Adaptive Reallocation of RAN Functions for Resilient 6G Networks

TL;DR

Addresses resilience in disaggregated 6G RANs where cascading failures can disrupt the CU–DU–RU function chain. Proposes an autonomous resilience mechanism that re-instantiates CU and DU across available cloud locations to re-create function chains and restore service. Formulated as a mixed ILP that jointly accounts computing workloads, routing, and latency constraints to maximize post-failure throughput. Experiments on a real-world topology show up to 70% throughput improvement over existing resilience approaches and strong resilience across varying failure severities, indicating practical viability.

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 disrupt the entire functional chain, impacting network performance and leading to outages. In this paper, we propose the first resilience mechanism leveraging the adaptive placement of RAN functions to mitigate disruptions and recover service continuity in the presence of compromised infrastructure. Our model detects disrupted RUs due to cascading failures, reacts by re-instantiating CU and DU in alternative cloud locations, and recovers service continuity by reestablishing functional chains. We formulate this recovery process as an optimization problem that maximizes post-failure network performance while considering computational and communication constraints of the infrastructure. We numerically evaluated our approach on a real-world mobile network topology under multiple failure scenarios, and demonstrated that our solution recovers up to 70% higher throughput compared to conventional resilience mechanisms.

Figures (6)

• Figure 1: Disaggregated mobile networks are susceptible to cascading failures, as disruption in a single RAN function can compromise the entire function chain and cause outages.
• Figure 2: Evolution of the network utility over time, showing the detrimental impact of failures and the objective of our resilience mechanism to restore service and recover utility.
• Figure 3: Temporal evolution of network utility. After a disruption ($t_0$), the network stabilizes ($t_d$) and triggers our resilience solution ($t_u$) defining the recovery policy ($t_s$) recovering utility ($t_r$).
• Figure 4: Recovery performance of different resilience mechanisms. Across all scenarios, our solution achieves higher network utility after recovery compared to baseline approaches.
• Figure 5: Comparison of throughput resilience. Our solution achieves higher resilience across all scenarios presenting 80% recovery for most cases and up to 96% in larger topologies.