The open, virtualized, and disaggregated nature of Open Radio Access Network (O-RAN) designs has brought new security issues to mobile networks. Strong and flexible security measures are required in response to these issues, and the integration with existing IT security tools such as Security Information and Event Management (SIEM) systems may represent a viable solution. The method for combining SIEM with O-RAN is presented in this study in order to improve security threat detection and mitigation in mobile and IT settings. In order to improve real-time anomaly detection and reaction, the proposed system will design a specific xApp that acts as an agent to pass events from mobile networks to the SIEM. This would address threats like jamming and Distributed Denial of Service (DDoS) assaults. In addition to improving Intrusion Detection Systems' (IDS) capabilities, SIEM integration with O-RAN offers a dynamic and intelligent security architecture that can react quickly to threats that are always changing in both IT and mobile infrastructures. This study shows how O-RAN and SIEM technology can be combined to protect next-generation mobile networks against advanced cyberattacks.

Urban Air Mobility (UAM) represents an innovative mobility solution designed to alleviate the traffic congestion associated with conventional two-dimensional transportation, which has already saturated both urban and underground spaces. The development of unmanned aerial vehicles (UAVs) and electric vertical takeoff and landing (eVTOL) aircraft is a key enabler for this new, sustainable mode of transporting goods and passengers. To ensure safe operation—especially as these vehicles advance toward autonomous capabilities—and to maintain connectivity for passengers, UAM requires high data-rate, reliable, and low-latency communications. Within the 6G framework, several architectural solutions have been proposed, incorporating components such as terrestrial stations, satellites, and UAVs. In this paper, we propose a two-tier architecture: an aerial tier that leverages multi-hop THz communications among UAM vehicles as backhaul links, and a terrestrial tier comprising conventional cellular base stations (BSs) along with a number of enhanced base stations (eBSs) equipped with high-elevation antenna systems to provide aerial coverage for UAM vehicles (UVs). We will show that our approach ensures reliable UAM connectivity even in high-density urban airspaces, striking an optimal balance between the required number of eBSs and the airborne THz backhauling.

Ensuring seamless connectivity for first responders represents a critical issue in emergency scenarios, where disrupted infrastructures and network congestion can significantly hinder the communication. Open Radio Access Network (O-RAN) promises a level of openness in Radio Access Networks (RANs) that could be advantageous under such critical conditions, allowing for enhanced integration across different operators’ networks to mitigate resource scarcity. This paper introduces an architectural solution based on O-RAN that leverages neutral hosting to provide a unified gateway to RAN infrastructures. Our approach facilitates seamless access for emergency responders and optimizes the allocation of resources at both the RAN and Core levels within a specified area by exploiting the deployment of near-real-time Applications (xApps) and non-real-time Applications (rApps) managed by the neutral host. The results demonstrate the effectiveness of O-RAN neutral hosting in enhancing first responders’ connectivity, showcasing significant improvements in throughput and latency, especially under disaster scenarios. • O-RAN Neutral Hosting is a feasible solution for emergency operator connectivity. • First responders seamlessly communicate through efficient radio resource allocation. • O-RAN Neutral Hosting outperforms traditional RAN before and after the disaster. • Slice isolation strategy further amplifies the advantages of O-RAN Neutral Hosting.

The rapidly evolving landscape of cyber threats poses significant challenges to network security, particularly in decentralized environments, such as edge computing. This article proposes an enhanced intrusion detection system (IDS) architecture that integrates transfer learning (TL) to create a unified supermodel, enabling adaptability and scalability for detecting emerging threats across diverse datasets (DS). The key contributions of this study include: 1) leveraging bidirectional encoder representations from transformer (BERT)-based feature extraction to enhance the semantic understanding of intrusion patterns; 2) employing an multilayer perceptron (MLP) classifier refined through TL to improve classification performance; 3) addressing class imbalance using synthetic minority over-sampling technique (SMOTE); and 4) optimizing model deployment by distributing the heaviest computational tasks for the creation of the unified supermodel across the edge nodes with the available capacity, thereby reducing latency and enabling accurate real-time threat detection by resource constrained IoT devices. The proposed TL-enabled supermodel is periodically updated and shared with the IoT devices, ensuring robust and adaptive security mechanisms without the need for extensive local training. Our experimental evaluation on CIC-IDS 2017 and NSL-KDD 2009 DS demonstrates the effectiveness of the approach, achieving 99% accuracy, precision, recall, and F1-score. Our results highlight the scalability, efficiency, and real-world applicability of our IDS framework, reinforcing its role in fortifying network security within highly dynamic cyber threat landscapes.

