The Int5Gent approach integrates advanced technologies across the data plane, control plane, and application layers to enable high-capacity, low-latency, and flexible network solutions for demanding vertical applications in beyond 5G (B5G) networks. Focusing on edge computing, Sigma-Delta over-Fiber (SDoF), Digital Radioover- Fiber (D-RoF), Analog Radio-over-Fiber (A-RoF), millimeter-wave (mmWave) backhaul, and GPU-enabled processing, it supports Artificial Intelligence (AI)-driven use cases such as realtime video analysis and critical communication for Public Protection and Disaster Relief (PPDR). The platform demonstrators show: a) real-time human detection and AI inference at 15 frames per second (fps), showcasing network resiliency and integration of A-RoF with D-band transceivers for high-speed video transmission and b) the system's ability to monitor critical infrastructure (railway), using edge-based processing for safety- critical applications and dynamic transport slicing for operational needs. Both demonstrators validated the platform's ability to orchestrate and optimize resources in real-time, offering scalable, resilient solutions for future 5G deployments.

Effective management of end-to-end 6G network services is crucial, with peak capacity requirements for 6G transport connections expected to exceed 1 Tb/s. As demand for high bandwidth rises, there is a growing necessity for high-capacity optical fiber links, including ultra-wideband (UWB) and multiple fiber links within the network. Scaling up to accommodate these demands, designing wavelength-selective switches (WSSs) for such networks significantly increases the port count. To tackle this issue, we propose various multi-granular optical node (MG-ON) architectures utilizing heterogeneous wavelength, waveband, and spatial switching. We evaluate these architectures’ performance against high-capacity wavelength division multiplexed (WDM) networks through various simulation parameters.

Multi-access edge computing (MEC) provides IT resources at the edge of the network. Proximity to the users significantly enhances real-time gaming experiences because of the available high-bandwidth and low-latency network connection, which is essential for modern gaming demands. Leveraging TeraFlowSDN (TFS), a cloud-native open-source Software Defined Networking (SDN) controller, we introduce a novel MEC bandwidth management service that extends TFS’s API capabilities to handle standardized bandwidth requests. Our approach facilitates the provisioning of network bandwidth through interaction between an extended open-source gaming client and TFS. This integration enables dedicated bandwidth allocation, prioritizes gaming traffic, and ultimately improves the user experience by delivering high-bandwidth, low-latency services tailored to end-user applications. This work demonstrates a practical, scalable solution for optimizing network resources in latency-sensitive environments like gaming.

We experimentally demonstrate the dynamic reconfiguration of WDM VNTs in response to SDM spatial channel failures. We present an SDN control architecture with gRPC-telemetry and analytics to detect failures and restore failed virtual WDM links ©2022 The Authors.

Unsupervised learning (UL) is a technique to detect previously unseen anomalies without needing labeled datasets. We propose the integration of a scalable UL-based inference component in the monitoring loop of an SDN-controlled optical network. © 2022 The Author(s)

This demonstration will showcase the end-to-end orchestration of virtual network functions in the full-fledged ADRENALINE Testbed Cloud Platform expanding from the edge to the cloud. The Management and Orchestration (MANO) software ETSI OpenSource MANO (OSM) is used to deploy and handle a multi-site network service involving both edge and core Data Centers (DCs). Besides, the inter- and intra-DC connectivity is directly managed by a novel OSM WAN Infrastructure Manager (WIM) connector using the Transport API (TAPI) interface, thus completely abstracting the details of the underlying SDN controllers handling the programmability of the WAN network interconnecting the DCs.

This paper presents a beyond 5G fronthaul network with dynamic beamforming and -steering. The proposed fronthaul solution deploys optical beamforming (OBF) by combining space division multiplexing (SDM), analogue radio-over-fiber (ARoF), and the novel optical beam forming network (OBFN) technologies. From the service management and orchestration (MANO) point of view, the proposed fronthaul solution also deploys an advanced software defined networking (SDN) and Network Function Virtualization (NFV) control and orchestration architecture developed with the goal to optimally manage and reconfigure the physical layer resources (i.e., optical and radio) at the central office and cell sites (i.e., pool of baseband units (BBUs), remote radio heads (RRHs), ARoF transceivers and OBFNs). The proposed beyond 5G fronthaul architecture is primarily oriented to deploy massive machine-type communication (mMTC) services with high-bandwidth requirements, such as for industry 4.0. In this paper we experimentally validate the novel OBFN system, and the dynamic SDN/NFV MANO of the transport connectivity and network services for optical beamforming. The obtained experimental results show that the overall delay for the provisioning and removal of an OBF service, considering the contribution of the involved optical and radio systems and the SDN/NFV MANO layer, is 134s and 18s respectively. The reconfiguration of the OBF service to add or remove a beam can be performed in the range of 65-87s.

