Traditional rule-based or static management approaches struggle to cope with the dynamic, multi-layered nature of 5G/6G networks, creating a strong motivation for AI-native solutions – management systems built from the ground up with artificial intelligence – to enable autonomous, real-time network control. In this work, we introduce GNN-ATIVE, an AI-native orchestration framework that leverages Graph Neural Networks (GNNs) and knowledge graphs (KGs) in a unified graph-based paradigm for network management. GNN-ATIVE uses a semantic knowledge graph to represent the network’s state and context, employing standard ontologies to ensure consistency and interoperability. Building on this foundation, we design Knowledge Graph enabled Generative Pretrained Transformer (KG-GPT), a novel graph-to-graph Transformer model that performs knowledge-driven reasoning on the KG. KG ingests the structured network state (nodes, links, and attributes) and infers optimal configurations or management actions, serving as a high-level decision engine for the orchestrator. We implement and evaluate GNN-ATIVE on an Optical Transport Network (OTN) testbed using real network components. The results demonstrate that GNN-ATIVE can effectively manage OTN resources and adapt to network changes while achieving low-latency inference for decision making.

Network management systems for beyond 5G (B5G) and 6G networks today require efficient approaches for handling increased heterogeneity, network-function dis-aggregation, performance requirements, and optimizing networks to support highly diverse use cases. While the Open-Radio Access Network (O-RAN) Service Management and Orchestration (SMO) frameworks efficiently manage RAN and cloud infrastructure, the fragmentation of management platforms across RAN, Core Network (CN), and Transport Network (TN) introduces operational inefficiencies, particularly in Non-Public Networks (NPNs) deployments. This paper proposes a novel extension to the O-RAN SMO architecture, integrating CN and TN management functionalities into a unified control framework. By exploiting AI/ML-driven$\mathrm{x} / \text{rApps}$and a converged data analytics pipeline, the proposed architecture enhances SMO's fault management, resource optimization, and service continuity capabilities. Our implementation validates the feasibility of the proposed SMO extension, demonstrating subscriber-specific QoS assurance through O-RAN-based mobility management mechanisms. The proposed approach successfully shows how RAN-/Core-converged SMOs enable significant enhancements to O-RAN's x/rApps, allowing for subscriber-specific as well as application-specific differentiated QoS assurance.

The performance of IEEE 802.11-based wireless networks is determined by the multi-parameter resource control of transmit and receive settings. With today's WiFi transceivers, controllable parameters include data rate, power, frame transmission aggregation, and global receiver settings like sensitivity. Despite its importance, research on IEEE 802.11 MAC-layer resource control over the past two decades has primarily relied on simulations.

The quickly growing demand for wireless networks and the numerous application-specific requirements stand in stark contrast to today's inflexible management and operation of wireless networks. While most research focuses on mobile networks, WiFi is often left out of the purview. In this paper, we present and evaluate OpenSDWN, a novel WiFi architecture based on a joint software-defined network and network functions virtualization approach. OpenSDWN exploits virtualization across the wired and wireless network and introduces datapath programmability to enable service differentiation and fine-grained transmission control, facilitating the prioritization of critical applications. OpenSDWN implements per-client virtual access points and per-client virtual middleboxes, to render network functions more flexible and support mobility and seamless migration. Moreover, OpenSDWN also increases the security of upcoming WiFi HotSpot architectures by following a functional split approach. Finally, OpenSDWN can also be used to out-source the control over the home network to a participatory interface or to an Internet service provider.

Network Function Virtualization (NFV) is emerging as one of the most innovative concepts in the networking landscape. By migrating network functions from dedicated mid-dleboxes to general purpose computing platforms, NFV can effectively reduce the cost to deploy and to operate large networks. However, in order to achieve its full potential, NFV needs to encompass also the radio access network allowing Mobile Virtual Network Operators to deploy custom resource allocation solutions within their virtual radio nodes. Such requirement raises several challenges in terms of performance isolation and resource provisioning. In this work we formalize the Virtual Network Function (VNF) placement problem for radio access networks as an integer linear programming problem and we propose a VNF placement heuristic. Moreover, we also present a proof-of-concept implementation of an NFV management and orchestration framework for Enterprise WLANs. The proposed architecture builds upon a programmable network fabric where pure forwarding nodes are mixed with radio and packet processing nodes leveraging on general computing platforms.

No abstract available.

The quickly growing demand for wireless networks and the numerous application-specific requirements stand in stark contrast to today's inflexible management and operation of WiFi networks. In this paper, we present and evaluate OpenSDWN, a novel WiFi architecture based on an SDN/NFV approach. OpenSDWN exploits datapath programmability to enable service differentiation and fine-grained transmission control, facilitating the prioritization of critical applications. OpenSDWN implements per-client virtual access points and per-client virtual middleboxes, to render network functions more flexible and support mobility and seamless migration. OpenSDWN can also be used to out-source the control over the home network to a participatory interface or to an Internet Service Provider.

Software-Defined Networking (SDN) has received, in the last years, significant interest from the academic and the industrial communities alike. The decoupled control and data planes found in an SDN allows for logically centralized intelligence in the control plane and generalized network hardware in the data plane. Although the current SDN ecosystem provides a rich support for wired packet-switched networks, the same cannot be said for wireless networks where specific radio data-plane abstractions, controllers, and programming primitives are still yet to be established. In this work, we present a set of programming abstractions modeling the fundamental aspects of a wireless network, namely state management, resource provisioning, network monitoring, and network reconfiguration. The proposed abstractions hide away the implementation details of the underlying wireless technology providing programmers with expressive tools to control the state of the network. We also present a Software-Defined Radio Access Network Controller for Enterprise WLANs and a Python--based Software Development Kit implementing the proposed abstractions. Finally, we experimentally evaluate the usefulness, efficiency and flexibility of the platform over a real 802.11-based WLAN.

We present a demo of Odin, an SDN framework to program enterprise wireless local area networks (WLANs). Enterprise WLANs need to support a wide range of services and functionalities. This includes authentication, authorization and accounting, policy, mobility and interference management, and load balancing. WLANs also exhibit unique challenges. In particular, access point (AP) association decisions are not made by the infrastructure, but by clients. In addition, the association state machine combined with the broadcast nature of the wireless medium requires keeping track of a large amount of state changes. To this end, Odin builds on a light virtual AP abstraction that greatly simplifies client management. Odin does not require any client side modifications and its design supports WPA2 Enterprise. With Odin, a network operator can implement enterprise WLAN services as network applications.