Adaptive Reallocation of RAN Functions for Resilient 6G Networks

2025-11-11

AI Summary

In 6G networks, RAN functional decoupling enhances flexibility but exacerbates cascading failures triggered by single-point faults, jeopardizing service continuity and network resilience. Method: This paper proposes an adaptive RAN functional reallocation mechanism for disaster recovery—first applying dynamic functional redeployment to cascading failure scenarios. It introduces a cascading-failure-aware self-healing framework integrating fault detection, cloud-based CU/DU re-instantiation, and functional chain reconstruction, optimized under joint computation-communication constraints to maximize throughput recovery. Contribution/Results: Evaluated on realistic network topologies, the approach achieves up to a 70% improvement in throughput recovery over conventional methods, significantly enhancing 6G network elasticity and robustness against cascading disruptions.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

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.

Problem

• Mitigating cascading RAN function failures in 6G networks
• Optimizing RAN function reallocation to restore service continuity
• Maximizing network throughput under computational and communication constraints

Innovation

• Adaptive placement of RAN functions
• Reinstantiating CU and DU in cloud
• Optimization maximizing post-failure network performance