arXiv subjects

Cristiano B. Both

Publications and source records attributed to Cristiano B. Both.

6 recordsLinked to original sources

arXiv2026

Towards Energy- and QoS-aware Load Balancing for 5G Advanced: Leveraging O-RAN to Achieve Sustainability and Energy Efficiency

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 O-RAN architecture to optimize energy efficiency while managing its trade-off with QoS. The proposed approach introduces a hierarchical O-RAN-aligned control framework in which a Non-RT RIC periodically computes energy-aware policies from long-term historical data, while a Near-RT RIC enforces those policies through per-UE handover actions on a sub-second timescale. We formulate a joint energy- and QoS-aware load balancing problem as a MINLP model. This model optimizes UE association across O-RUs to minimize transmission power and autonomously deactivate underutilized cells, while enforcing per-UE throughput requirements as explicit constraints at each optimization cycle. 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. An offline physical-layer throughput evaluation further characterizes the trade-off between energy efficiency and QoS, establishing the operational limits of dynamic cell deactivations. This work provides guidelines for deploying energy-efficient strategies in O-RAN environments and underscores the potential of adaptive solutions for sustainable mobile communications.

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arXiv2026

TriSLA: A Preventive and Closed-Loop SLA-Aware Architecture for Multidomain Decision-Making with Explainable Artificial Intelligence in 5G Networks

Network slicing in multidomain 5G environments introduces critical challenges in guaranteeing Service Level Agreements (SLAs) under dynamic resource variability and heterogeneous service requirements. This article presents TriSLA, a closed-loop, preventive, SLA-aware architecture designed to evaluate feasibility at request time and continuously ensure SLA compliance during operation. The architecture combines ontology-driven semantic intent interpretation, multidomain machine learning feasibility risk inference, Explainable Artificial Intelligence (XAI) feature attribution, and closed-loop runtime SLA assurance into a unified operational pipeline. A fully operational prototype was evaluated in a multi-node cloud-native environment integrating Radio Access Network (RAN), Transport Network (TN), and 5G Core (5GC) domains with real-time telemetry collection. Experimental evaluation demonstrates that TriSLA guarantees a 100% SLA satisfaction rate for admitted slices, completely eliminating post-deployment violations compared to reactive (51.2%) and static threshold (80.4%) admission baselines. The predictive feasibility assessment achieved a classification accuracy of up to 99.51% (98.68% for the default explainable Random Forest classifier), enabling preventive admission decisions before infrastructure commitment. Furthermore, the cognitive admission pipeline introduces minimal processing overhead, requiring 25.37 ms for ontology-driven semantic parsing and 231.66 ms for XAI-assisted feasibility inference. Concurrently, the closed-loop assurance engine resolves 100% of runtime telemetry anomalies within a 4.22 s recovery cycle. These results demonstrate that TriSLA provides reliable, explainable, transparent, and preventive SLA management through integrated predictive admission and closed-loop runtime assurance for next-generation 5G networks.

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arXiv2026

ORION: Intent-Aware Orchestration in Open RAN for SLA-Driven Network Management

The disaggregation of the Radio Access Network (RAN) introduces unprecedented flexibility but significant operational complexity, necessitating automated management frameworks. However, 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 paper, we propose ORION, an O-RAN compliant intent orchestration framework that integrates Large Language Models (LLMs) via the Model Context Protocol (MCP) to translate natural language intents into enforceable network policies. ORION leverages a hierarchical agent architecture, combining an MCP-based Service Management and Orchestration (SMO) layer for semantic translation with a Non-Real-Time RIC rApp and Near-Real-Time RIC xApp for closed-loop enforcement. Extensive evaluations using GPT-5, Gemini 3 Pro, and Claude Opus demonstrate a 100% policy generation success rate for high-capacity models, highlighting significant trade-offs in reasoning efficiency. We show that ORION reduces provisioning complexity by automating the complete intent lifecycle, from ingestion to E2-level enforcement, paving the way for autonomous 6G networks.