The networking industry is offering new services leveraging recent technological advances in connectivity, storage, and computing such as mobile communications and edge computing. In this regard, extended reality, a term encompassing virtual reality, augmented reality, and mixed reality, can provide unprecedented user experience and pioneering service opportunities such as: live concerts, sports, and other events; interactive gaming and entertainment; immersive education, training, and demos. These services require high-bandwidth, low-latency, and reliable connections, and are supported by next-generation ultra-reliable and low-latency communications in the vision of 6G mobile communication systems. In this work, we devise a novel scheme, called backup from different data centers with multicast and adaptive bandwidth provisioning, to admit reliable, low-latency, and high-bandwidth extended reality live streams in next-generation networks. We consider network services where contents are non-cacheable and investigate how backup services can be offered by different data centers with multicast and adaptive bandwidth provisioning. Our proposed service-provisioning scheme provides protection not only against link failures in the physical network but also against computing and storage failures in data centers. We develop scalable algorithms for the service-provisioning scheme and evaluate their performance on various complex network instances in a dynamic environment. Numerical results show that, compared to conventional service-provisioning schemes such as those seeking backup services from the same data center, our proposed service-provisioning scheme efficiently utilizes network resources, ensures higher reliability, and guarantees low latency; hence, it is highly suitable for extended reality live streams.

Service providers face significant challenges from Distributed Denial of Service (DDoS) attacks since existing mitigation techniques, including various Machine Learning and flow-based approaches, often lack efficiency due to high latency and inadequate filtering. This paper proposes a novel DDoS mitigation strategy using programmable Data Processing Units (DPUs) to offload detection and mitigation processes, utilizing hardware acceleration. Our approach leverages DPUs to execute flow-based filtering, focusing on mitigating TCP SYN flood attacks. We demonstrate that our DPU-based hardware-accelerated framework successfully eliminates, after a fast learning phase, all the malicious traffic while maintaining high data throughput up to 100 Gbps.

The adoption of Time Division Duplexing (TDD) in 5G not only facilitates efficient spectrum utilization but also paves the way for the implementation of innovative schemes that leverage the unique characteristics of the TDD patterns. For example, Cooperative Dynamic Bandwidth Allocation (CO DBA) exploits information about the TDD pattern to reduce latency in Time Division Multiplexing Passive Optical Networks (TDM-PON)-based fronthaul. In this paper, we harness the information about 5G TDD patterns alongside the programmability offered by Open Radio Access Network (O-RAN) and Software Defined Optical Access Networks (SDOANs) for a different purpose: enhancing x-haul energy efficiency. The scheme we propose seeks to minimize the energy consumption of PON-based x-haul networks by dynamically adjusting the operational states of selected subsystems within the Optical Network Terminal (ONT) in coordination with the TDD patterns. Preliminary simulation results show that up to 80% energy savings can be achieved with the proposed cooperative technique.

The rapid evolution of next-generation mobile networks introduces challenges and opportunities in achieving various use cases with extremely low latency, high data rates, and dense user connectivity. However, these objectives can lead to the higher energy consumption of mobile networks, particularly within the Radio Access Network (RAN), which generally consumes 75% of the mobile networks total energy consumption. The current studies available focus mainly on the energy consumption of the next-generation Core Network (5GC), while this demonstration provides a better understanding of the energy consumption of the RAN components. The demonstration provides energy observability leveraging various open-source software tools to measure and monitor RAN energy consumption trends deployed on the Kubernetes platform — a widely adopted container orchestration platform. Moreover, this demonstration shows energy consumption on different RAN architectures — Monolithic, Disaggregated, and Control Plane and User Plane Separation (CUPS) — utilizing tools such as Kepler and Scaphandre for comprehensive energy measurement.

This demonstration shows the effectiveness of a hierarchical Software-Defined approach in coordinating Radio Access Network (RAN) and Passive Optical Network (PON)-based RAN transport to proactively scale virtual Distributed Units (vDUs) / Radio Units (RUs) and to jointly reconfigure the mid-haul transport.

5G networks are designed to support both high data rate eMBB services, and low-latency high-reliability URLLC services. Multiplexing these two services on the same wireless frequency band leads to a challenging radio resource allocation problem due to their heterogeneous requirements. Usually, multiplexing is based on the puncturing mechanism by URLLC traffic at the expense of the eMBB throughput’s reduction. In this paper, we investigate the potential of cell-free massive MIMO to multiplex URLLC and eMBB traffic by exploiting the sole spatial diversity through network slicing. We define a theoretical framework to evaluate the system’s performance, based on closed-form expressions for the spectral efficiency and the throughput of eMBB and URLLC services in this new asset. We show that by design the cell-free massive MIMO architecture can support such heterogeneous services without relying on sophisticated interference management techniques or pilot assignment schemes, while guaranteeing their specific QoS requirements.

