Cloud radio access networks (RANs) enable cost-effective management of mobile networks by dynamically scaling their capacity on demand. However, deploying adaptive controllers to implement such dynamic scaling in operational networks is challenging due to the risk of breaching service agreements and operational constraints. To mitigate this challenge, we present a novel method for learning the safe operating region of the RAN, i.e., the set of resource allocations and network configurations for which its specification is fulfilled. The method, which we call (C)ausal (O)nline (L)earning, operates in two online phases: an inference phase and an intervention phase. In the first phase, we passively observe the RAN to infer an initial safe region via causal inference and Gaussian process regression. In the second phase, we gradually expand this region through interventional Bayesian learning. We prove that COL ensures that the learned region is safe with a specified probability and that it converges to the full safe region under standard conditions. We experimentally validate COL on a 5G testbed. The results show that COL quickly learns the safe region while incurring low operational cost and being up to 10x more sample-efficient than current state-of-the-art methods for safe learning.

翻译：云无线接入网络（RAN）通过按需动态扩展容量，实现了移动网络的经济高效管理。然而，在运营网络中部署自适应控制器以实现这种动态扩展具有挑战性，因为存在违反服务协议和运营约束的风险。为缓解这一挑战，我们提出了一种学习RAN安全运行区域的新方法，即满足其规范要求的资源分配和网络配置集合。该方法我们称之为（因）果（在）线（学）习，在两个在线阶段运行：推理阶段和干预阶段。在第一阶段，我们被动观察RAN，通过因果推断和高斯过程回归推断初始安全区域。在第二阶段，我们通过干预式贝叶斯学习逐步扩展该区域。我们证明COL确保学习到的区域以指定概率是安全的，并且在标准条件下收敛到完整安全区域。我们在5G测试平台上对COL进行了实验验证。结果表明，COL能快速学习安全区域，同时产生较低的运营成本，并且比当前最先进的安全学习方法样本效率高出高达10倍。