Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks
Without any doubt, Machine Learning (ML) will be an important driver of future communications due to its foreseen performance when applied to complex problems. However, the application of ML to networking systems raises concerns among network operators and other stakeholders, especially regarding tr...
| Autores: | , , , , , |
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| Formato: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2021 |
| País: | España |
| Recursos: | Universitat Oberta de Catalunya (UOC) |
| Repositorio: | O2, repositorio institucional de la UOC |
| OAI Identifier: | oai:openaccess.uoc.edu:10609/148580 |
| Acesso em linha: | http://hdl.handle.net/10609/148580 http://dx.doi.org/10.1109/MWC.001.2000206 |
| Access Level: | acceso abierto |
| Palavra-chave: | future networks ITU network simulation machine learning wireless local area networks |
| Resumo: | Without any doubt, Machine Learning (ML) will be an important driver of future communications due to its foreseen performance when applied to complex problems. However, the application of ML to networking systems raises concerns among network operators and other stakeholders, especially regarding trustworthiness and reliability. In this paper, we devise the role of network simulators for bridging the gap between ML and com- munications systems. In particular, we present an architectural integration of simulators in ML-aware networks for training, testing, and validating ML models before being applied to the operative network. Moreover, we provide insights on the main challenges resulting from this integration, and then give hints discussing how they can be overcome. Finally, we illustrate the integration of network simulators into ML-assisted communi- cations through a proof-of-concept testbed implementation of a residential Wi-Fi network. |
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