The OPTIMIST project is positioned into one of the most challenging and societal impactful innovation paths, by contributing to the design and implementation of a Multi-access Edge Computing-empowered service platform enabling the optimized delivery of the most popular type of traffic in the forthcoming 5G networks: mobile video. Advanced prediction tools (e.g. data analytics), MEC-enabled service provisioning, 5G networking, QoE-driven and model-based service optimization using stochastic modeling and algorithmic optimization, are major components toward a holistic system that combines analytical tools and close-to-market technologies. In addition, despite the considerable amount of valuable academic research conducted in the area of MEC- enabled service provisioning, the vast majority of obtained results remains hardly compatible (in terms of system models and implementation platforms) and are often limited to the scope of academic research with no envisaged market transfer plans. OPTIMIST fosters fruitful collaboration of academia and industry targeting at developing an innovative platform for fully personalized delivery of 5G mobile video content.

Network and computing infrastructures are nowadays challenged to meet the increasingly stringent requirements of novel applications. One of the most critical aspect is optimizing the latency perceived by the end-user accessing the services. New network architectures offer a natural framework for the efficient orchestration of microservices. However, how to incorporate accurate latency metrics into orchestration decisions still represents an open challenge.In this work we propose a novel architectural approach to perform scheduling operations in Kubernetes environment. Existing approaches proposed the collection of network metrics, e.g. latency between nodes in the cluster, via purposely-built external measurement services deployed in the cluster. Compared to other approaches the proposed one: (i) collects performance metrics at the application layer instead of network layer; (ii) relies on latency measurements performed inside the service of interest instead of utilizing external measurement services; (iii) takes scheduling decisions based on effective end-user perceived latency instead of considering the latency between cluster nodes.We show the effectiveness of our approach by adopting an iterative discovery strategy able to dynamically determine which node operates with the lowest latency for the Kubernetes pod placement.

With the adoption of edge computing, several data centers are available within the footprint of an optical metro network, and contents are replicated in multiple locations. Such a wide content replication offers a unique opportunity to provide better services to users, especially for content-based services, e.g., video delivery. Thus, a service-provisioning scheme can embrace this opportunity to optimize network resource utilization, improve reliability, and achieve lower latency. In this study, we propose a reliable service-provisioning scheme that selects the optimal subset of data centers hosting the desired content and inversely multiplexes a content request over multiple link-disjoint paths. We formulate an integer linear program and develop heuristics for the problem, and use them to solve various complex and realistic network instances. Numerical data show that, compared to conventional service-provisioning schemes such as multipath routing from a single data center or dedicated-path protection, our proposed scheme efficiently utilizes network resources, improves reliability, and reduces latency; hence, it is suitable for the above-mentioned services.

This paper discusses the use of data-driven methodologies to improve multiple-input multiple-output (MIMO) Digital Signal Processing (DSP) performance for crosstalk cancelation in optical systems supporting multiple spatial modes. A crucial problem in time-domain equalization is the large quantity of data needed to compensate the effect of the crosstalk when the signals cover a large number of kilometers. In this respect, the study of techniques that are able to reduce the amount of information needed to perform equalization is addressed, and a reduction algorithm based on the Principal Components Analysis (PCA) and cross-correlation analysis to improve traditional equalizers’ performance is proposed. Experimental validation is performed over the world-first space-division multiplexed (SDM) field trial.

Softwarization of mobile and optical networks facilitates the inter-working between control planes of the two domains, allowing a more efficient management of available resources. Radio resource utilization benefits from the centralization of mobile network functionalities with the application of high-order functional split options by fronthauling. However, higher-order options require larger bandwidth and lower latency in the fronthaul. Advanced mechanisms for the joint control of the access network represent the sole solution to support such fronthaul requirements. This paper proposes a new cooperation scheme to manage the adaptive flexible functional split in 5G networks conditioned to the resource availability in the optical access network. Results show that the proposed converged approach guarantees the optimal allocation of optical resources through a software defined wavelength and bandwidth allocation. The proposed scheme adapts to current traffic demand and simultaneously allows the mobile network to take advantage of the highest possible centralization of mobile network functions by leveraging flexible functional split adaptively compliant to the current optical traffic demand.