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

2026-05-14

AI Summary

This study addresses the challenge of cascading failures in sixth-generation (6G) radio access networks (RANs) caused by functional decomposition, proposing the first resilience recovery mechanism tailored for disaggregated mobile networks. The approach dynamically re-instantiates Central Units (CUs) and Distributed Units (DUs) under uncertainty in user locations and channel states to restore disrupted service function chains and ensure service continuity. A two-stage stochastic optimization model is formulated, solved via Sample Average Approximation (SAA) through its deterministic equivalent, and jointly optimizes bandwidth allocation and routing decisions. Experimental evaluations on real-world network topologies demonstrate that the proposed method improves service recovery performance by up to 80% compared to conventional approaches.

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.

Problem

• resilience
• disaggregated RAN
• cascading failures
• 6G networks
• service continuity

Innovation

• stochastic optimization
• RAN disaggregation
• adaptive reinstantiation
• resilience mechanism
• Sample Average Approximation