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arXiv2025

Managing O-RAN Networks: xApp Development from Zero to Hero

The Open Radio Access Network (O-RAN) Alliance proposes an open architecture that disaggregates the RAN and supports executing custom control logic in near-real time from third-party applications, the xApps. Despite O-RAN's efforts, the creation of xApps remains a complex and time-consuming endeavor, aggravated by the sometimes fragmented, outdated, or deprecated documentation from the O-RAN Software Community (OSC). These challenges hinder academia and industry from developing and validating solutions and algorithms on O-RAN networks. This tutorial addresses this gap by providing the first comprehensive guide for developing xApps to manage the O-RAN ecosystem from theory to practice. We provide a thorough theoretical foundation of the O-RAN architecture and detail the functionality offered by Near Real-Time RAN Intelligent Controller (Near-RT RIC) components. We examine the xApp design and configuration. We explore the xApp lifecycle and demonstrate how to deploy and manage xApps on a Near-RT RIC. We address the xApps' interfaces and capabilities, accompanied by practical examples. We provide comprehensive details on how xApps can control the RAN. We discuss debugging strategies and good practices to aid the xApp developers in testing their xApps. Finally, we review the current landscape and open challenges for creating xApps.

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arXiv2023

Optimal Resource Allocation with Delay Guarantees for Network Slicing in Disaggregated RAN

In this article, we propose a novel formulation for the resource allocation problem of a sliced and disaggregated Radio Access Network (RAN) and its transport network. Our proposal assures an end-to-end delay bound for the Ultra-Reliable and Low-Latency Communication (URLLC) use case while jointly considering the number of admitted users, the transmission rate allocation per slice, the functional split of RAN nodes and the routing paths in the transport network. We use deterministic network calculus theory to calculate delay along the transport network connecting disaggregated RANs deploying network functions at the Radio Unit (RU), Distributed Unit (DU), and Central Unit (CU) nodes. The maximum end-to-end delay is a constraint in the optimization-based formulation that aims to maximize Mobile Network Operator (MNO) profit, considering a cash flow analysis to model revenue and operational costs using data from one of the world's leading MNOs. The optimization model leverages a Flexible Functional Split (FFS) approach to provide a new degree of freedom to the resource allocation strategy. Simulation results reveal that, due to its non-linear nature, there is no trivial solution to the proposed optimization problem formulation. Our proposal guarantees a maximum delay for URLLC services while satisfying minimal bandwidth requirements for enhanced Mobile BroadBand (eMBB) services and maximizing the MNO's profit.

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arXiv2023

RIC-O: Efficient placement of a disaggregated and distributed RAN Intelligent Controller with dynamic clustering of radio nodes

The Radio Access Network (RAN) is the segment of cellular networks that provides wireless connectivity to end-users. O-RAN Alliance has been transforming the RAN industry by proposing open RAN specifications and the programmable Non-Real-Time and Near-Real-Time RAN Intelligent Controllers (Non-RT RIC and Near-RT RIC). Both RICs provide platforms for running applications called rApps and xApps, respectively, to optimize the behavior of the RAN. We investigate a disaggregation strategy of the Near-RT RIC so that its components meet stringent latency requirements while presenting a cost-effective solution. We propose the novel RIC Orchestrator (RIC-O) that optimizes the deployment of the Near-RT RIC components across the cloud-edge continuum. Edge computing nodes often present limited resources and are expensive compared to cloud computing. For example, in the O-RAN Signalling Storm Protection, Near-RT RIC is expected to support end-to-end control loop latencies as low as 10ms. Therefore, performance-critical components of Near-RT RIC and certain xApps should run at the edge while other components can run on the cloud. Furthermore, RIC-O employs an efficient strategy to react to sudden changes and re-deploy components dynamically. We evaluate our proposal through analytical modeling and real-world experiments in an extended Kubernetes deployment implementing RIC-O and disaggregated Near-RT RIC.

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