← 研踪 PaperTrailarXiv:2611.14121 · v1
Criticality-Aware RAN Function Placement for Robust 6G Networks
首次提交 2026-11-10 22:09
版本更新 2026-11-10 22:09
雷达观测 2026-11-11 13:08
摘要
The disaggregation of base stations into radio access network (RAN) functions introduces new vulnerabilities to mobile networks, as disruptions in cloud locations can interrupt multiple RAN functions and trigger cascading failures, leading to widespread outages. In this paper, we propose a criticality-aware RAN function placement solution to improve the robustness of disaggregated mobile networks against random failures and targeted attacks. Our solution optimizes the placement of centralized units (CUs) and distributed units (DUs) across cloud locations to reduce functional dependencies and limit the criticality of cloud locations, while preserving the cost benefits of RAN function centralization. We formulate the placement as an optimization model that jointly minimizes functional dependency and placement cost, subject to criticality constraints, as well as computing and transport requirements. We numerically evaluated our approach on a real-world mobile network topology across multiple disruption scenarios and demonstrated that our solution improves robustness, yielding up to 75% more operational radio units (RUs) after disruptions than existing approaches.
作者备注：6 pages, 6 figures
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v1 · 2026-11-10 22:09 · 最新观测版本
Criticality-Aware RAN Function Placement for Robust 6G Networks
The disaggregation of base stations into radio access network (RAN) functions introduces new vulnerabilities to mobile networks, as disruptions in cloud locations can interrupt multiple RAN functions and trigger cascading failures, leading to widespread outages. In this paper, we propose a criticality-aware RAN function placement solution to improve the robustness of disaggregated mobile networks against random failures and targeted attacks. Our solution optimizes the placement of centralized units (CUs) and distributed units (DUs) across cloud locations to reduce functional dependencies and limit the criticality of cloud locations, while preserving the cost benefits of RAN function centralization. We formulate the placement as an optimization model that jointly minimizes functional dependency and placement cost, subject to criticality constraints, as well as computing and transport requirements. We numerically evaluated our approach on a real-world mobile network topology across multiple disruption scenarios and demonstrated that our solution improves robustness, yielding up to 75% more operational radio units (RUs) after disruptions than existing approaches.
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