Thursday, 5 June 2025, 12:30-13:00, level 1
Session Chair: Pawel Kryszkiewicz (Poznan Univ. of Technology, PL)
P-2.1 Integration of QKD Optical Network into the 5G Standalone Network
Guntis Ancāns (Electronic Communications Office of Latvia); Māris Čamans, Reinis Grūnvalds, Uldis Akmens, Jurijs Tutovs and Māris Aleksandrovs (Electronic Communications Office of Latvia, Latvia)
Within the project “Development of experimental quantum communication infrastructure in Latvia” (LATQN) project partner’s Quantum Key Distribution (QKD) optical networks were integrated into the 5G experimental network created by the Electronic Communications Office of Latvia (ECO). ECO carried out the implementation of 5G testing scenarios using the project partner QKD nodes. The 5G network was implemented using 5G Standalone (SA) technology, ensuring autonomous operation of the 5G network in a closed and controlled environment. For comparing the test results, the 5G network was implemented with and without the QKD optical networks, performing data transmission simulations and appropriate measurements, including under the influence of external signals. As part of the tests, a software developed by ECO was also tested, as well as tests of 5G / Wi-Fi wireless routers with a built-in Quantum Random Number Generation (QRNG) chip in the established 5G experimental network were carried out.
P-2.2 Empirical Evaluation of Deep Reinforcement Learning for Task Offloading in 5G Environments
Gorka Nieto and Idoia de la Iglesia (Ikerlan, Spain); Unai Lopez-Novoa (University of the Basque Country, Spain); Cristina Perfecto (University of the Basque Country UPV/EHU, Spain); Ali Balador (Ericsson Research, Sweden); Mohammad Ashjaei (Malardalen University, Sweden)
In recent years, the exponential adoption of the Internet-of-Things (IoT) along with modern communication networks like 5G have enabled the creation of paradigms like Multi-Access Edge Computing (MEC). MEC environments usually offer a number of compute locations, from IoT devices to servers in a Cloud, which enables the offloading of computation loads from less capable devices to more powerful or available servers. Choosing the most adequate location for a given workload can be complex due to the different characteristics of involved elements. Besides, many approaches in the literature make this choice in a centralized manner, but this poses a central point of failure which can be blocking for resource-demanding applications. In this work, we present a Deep Reinforcement Learning (DRL) algorithm aimed at solving the choice of compute locations in a decentralized manner, and its assessment in a real testbed. This testbed is formed of an end-user device running the main algorithm, which connects to a MEC server via 5G, and a Cloud server. We compare the algorithm against other 4 alternatives (1 of those based on DRL too) and analyze their performance and energy consumption in the end-user device, which runs tasks generated synthetically. Results show that DRL algorithms provide the best tradeoff between performance an energy consumption in changing conditions.
P-2.3 SLAQ: an SLA-Driven 6G O-RAN QoS Framework Using Deep Reinforcement Learning
Noe Yungaicela-Naula, Vishal Sharma and Sandra Scott-Hayward (Queen’s University Belfast, United Kingdom (Great Britain))
As 6G scenarios grow in complexity, network operators need an automated and optimized approach to managing Quality-of-Service (QoS) in the radio access network (RAN). Transitioning from resource-focused management to one that considers evolving operator intents is essential. This can be achieved by translating service-level agreement (SLA) intents into operational rules to ensure compliance and save costs. O-RAN introduced the RAN intelligent controller (RIC) and intelligent applications (xApps) to enhance the RAN operation. Furthermore, RAN slicing has been shown as the most promising method to provide O-RAN-based QoS in 6G. However, while existing methods optimize resources allocated per slice, they often overlook SLAs. This leads to suboptimal resource usage and outages when the system fails to meet target key performance indicators (KPIs). This work introduces SLAQ, an xApp designed to translate service operator requirements to optimize spectrum resource sharing. SLAQ monitors key performance metrics (KPMs) and employs deep reinforcement learning (DRL) to make decisions within the RAN. SLA policies are modeled and integrated into the xApp to optimize resource distribution while preventing SLA conflicts and outages. Our highly-detailed system-level simulations show that SLAQ effectively learns optimal actions, achieving high communication reliability, i.e., close to 99% for ultra-reliable low-latency communications.
