Resilience under Uncertainty: Securing 6G through Stochastic Reinstantiation of RAN Functions

TL;DR

This paper introduces a novel resilience mechanism for 6G disaggregated RANs, using stochastic optimization to adaptively reinstate functions and mitigate cascading failures under uncertainty.

Contribution

It presents the first adaptive reinstantiation approach for disaggregated mobile networks, formulated as a two-stage stochastic optimization problem and solved with SAA.

Findings

Achieves up to 80% higher recovery performance compared to conventional methods.

Effectively restores RAN functions during cascading failures in real-world topologies.

Demonstrates robustness across multiple failure scenarios and traffic demands.

Abstract

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.