Spurred by the recent advances in deep learning to harness rich information hidden in large volumes of data and to tackle problems that are hard to model/solve (e.g., resource allocation problems), there is currently tremendous excitement in the mobile networks domain around the transformative potential of data-driven AI/ML based network automation, control and analytics for 5G and beyond. In this article, we present a cautionary perspective on the use of AI/ML in the 5G context by highlighting the adversarial dimension spanning multiple types of ML (supervised/unsupervised/RL) and support this through three case studies. We also discuss approaches to mitigate this adversarial ML risk, offer guidelines for evaluating the robustness of ML models, and call attention to issues surrounding ML oriented research in 5G more generally.

中文翻译：

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通过对抗性镜头检查 5G 及以后的机器学习

在深度学习的最新进展以利用隐藏在大量数据中的丰富信息并解决难以建模/解决的问题（例如资源分配问题）的推动下，目前在移动网络领域围绕变革性基于数据驱动的 AI/ML 的网络自动化、控制和分析在 5G 及更高版本中的潜力。在本文中，我们通过强调跨越多种类型 ML（监督/无监督/强化学习）的对抗性维度，提出了在 5G 环境中使用 AI/ML 的警示观点，并通过三个案例研究支持这一观点。我们还讨论了减轻这种对抗性 ML 风险的方法，为评估 ML 模型的稳健性提供指导，并更普遍地呼吁关注 5G 中以 ML 为导向的研究的问题。