Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks
| العنوان: | Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks |
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| المؤلفون: | Marc Carrascosa, Francesc Wilhelmi, Cristina Cano, Boris Bellalta, Vishnu Ram, Anders Jonsson |
| المصدر: | IEEE Wireless Communications. 28:160-166 |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, Bridging (networking), Computer science, Reliability (computer networking), 02 engineering and technology, Communications system, Machine learning, computer.software_genre, Communication systems, Machine Learning (cs.LG), Computer Science - Networking and Internet Architecture, Stakeholders, FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering, Wifi network, Wireless fidelity, Electrical Engineering and Systems Science - Signal Processing, Electrical and Electronic Engineering, Complex problems, Networking and Internet Architecture (cs.NI), Training data, 6G mobile communication, business.industry, Testbed, 5G mobile ommunication, 020206 networking & telecommunications, Reliability, Computer Science Applications, Trustworthiness, Artificial intelligence, business, computer, 5G |
| الوصف: | 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 article, we devise the role of network simulators for bridging the gap between ML and communications 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 into 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 communications through a proof-of-concept testbed implementation of a residential WiFi network. This work has been partially supported by grants MDM-2015-0502, WINDMAL PGC2018-099959- B-I00 (MCIU/AEI/FEDER,UE), 2017-SGR-11888, and by SPOTS project (RTI2018-095438-A-I00) funded by the Spanish Ministry of Science, Innovation and Universities. |
| وصف الملف: | application/pdf |
| تدمد: | 1558-0687 1536-1284 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::819d08daf12f8c8e0ba4bc0d834f9030Test https://doi.org/10.1109/mwc.001.2000206Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....819d08daf12f8c8e0ba4bc0d834f9030 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15580687 15361284 |
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