Gabriel M. Almeida, Joao F. Santos, Walid Saad, Nicola Marchetti, Kleber V. Cardoso
原文摘要
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.
Gustavo Z. Bruno, Gabriel M. Almeida, Aloizio Da Silva, Luiz A. DaSilva, Joao F. Santos, Alexandre Huff, Kleber V. Cardoso, Cristiano B. Both
原文摘要
The increasing energy consumption of next-generation mobile networks necessitates the adoption of autonomous and energy-aware management strategies. This article proposes a novel adaptive solution leveraging the Open Radio Access Network (O-RAN) architecture to optimize energy efficiency while maintaining Quality of Service (QoS). The proposed approach introduces a hierarchical O-RAN-aligned control framework in which a Non-Real-Time RAN Intelligent Controller (Non-RT RIC) periodically computes energy-aware policies from long-term historical data, while a Near-Real-Time RIC enforces those policies through per-User Equipment (UE) handover actions on a sub-second timescale. We formulate a joint energy- and QoS-aware load balancing problem as a Mixed-Integer Non-Linear Programming (MINLP) model. This model optimizes UE association across E2 Nodes to minimize transmission power and autonomously deactivate underutilized cells, while satisfying strict QoS constraints. To validate the proposed solution, we deploy it in an experimental environment that simulates massive sports events. Experimental results demonstrate 72% energy savings over a 24-hour trace and sub-second handover delays (0.123-0.204 s/UE), confirming the solution's feasibility for autonomous and sustainable 5G Advanced networks. This work provides guidelines for deploying energy-efficient strategies in O-RAN environments and underscores the potential of adaptive solutions for sustainable mobile communications.