The need of telecommunications operators to reduce Capital and Operational Expenditures in networks which traffic is continuously growing has made them search for new alternatives to simplify and auto-mate their procedures. Because of the different transport network segments and multiple layers, the deployment of end-to-end services is a complex task. Also, because of the multiple vendor existence, the control plane has not been fully homogenized, making end-to-end connectivity services a manual and slow process, and the allocation of computing resources across the entire network a difficult task. The new massive capacity requested by Data Centers and the new 5G connectivity services will urge for a better solution to orchestrate the transport network and the distributed computing resources. This article presents and demonstrates a Network Slicing solution together with an end-to-end service orchestration for transport networks. The Network Slicing solution permits the co-existence of virtual networks (one per service) over the same physical network to ensure the specific service requirements. The network orchestrator allows automated end-to-end services across multi-layer multi-domain network segments making use of the standard Transport API (TAPI) data model for both 10 and 12 layers. Both solutions will allow to keep up with beyond 5G services and the higher and faster demand of network and computing resources.

Network operators have been dealing with the necessity of a dynamic network resources allocation to provide a new generation of customer-tailored applications. In that sense, Telecom providers have to migrate their BSS/OSS systems and network infrastructure to more modern solutions to introduce end-to-end automation and support the new use cases derived from the 5G adoption and transport network slices. In general, there is a joint agreement on making this transition to an architecture defined by programmable interfaces and standard protocols. Hence, this paper uses the iFusion architecture to control and program the network infrastructure. The work presents an experimental validation of the network slicing instantiation in an IP/Optical environment using a set of standard protocols and interfaces. The work provides results of the creation, modification and deletion of the network slices. Furthermore, it demonstrates the usage of standard communication protocols (Netconf and Restconf) in combination with standard YANG data models.

This demo shows a cloud-native SDN controller that can estimate end-to-end QoT using both analytical (GNPy) and machine learning algorithms on WDM systems. This transport SDN controller is able to scale horizontally using custom metrics.

We present a cloud-native architecture with a machine learning QoT predictor that enables cognitive functions in transport SDN controllers. We evaluate the QoT predictor training and auto-scaling capabilities in a real WDM/SDM testbed.

We propose and demonstrate a set of microservice-based security components able to perform physical layer security assessment and mitigation in optical networks. Results illustrate the scalability of the attack detection mechanism and the agility in mitigating attacks.

This paper presents an experimental evaluation of network protocols for control and management of optical networks and optical network equipment as seen in current trends. This paper presents the YANG data modeling language and its associated RESTCONF/NETCONF protocols. Later, it details multiple data models used in optical networks, such as IETF TEAS, ONF Transport API, OpenROADM, and OpenConfig. It also presents multiple protocols for telemetry (i.e., YANG PUSH, gRPC, and gNMI). Later, a zero-touch SDN controller architecture for multiple standard-defining organizations (SDOs) is presented, to support the usage of multiple protocols in a zero-touch optical network. Finally, the presented protocol implementations are experimentally evaluated and compared in terms of latency and overhead. The paper explores the use of these protocols (e.g., in research, demonstrations, and open-source optical networking projects) and provides results and recommendations for their integration in equipment and networks.

We experimentally demonstrate a 5G digital fronthaul network that relies on multi-adaptive bandwidth/bitrate variable transceivers (BVTs) and an autonomic software-defined networking (SDN) control system for partially-disaggregated wavelength division multiplexing (WDM)/space division multiplexing (SDM). Transmission of 256-QAM 760.32 MHz orthogonal frequency-division multiplexing (OFDM) radio signal is performed, with a total radio transmission capacity of 5.667 Gb/s. Digitized signal samples are carried as a 22.25 Gb/s digitized radio-over-fiber (DRoF) data stream and transmitted over a WDM/SDM infrastructure including 40-wavelength 100-GHz arrayed waveguide gratings (AWGs) and 19-core fiber. The autonomic SDN controller deploys a control loop for the multi-adaptive OFDM-based BVTs that monitors the per-subcarrier signal to noise ratio (SNR) and assigns the optimal constellation based on the actual signal degradation. An error vector magnitude (EVM) below the targeted 2.1% is achieved while setting up connections in less than 5 s.

This paper presents and experimentally validates an SDN/NFV architecture for the management of optical beamforming connectivity and network services for beyond 5G fronthaul, controlling OBFN coherent and incoherent systems, ARoF transceivers, BBUs with analog output/input and RRHs.

Current SDN controllers are monolithic applications that are run on dedicated servers and require specific protocols for synchronization among them. These SDN controllers do not provide the required flexibility to scale-out in case of a cloud-scale number of connectivity service requests. In this demonstration, We present the control of transport networks based on ONF Transport API using a cloud-native SDN controller based on micro-services. This demo provides insights on novel software implementations of transport network control technologies, such as container-based control architectures and standard interfaces. The proposed SDN controller components synchronize among them using gRPC protocol and a defined protocol buffer.