resumo
This paper evaluates the performance of Machine Learning (ML) algorithms to provide Quality of Experience (QoE) user decisions in multimedia services. A specialized dataset was built for mapping network Quality of Service (QoS) Key Performance Indicators (KPIs) with video quality metrics to perform QoE predictions. An evaluation is then carried out considering the main regression models to support the development of QoE-aware systems and meet the critical requirements of applications characteristic of 6G scenarios.
Referências
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Jardim, S., Dantas Silva, F. S., Neto, A., Bustos, H., Immich, R., and Fontes, R. (2021). Analysis of ml algorithms to support elastic service chaining in ehealth vertical applications. In 2021 International Wireless Communications and Mobile Computing (IWCMC), pages 336–341.
Khan, W., Raza, A., Kuusniemi, H., and Elmusrati, M. (2021). Comparison of machine learning algorithms for venue presence with inclusion of neighbours. In 2021 29th Telecommunications Forum (TELFOR), pages 1–4.
Liu, J. and Huang, C.-T. (2023). Evaluation of different time series forecasting models for 5g v2v networks. In 2023 International Conference on Computing, Networking and Communications (ICNC), pages 278–282.
Sharma, A., Pandit, S., and Talluri, S. R. (2021). A comparative study to classify and predict the throughput of fifth generation wireless technology using supervised machine learning algorithms. In 2021 Sixth International Conference on Image Information Processing (ICIIP), volume 6, pages 288–292.
Silva, F. S. D., Schneider, L. M., Rosário, D., and Neto, A. V. (2022). Network slicing mobility aware control to assist handover decisions on e-health 5g use cases. In 2022 International Wireless Communications and Mobile Computing (IWCMC), pages 1034–1039.
Wang, C.-X., You, X., Gao, X., Zhu, X., Li, Z., Zhang, C., Wang, H., Huang, Y., Chen, Y., Haas, H., Thompson, J. S., Larsson, E. G., Renzo, M. D., Tong, W., Zhu, P., Shen, X., Poor, H. V., and Hanzo, L. (2023). On the road to 6g: Visions, requirements, key technologies and testbeds. IEEE Communications Surveys & Tutorials, pages 1–1.
Wu, W., Zhou, C., Li, M., Wu, H., Zhou, H., Zhang, N., Shen, X. S., and Zhuang, W. (2022). Ai-native network slicing for 6g networks. IEEE Wireless Communications, 29(1):96–103.
Jardim, S., Dantas Silva, F. S., Neto, A., Bustos, H., Immich, R., and Fontes, R. (2021). Analysis of ml algorithms to support elastic service chaining in ehealth vertical applications. In 2021 International Wireless Communications and Mobile Computing (IWCMC), pages 336–341.
Khan, W., Raza, A., Kuusniemi, H., and Elmusrati, M. (2021). Comparison of machine learning algorithms for venue presence with inclusion of neighbours. In 2021 29th Telecommunications Forum (TELFOR), pages 1–4.
Liu, J. and Huang, C.-T. (2023). Evaluation of different time series forecasting models for 5g v2v networks. In 2023 International Conference on Computing, Networking and Communications (ICNC), pages 278–282.
Sharma, A., Pandit, S., and Talluri, S. R. (2021). A comparative study to classify and predict the throughput of fifth generation wireless technology using supervised machine learning algorithms. In 2021 Sixth International Conference on Image Information Processing (ICIIP), volume 6, pages 288–292.
Silva, F. S. D., Schneider, L. M., Rosário, D., and Neto, A. V. (2022). Network slicing mobility aware control to assist handover decisions on e-health 5g use cases. In 2022 International Wireless Communications and Mobile Computing (IWCMC), pages 1034–1039.
Wang, C.-X., You, X., Gao, X., Zhu, X., Li, Z., Zhang, C., Wang, H., Huang, Y., Chen, Y., Haas, H., Thompson, J. S., Larsson, E. G., Renzo, M. D., Tong, W., Zhu, P., Shen, X., Poor, H. V., and Hanzo, L. (2023). On the road to 6g: Visions, requirements, key technologies and testbeds. IEEE Communications Surveys & Tutorials, pages 1–1.
Wu, W., Zhou, C., Li, M., Wu, H., Zhou, H., Zhang, N., Shen, X. S., and Zhuang, W. (2022). Ai-native network slicing for 6g networks. IEEE Wireless Communications, 29(1):96–103.
Publicado
22/05/2023
Como Citar
SILVA, Felipe S. Dantas; LIMA, Mathews; SANTOS, Charles H. F.; NETO, Augusto.
Avaliação de Algoritmos de Aprendizado de Máquina para Predição de QoE em Redes 6G. In: WORKSHOP DE REDES 6G (W6G), 3. , 2023, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 13-18.
DOI: https://doi.org/10.5753/w6g.2023.823.
