Toward End-to-End Intent-Based Networking for 6G: Architectures, Challenges, Recent Advances, and Future Directions

Performance Evaluation of Control Plane Signaling in 5G: A Novel Adaptive Software-Edge Architecture

CORMO-RAN: Energy Efficiency at the Near-RT RIC via Lossless Migration of O-RAN xApps

Open Radio Access Network (RAN) is a key paradigm to attain unprecedented flexibility of the RAN via disaggregation and Artificial Intelligence (AI)-based applications called xApps. In dense areas with many active RAN nodes, compute resources are engineered to support potentially hundreds of xApps monitoring and controlling the RAN to achieve operator's intents. However, such resources might become underutilized during low-traffic periods, where most cells are sleeping and, given the reduced RAN complexity, only a few xApps are needed for its control. In this paper, we propose CORMO-RAN, a data-driven orchestrator that dynamically activates compute nodes based on xApp load to save energy, and performs lossless migration of xApps from nodes to be turned off to active ones while ensuring xApp availability during migration. CORMO-RAN tackles the trade-off among service availability, scalability, and energy consumption while (i) preserving xApps'internal state to prevent RAN performance degradation during migration; (ii) accounting for xApp diversity in state size and timing constraints; and (iii) implementing several migration strategies and providing guidelines on best strategies to use based on resource availability and requirements. We prototype CORMO-RAN as an rApp, and experimentally evaluate it on an O-RAN private 5G testbed hosted on a Red Hat OpenShift cluster with commercial radio units. Results demonstrate that CORMO-RAN is effective in minimizing energy consumption of the RAN Intelligent Controller (RIC) cluster, yielding up to 64% energy saving when compared to existing approaches.

Intent-Based Networking for Vehicular Service Deployment

Next-generation networks $(5 \mathrm{G} / 6 \mathrm{G})$ provide capabilities such as network slicing, enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communications (URLLC), and edge computing. However, configuring the related radio, core, slicing, and edge resources requires significant operational expertise. This work proposes an Intent-Based Networking (IBN) framework that combines Natural Language Processing, Large Language Models (LLMs), TMF921-compliant intent representation, and CAMARA APIs to simplify the definition and activation of network-sensitive vehicular services. In the proposed model, a Service Provider establishes a business agreement with a Network Operator, while the operator administrator defines service requirements through a conversational interface. These requirements are translated into machine-readable intents and mapped into network and edge orchestration actions. The framework is evaluated through a Teleoperated Driving (ToD) use case for autonomous vehicle repositioning. Results show that the intent translation and management pipeline introduces limited and repeatable overhead, while orchestration time is mainly affected by the underlying MANO/IaaS platform. The results indicate that combining IBN and CAMARA APIs can support flexible service preparation by operators and dynamic service consumption by applications.

AI-driven orchestration for integrated satellite-terrestrial 6G networks: Architecture, handover management, and testbed evaluation

The integration of terrestrial and Non-Terrestrial Networks (NTNs) is a cornerstone of the sixth-Generation (6G) vision, yet orchestrating artificial intelligence (AI) workloads across these heterogeneous domains remains an open challenge. This paper introduces an AI-driven orchestration architecture for integrated satellite-terrestrial 6G networks that extends four components of our previously proposed AI-native design, namely the 3rd Generation Partnership Project (3GPP) enhanced Network Data Analytics Function (NWDAF), Service Hosting Environment (SHE), Network Knowledge Exposure Function (NKEF), and AI agent framework, with NTN-specific capabilities. Drawing on a systematic analysis of the 20 ubiquitous connectivity use cases and the five AI-NTN convergence use cases from the 3GPP Technical Report (TR) 22.870 document, we conceptualize these architectural extensions: (i) an NTN-enhancement to NWDAF for ingesting satellite telemetry and ephemeris data to be used in predictive handover and coverage analytics, (ii) a space-edge computing tier within SHE that enables onboard satellite AI inference with a latency-aware model placement framework, (iii) a cross-domain AI agent for intent-driven orchestration across terrestrial and satellite operator boundaries, and (iv) an NKEF feature exposing constellation topology and coverage predictions to third-party applications. In order to demonstrate the effectiveness of this orchestration architecture, we present a handover management framework addressing four transition types (satellite-to-satellite, satellite-to-terrestrial hand-out, terrestrial-to-satellite hand-in, and inter-orbit) with backhaul-aware path selection leveraging inter-satellite links. Next, a comparative evaluation of eight NTN testbed platforms identifies current validation capabilities and gaps with respect to the discussed end-to-end system. [...]

Emulating the SDN-Enabled Computing Continuum through Lightweight Virtualization

In recent years, the number of Internet-connected devices has increased notably, leading to a significant rise in data traffic. This increase has been fostered by the Internet of Things paradigm, the use of microservices architectures in application development, and the ability to deploy these applications across various layers in the Computing Continuum (including Fog, Edge, and Cloud layers). Consequently, choosing the right deployment strategy has become essential for network operators and developers, especially in intensive domains such as smart cities. In this work, we introduce an emulation framework that allows developers and operators to decide how to deploy networks, computing devices, and applications in a Computing Continuum environment, ensuring compliance with established Quality of Service standards. This framework supports both IP and SDN network paradigms and is highly adaptable to different scenarios due to its use of container-based virtualization. Furthermore, the SDN paradigm provides flexibility, enabling the implementation of a service discovery feature that simplifies communication between end devices and services. Evaluations conducted in a realistic smart city scenario show that this framework can be extended and applied to a wide range of situations and configurations, meeting the needs of the research community in the Computing Continuum domain.

Mitigating Security Challenges in 5G Wireless Networks

5G networks increasingly rely on key enabling technologies such as Software-Defined Networking (SDN), Network Function Virtualization (NFV), Multi-Access Edge Computing (MEC), and end-to-end network slicing to deliver heterogeneous services with strict quality-of-service (QoS) guarantees. However, programmability, multi-tenancy, and distributed edge–cloud operation significantly expand the attack surface. At the same time, traditional rule-based and reactive security mechanisms remain slow to adapt and may violate latency constraints during mitigation. This paper addresses the problem of QoS-compliant, closed-loop security control for sliced SDN/NFV infrastructures. We propose an AI-assisted, cross-layer security orchestration framework that integrates epoch-wise telemetry with ML-based risk estimation and formalizes mitigation as a Constrained Markov Decision Process (CMDP). The CMDP controller selects enforceable actions— slice isolation, rate limiting, traffic rerouting, and key reconfiguration—while explicitly satisfying latency/overhead constraints, and executes them via SDN flow-rule updates and NFV policy/VNF reconfiguration. Simulation results over 50 decision epochs demonstrate effective response to an injected high-risk event: risk spikes to 0.95 at epoch 15, after which the controller drives risk toward ≈0.10 while maintaining latency below the 40ms QoS bound (with a transient rise during mitigation and subsequent stabilization). The reward trajectory briefly degrades during disruption but recovers and converges to a positive long-term return, indicating stable constraint-aware operation. This work provides (i) a deployable cross-layer orchestration architecture for sliced networks, (ii) a QoS-constrained CMDP decision model that converts risk signals into actionable SDN/NFV controls, and (iii) empirical evidence that adaptive mitigation can reduce security risk without sacrificing service guarantees.