Gabriela da Silva Machado, Gustavo Z. Bruno, Alexandre Huff, José Marcos Camara Brito, Cristiano B. Both
原文摘要
The disaggregation of the Radio Access Network (RAN) introduces architectural flexibility but significant operational complexity, necessitating automated management frameworks. For instance, current Open RAN (O-RAN) orchestration relies on fragmented manual policies, lacking end-to-end intent assurance from high-level requirements to low-level configurations. In this article, we propose ORION, an intent orchestration framework that integrates Large Language Models (LLMs) through the Model Context Protocol (MCP). It translates natural language slice requests into network policies aligned with O-RAN interfaces, supporting the AI-RAN agentic orchestration and AI-for-RAN paradigms. ORION combines an MCP-based Service Management and Orchestration (SMO) layer with a Non-Real-Time RIC rApp and a Near-Real-Time RIC xApp to enforce Physical Resource Block (PRB) quotas over E2. We evaluate eight LLM configurations across proprietary and open-weight models on a 100-intent workload. Claude Opus 4.5 and GPT-5 achieve 100% policy-creation success with 99% and 96% slice-type classification accuracy, while GPT-5 Nano achieves 97% policy-creation success. The evaluation also assesses MCP tool-use quality, cost-performance trade-offs, resource consumption, and end-to-end control-path latency.
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.