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：自动化配置是实现日益复杂的移动网络中零接触网络管理的关键途径。深度学习技术在自动学习和处理高维网络问题方面展现出巨大潜力。然而，深度学习对输入空间偏移的脆弱性，在真实网络存在不可预测的变异性及仿真与现实差距的情况下，引发了越来越多的部署担忧。本文提出了一种新颖的RoNet框架，旨在提升神经辅助配置策略的鲁棒性。我们将网络配置问题形式化为在服务多样化用户应用时最大化性能效率的任务。我们设计了三个集成阶段，包括新颖的正常训练、学习攻击和鲁棒防御方法，以平衡策略的鲁棒性与性能。我们通过NS-3仿真器对RoNet进行了广泛评估，仿真结果表明，RoNet在鲁棒性、适应性和可扩展性方面均优于现有解决方案。