Pablo Salva-Garcia

The rapid evolution of Beyond 5G (B5G) and 6G networks demands advanced research frameworks that enable the emulation and digital twinning of complex network topologies, the automation of experiments, and the investigation of cybersecurity challenges. This paper introduces CORE eXecutor (COREX), a novel automation framework designed to orchestrate the setup of emulated 5G/6G network topologies, execute predefined use cases and cyber ranges, and facilitate cybersecurity experiments. COREX interacts with key autonomous network domains —Access, Edge, Transport, and Core—while supporting multi-tenancy, user mobility, and end-to-end resource management. The experimental evaluation demonstrates the framework efficiency and scalability. Results show that execution time increases with the number of User Equipment (UE), ranging from 699 to 1191 seconds, due to the setup stage, where the orchestrator provides the network topology across multiple physical machines. Bandwidth tests indicate that the framework maintains expected performance at lower loads (128 Mbps) with up to 99.9% bandwidth efficiency at the Edge segment. The framework’s ability to automate experiment execution has been validated through a self-protection loop cybersecurity use case, demonstrating its capability to detect, plan, and mitigate cybersecurity threats in 5G / 6G networks. COREX presents a significant advancement in network emulation, providing researchers with a powerful tool to explore 5G and 6G cybersecurity, optimise network performance, and refine autonomous network principles.

Fifth-generation (5G) mobile networks are widely positioned as key enablers of industrial digital transformation. However, despite extensive coverage expansion, the deployment landscape remains dominated by Non-Standalone (NSA) architectures integrated with legacy 4G cores, limiting the practical availability of advanced capabilities such as Ultra-Reliable Low-Latency Communication (URLLC), Massive Machine-Type Communication (mMTC), and network slicing. This has contributed to a disparity between projected 5G functionality and realised industrial utility. This paper investigates the economic and structural factors constraining advanced 5G adoption and examines their implications for emerging sixth-generation (6G) frameworks. We conceptualise the current stagnation as arising from concurrent supply-side and demand-side constraints: elevated Radio Access Network (RAN) capital expenditure relative to previous generations, and limited demonstrable return on investment (ROI) for advanced service capabilities. To evaluate these dynamics empirically, a regional stakeholder study was conducted across industrial and public sector organisations in Ayrshire, Scotland. Data were collected through structured surveys and workshop-based questionnaires involving 34 participants, with proportional sectoral analysis performed to assess representativeness. The results indicate that high initial deployment costs and ROI uncertainty are the primary adoption barriers, with 45.83% of respondents reporting no immediate operational requirement for advanced 5G features. The findings identify an implementation gap in which economic viability, rather than technical feasibility, limits progression beyond basic 5G deployment. The paper argues that unless cost-efficiency and sector-specific value articulation are addressed, similar adoption constraints may extend into 6G development. These results provide empirically grounded insights to inform more economically aligned next-generation network planning.

The UK telecommunications sector’s 5G rollout is projected to consume 2.1% of national electricity by 2030, raising urgent sustainability concerns. This study empirically investigates, under controlled laboratory conditions, the energy performance and cost characteristics of two private 5G architectures—Vodafone’s Mobile Private Network (MPN) and an Open Radio Access Network (O-RAN) via BubbleRAN—and contextualises them against public network references and the United Nations Sustainable Development Goals (SDGs). Two complementary dimensions of energy performance are assessed: absolute power consumption (Watts), reflecting total system draw regardless of throughput; and throughput efficiency (Mbps/W), capturing useful data delivered per unit of energy. In terms of absolute power, O-RAN consumes less (460 W active, 378 W idle) than MPN (645 W active, 620 W idle). In terms of throughput efficiency, MPN delivers 1.45 Mbps/W versus O-RAN’s 0.44 Mbps/W under these specific controlled, single-cell conditions, a difference that reflects the tested hardware configurations (n77 vs. n78 band; 936 Mbps vs. 202 Mbps throughput; 2 × 2 vs. 4 × 4 MIMO) as much as any intrinsic architectural distinction. Both architectures offer substantially lower annual energy costs (£1060–£1486) compared to public micro-cells (£1991–£2666), representing 44–60% savings. Session continuity was 100% across all controlled trials; this reflects short-term laboratory conditions and should not be extrapolated to a long-term network availability guarantee without extended field validation. These results are configuration-specific preliminary indicators; the relative efficiency advantage of each architecture is expected to vary with load, band, and deployment scale. By 2030, UK 5G network operations are projected to generate 795,347–1,260,532 tonnes of CO₂ annually across low-to-high demand scenarios; private deployment, by reducing site proliferation 15–33%, could displace a meaningful share of this footprint. These findings support SDGs 4, 8, 9, 12, and 13. Hybrid O-RAN–MPN pilots are recommended to maximise sustainability gains while advancing social equity and net-zero targets.

This paper presents the design and implementation of an autonomous UAV-based search and rescue system developed within the Horizon Europe project P2CODE. The proposed system leverages a modular and scalable architecture integrating edge-based real-time video processing, AI-based human detection, asynchronous message communication, and persistent state logging, all orchestrated through a web-based operator interface. Central to the system is a swarm intelligence algorithm that partitions the search area among multiple UAVs, taking into account factors such as battery levels and initial positions to generate balanced and coherent flight paths. By combining a Divide Areas based on Robots' initial Positions (DARP) method with a Spanning Tree Coverage (STC) algorithm, the system ensures efficient and complete coverage of large outdoor regions. The operational workflow supports both fully autonomous exploration and reactive human-in-the-loop intervention in response to real-time detections. This work contributes a practical blueprint for large-scale, multi-agent coordination in dynamic and unstructured environments, advancing the state of the art in autonomous search and rescue missions.

