The disaggregation of base stations into discrete RAN functions introduces new threats to mobile networks, as failures in one RAN function can trigger cascading failures and interrupt entire function chains, with potential to degrade network performance and disrupt service. In this paper, we propose the first resilience mechanism for disaggregated mobile networks that leverages the adaptive reinstantiation of RAN functions under uncertainty to mitigate disruptions and maintain service continuity in the presence of compromised infrastructure. Our mechanism reacts to cascading failures that disrupt Radio Units (RUs) by reinstantiating Central Units (CUs) and Distributed Units (DUs) in alternative cloud locations, restoring their function chains while accounting for uncertainty in users' locations and wireless channel conditions during the in-failure state. We formulate this recovery process as a two-stage stochastic optimization problem, where reinstantiation and routing decisions are made under uncertainty, and bandwidth allocation decisions are performed after uncertainty is resolved. We solve the problem using a Sample Average Approximation (SAA)-based solution as a tractable, deterministic equivalent problem. We numerically evaluate our approach on a real-world disaggregated mobile network topology across multiple failure scenarios and traffic demand conditions, and our results demonstrate that our solution can achieve up to 80% higher recovery performance compared to conventional resilience mechanisms.

翻译：基站分解为离散的RAN功能给移动网络引入了新威胁，单一RAN功能的失效可能引发级联故障并中断整个功能链，导致网络性能下降和服务中断。本文提出了首个面向分解式移动网络的韧性机制，该机制利用不确定性下RAN功能的自适应重实例化来缓解干扰，并在基础设施受损时保持服务连续性。针对中断无线单元（RU）的级联故障，本机制通过在替代云位置重实例化中央单元（CU）和分布式单元（DU）来恢复功能链，同时考虑故障状态下用户位置和无线信道条件的不确定性。我们将该恢复过程建模为两阶段随机优化问题：在不确定性下做出重实例化与路由决策，并在不确定性消除后执行带宽分配决策。采用基于样本平均逼近（SAA）的方法将原问题转化为可处理的确定性等价问题。基于真实分解式移动网络拓扑的数值评估表明，在多种故障场景和流量需求条件下，本方案相比传统韧性机制可提升高达80%的恢复性能。