Automating configuration is the key path to achieving zero-touch network management in ever-complicating mobile networks. Deep learning techniques show great potential to automatically learn and tackle high-dimensional networking problems. The vulnerability of deep learning to deviated input space, however, raises increasing deployment concerns under unpredictable variabilities and simulation-to-reality discrepancy in real-world networks. In this paper, we propose a novel RoNet framework to improve the robustness of neural-assisted configuration policies. We formulate the network configuration problem to maximize performance efficiency when serving diverse user applications. We design three integrated stages with novel normal training, learn-to-attack, and robust defense method for balancing the robustness and performance of policies. We evaluate RoNet via the NS-3 simulator extensively and the simulation results show that RoNet outperforms existing solutions in terms of robustness, adaptability, and scalability.

翻译：在不断复制的移动网络中,自动化配置是实现零触摸网络管理的关键途径。深层学习技术显示出自动学习和解决高维网络问题的巨大潜力。但是,深层学习偏差输入空间的脆弱性在不可预测的变异性和真实世界网络的模拟到现实差异下引起越来越多的部署问题。在本文中,我们提议了一个新的网络框架,以提高神经辅助配置政策的稳健性。我们设计网络配置问题,以便在为各种用户应用程序服务时最大限度地提高性能效率。我们设计了三个综合阶段,采用新颖的正常培训、学习到攻击和强健的防御方法来平衡政策的稳健性和绩效。我们通过NS-3模拟器对RONet进行了广泛评估,模拟结果显示RoNet在稳健性、适应性和可扩缩性方面超越了现有的解决方案。