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.

中文翻译：

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网络模拟器在机器学习辅助的5G / 6G网络中的使用

毫无疑问，由于机器学习（ML）在应用于复杂问题时具有可预见的性能，因此它将成为未来通信的重要驱动力。但是，机器学习在网络系统中的应用引起了网络运营商和其他利益相关者的关注，特别是在可信赖性和可靠性方面。在本文中，我们设计了网络模拟器的作用，以缩小ML和通信系统之间的差距。特别是，我们提出了在支持ML的网络中仿真器的体系结构集成，用于训练，测试和验证ML模型，然后将其应用于有效网络。此外，我们提供了有关集成带来的主要挑战的见解，然后给出了讨论如何克服这些挑战的提示。最后，