Mobile networks require safe optimization to adapt to changing conditions in traffic demand and signal transmission quality, in addition to improving service performance metrics. With the increasing complexity of emerging mobile networks, traditional parameter tuning methods become too conservative or complex to evaluate. For the first time, we apply safe Bayesian optimization to mobile networks. Moreover, we develop a new safe collaborative optimization algorithm called CoSBO, leveraging information from multiple optimization tasks in the network and considering multiple safety constraints. The resulting algorithm is capable of safely tuning the network parameter online with very few iterations. We demonstrate that the proposed method improves sample efficiency in the early stages of the optimization process by comparing it against the SafeOpt-MC algorithm in a mobile network scenario.

翻译：移动网络在提升服务性能指标的同时，需要进行安全优化以适应流量需求和信号传输质量的动态变化。随着新兴移动网络复杂度的日益增加，传统参数调优方法变得过于保守或难以评估。本研究首次将安全贝叶斯优化应用于移动网络，并提出一种名为CoSBO的新型安全协作优化算法，该算法利用网络中多个优化任务的信息，并考虑多重安全约束。所提出的算法能够以极少的迭代次数实现网络参数在线安全调优。通过在移动网络场景中与SafeOpt-MC算法进行对比实验，我们证明该方法在优化过程早期显著提升了样本效率。