The heterogeneous requirements imposed by different vertical businesses have motivated a networking paradigm shift in the next generation of mobile networks (beyond 5G and 6G), leading to critical operation competitiveness of improved productivity, performance and efficiency. Furthermore, with the global digital revolution, such as Industry 4.0, and a connected world, network virtualisation together with high reliability and high performance communications have become crucial elements for mobile network operators. To minimise the negative effects that could affect critical services, network slicing is widely recognised as a key technology with the objective of meeting the Service-Level Agreements (SLAs) and Key Performance Indicators (KPIs) in future 6G networks. In this context, it is essential to introduce a programmable data plane able to enforce flexible Quality of Service (QoS) commitments, while providing high-performance packet processing and real-time monitoring capabilities. To this end, this paper is focused on designing, prototyping and evaluating a novel framework that leverages a set of hardware-based technologies including eXpress Data Path (XDP), extended Barkeley Packet Filter (eBPF) and Smart Network Interface Cards (SmartNICs) to offload network functionality with the objective of providing high-performance pre-6G front-, mid-and back-haul network communications and thus, decreasing the overhead incurs by the Linux Kernel. The proposed solution is implemented based on bypassing the Linux Kernel and accelerating the communication, while providing network slice control and real-time monitoring capabilities. The main aim of this framework is to ensure network communications in forthcoming 6G infrastructures by guaranteeing 6G KPIs and avoiding system overload. The empirical validation of this solution for Industry 4.0 services as an example use case demonstrates key performance improvements in terms of packet processing as high as about 25Gbps, 20M packet per second, 0% packet loss, 0.1ms of latency and less than 10% load on the CPUs.

Next-generation networks are expected to combine advanced physical and digital technologies in super-high-speed connected system infrastructures, gaining critical operation competitiveness of improved efficiency, productivity and quality of services. Towards a fully digital and connected world, these platforms will enable infrastructure virtualization and support of edge processing, making emerging sectors, such as Industry 4.0, ready to exploit its full potentials. Nevertheless, the fast growth of data-centric and automated systems may exceed the capabilities of the overall infrastructure beyond the radio access networks, becoming unable to fulfil the demands of vertical sectors and representing a bottleneck. To minimize the negative effects that could affect critical services in a heavily loaded network, it is essential for network providers to deploy highly scalable and prioritisable in-network optimisation schemes to meet industry expectations in next-generation networks. To this end, this work presents a novel framework that leverages extended Berkeley Packet Filter (eBPF) and eXpress Data Path (XDP) to offload network functions to reduce unnecessary overhead in the backbone infrastructure. The proposed solution is envisioned to be implemented as a Network Application (NetApp) service, which will greatly benefit the compatibility with next-generation networking ecosystem empowered by Artificial Intelligence (AI), advanced automation, multi-domain network slicing, and other related technologies. The achieved results demonstrate key performance improvements in terms of packet processing capacity as high as about 18 million packets per second (Mpps), system throughput up to 6.1 Mpps with 0% of packet loss, and illustrate the flexibility of the framework to adapt to multiple network policy rules dynamically on demand.

The Fifth Generation (5G) mobile networking coupled with Internet of Things (IoT) can provide innovative solutions for a wide range of uses cases. The flexibility of virtualized, softwarized and multi-tenant infrastructures and the high performance promised by 5G technology are key to cope with the deployment of the IoT use cases demanded by various vertical businesses. Such 5G IoT use cases incur challenging Quality of Service (QoS) requirements especially connectivity for millions of IoT devices to achieve massive Machine-Type Communication (mMTC). In addition, network slicing is a key enabling technology in 5G multi-tenant networks to create logical virtualized networks for delivering customised solutions to meet diverse QoS requirements. This work presents a 5G IoT framework with network slicing capabilities able to manage a vast number of heterogeneous IoT network slices dynamically on demand. The proposed solution has been empirically tested and validated in five realistic vertical-oriented IoT use cases. The achieved results demonstrate a excellent stability, isolation and scalability while being able to meet extreme QoS requirements even in the most congested and stressful scenarios.

The increasing popularity of video applications and ever-growing high-quality video transmissions (e.g. 4K resolutions), has encouraged other sectors to explore the growth of opportunities. In the case of health sector, mobile Health services are becoming increasingly relevant in real-time emergency video communication scenarios where a remote medical experts’ support is paramount to a successful and early disease diagnosis. To minimize the negative effects that could affect critical services in a heavily loaded network, it is essential for 5G video providers to deploy highly scalable and priorizable in-network video optimization schemes to meet the expectations of a large quantity of video treatments. This paper presents a novel 5G Video Optimizer Virtual Network Function () that leverages the latest technologies in 5G and video processing to address this important challenge. Advanced traffic filtering is coupled with Scalable H.265 video coding to enable run-time bandwidth-saving video optimization without compromising Quality of Service (QoS); kernel-space video processing is introduced to achieve further performance gains; and the use of a Virtual Network Function (VNF) facilitates dynamic deployment of virtualized video optimizers to achieve scalability and flexibility in this service. The proposed approach is implemented in a realistic 5G testbed and empirical results demonstrate the superior scalability and performance achieved.