2026· IEEE Transactions on Network and Service Management· Vol 23, pp. 6222-6232· 0 citations· 23 references
Computer Science
TL;DR
A new Open RAN KPI monitoring framework that outperforms fixed-interval solutions by dynamically adjusting the KPI collection timescale at runtime, using Risk Analysis of monitored KPI accuracy loss to balance monitoring needs and resource usage is introduced.
Abstract
Open RAN emerged to break out of vendor lock-in constraints, enabling specialized control-plane applications with well-defined open and standardized interfaces. In a monitoring-oriented Real-Time Radio Access Network Intelligent Controller, xApps typically collect Key Performance Indicators (KPIs) at fixed intervals, which can overload the system when very frequent reporting is required. This paper introduces a new Open RAN KPI monitoring framework that outperforms fixed-interval solutions by dynamically adjusting the KPI collection timescale at runtime, using Risk Analysis of monitored KPI accuracy loss to balance monitoring needs and resource usage. A prototype built with OpenAirInterface and FlexRIC shows reduced E2 signaling overhead and significant energy savings. Extrapolating to large-scale scenarios, the framework demonstrates potential savings of up to 608.48 kWh per day (18,254.4 kWh per month), equivalent to an estimated reduction of 0.99 tons of $CO_{2}$ emissions.
The transition to virtualized Radio Access Networks (vRAN) enables dynamic power control through fine-grained CPU resource management. However, existing approaches treat gNB as a monolithic entity, failing to exploit the heterogeneous computational characteristics of different protocol layers. This paper proposes ALARM...
Ali Srour, Farzad Veisi, Sami Taktak et al.· 0 citations
An autonomic control architecture based on the Monitor-Analyze-Plan-Execute with Knowledge loop is integrated with a Random Forest classifier that predicts four discrete QoS operational states with 91.9% accuracy, making the compliance gap explicit and quantifiable.
Jamal Et-Tousy, A. Zyane· EPJ Web of Conferences· 0 citations
This paper designs two AI-driven xApps running on the near-RT RIC that implement proactive CU-UP scaling based on machine-learning traffic forecasting that highlight that ML-driven CU-UP scaling is a practical and effective mechanism to reduce the power consumption, improve the energy efficiency, and the performance of...
Dimitris Kefalas, N. Makris, Serge Fdida et al.· IEEE Open Journal of the Com...· 0 citations
Open Radio Access Network (O-RAN) architectures extend programmability in next-generation cellular networks through Near-Real-Time and Non-Real-Time RAN Intelligent Controllers (RICs), enabling AI/ML-driven xApps and rApps to optimize network behavior dynamically. However, validating automated closed-loop control algor...
M. Catalan-Cid, Joan Josep Aleixendri, A. Betzler et al.· 0 citations
This paper presents the design, implementation, and experimental validation of an intelligent xApp for the Near-Real-Time RAN Intelligent Controller (Near-RT RIC), targeting 5G Open Radio Access Network (O-RAN) small-cell deployments. The system operates over three contiguous 100 MHz carriers in the 3.5-3.8 GHz band (3...
Mohammad Yaser Ammar, Ali Ahmed, Atef A. Aburas et al.· International Symposium on N...· 0 citations
Energy consumption is a dominant operational cost and a chief sustainability concern for Mobile Network Operators (MNOs), and the Radio Access Networks (RANs) alone account for around 73% of total network energy expenditures. Switching off capacity Radio Frequency (RF) carriers at times of low service demand is the mos...
Samuel Knight, Orlando E. Martínez-Durive, Ana Martínez-González et al.· Proceedings of the ACM Works...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