P-2.4 Securing 6G Networks: the HORSE Approach Using LLM-Driven Mitigation Actions
Michail Danousis (University of the Aegean, Greece & EIGHT BELLS LTD GREEK BRANCH, Greece); Alice Piemonti (Martel Innovate, Switzerland); Fabrizio Granelli (University of Trento, Italy); Xavi Masip-Bruin (Universitat Politècnica de Catalunya (UPC) & Advanced Network Architectures Lab (CRAAX), Spain); Eva Rodriguez (Universitat Politècnica de Catalunya, Spain); Alessandro Carrega (UNIGE, Italy & CNIT, Italy); Emmanouil Kafetzakis (Eight Bells Ltd., Cyprus); Ioannis Giannoulakis (Eight Bells Ltd, Cyprus)
The HORSE framework leverages artificial intelligence (AI) to secure next-generation 6G networks by addressing their complexity and evolving vulnerabilities. This abstract presents an LLM-agent-based subsystem within HORSE that automates cybersecurity mitigation actions. Utilizing a Knowledge Base enriched with threat-mitigation pairs (e.g. from MITRE ATT&CK), Large Language Models (LLMs) translate high-level mitigation strategies into executable Ansible commands. A dual-agent workflow-one LLM generating commands (“translator”) and another executing and refining them (“executor”)-ensures reliability through feedback loops. Experiments with open-source LLMs (Llama 2, Falcon) achieved up to 73% execution success and high intention accuracy. This approach enhances automation and adaptability for 6G security. We outline the HORSE architecture, LLM-driven workflow, experimental results, and future directions, supported by illustrative figures from the full study.
P-2.5 Feature Selection for Data-Driven Optimization: a Case Study on Adaptive Video Streaming in 5G-Enabled Factories
Abdelbaset Kabou and Mourad Khanfouci (Mitsubishi Electric R&D Centre Europe, France)
Feature selection (FS) is a critical yet often underestimated aspect of building high-performance predictors for the deployment of applications in complex environments such as 5G-enabled factory systems. This paper highlights the importance of FS through a comprehensive evaluation of different modeling techniques, using Dynamic Adaptive Streaming over HTTP (DASH) as a proof-of-concept scenario. For example we will explore Wrapper methods (e.g., RFE, Boruta), Filter methods (e.g., Pearson’s r, Spearman’s r, Kendall, MIFS, FCBF), and Embedded methods (e.g., Lasso, Ridge, ElasticNet, Shapley Value) for predicting the Quality of Experience (QoE) metrics of the DASH video streaming in the factory. A central focus of the paper is the addressing multicollinearity, a common challenge in network data when highly correlated features complicate training and degrade model performance. Key findings highlight top performing approaches, attributing their success to effective handling of the multicollinearity issue. Additionally, we underscore the significant computational efficiency gains achieved through these approaches and discuss the trade-offs involved in balancing the prediction accuracy, computational efficiency, and model interpretability. While DASH serves as a use case, our findings demonstrate how robust feature selection techniques can enhance the performance of data-driven models, paving the way for more efficient and more reliable 5G-enabled industrial systems.
P-2.6 O-RAN Based Mechanism to Feed External AI-Driven Services: a Testbed Deployment
Núria Domènech (Neutroon Technologies, Spain); Seyed Mahdi Darroudi (Neutroon Technologies & IEEE Member, Spain); Nupur Thakker and Cristian Armesto (Neutroon, Spain); Matteo Grandi (Neutroon Technologies, Spain)
Established in February 2018, O-RAN caused a paradigm shift in radio access network (RAN) disaggregation by introducing standard interfaces for RAN functions. The provision of standard interfaces enabled smaller players to participate in RAN technology and enrich the industry. The resource intelligent controller (RIC) enables the O-RAN to host AI-driven services. However, the majority of research are focused to optimize O-RAN functionality. Expanding AI-driven services, this study offers the solution to employ O-RAN metrics for external AI models for monitoring, security, and management purposes. This document introduces commercial grade testbed that provides a parallel metrics extraction method using the O1 interface and either prepares the desired dataset to train the AI model or visualizes the metrics in standard reporting environments.
P-2.7 Confidential Computing and Privacy-Preserving Technologies for 6G
Vera Stavroulaki (WINGS ICT SOLUTIONS, Greece); Drasko Draskovic (Abstract Machines, France); Dimitrios Schinianakis (Nokia Bell Labs, Germany); Markku Kylänpää (VTT, Finland); Amit K. Srivastava and George Saleh (NOKIA, France); David Solans Noguero (Telefónica, Spain); Paula Delgado-Santos (Telefonica, Spain); Nenad Gligoric (Zentrix Lab, Spain)
To enable new digital services to thrive, the 6G infrastructure needs to ensure reliability, trust, and resilience across different types of environments. However, as the 6G network becomes more widespread, concerns about security and privacy are increasing. Integrating AI tools, advanced hardware, computing capabilities, IoT devices, and edge nodes into the 6G network is essential to deliver a more intelligent, efficient, and comprehensive solution. However, existing IT security solutions are not equipped to manage the vast number of connected devices-particularly IoT devices, which often lack robust cryptographic protections and mutual authentication mechanisms. Under this environment, novel security techniques are needed to satisfy the higher security requirements of the future network, and new security approaches are necessary to ensure trustworthiness and privacy. This paper presents an overview of a set of technologies for confidential computing and privacy preservation for 6G including cryptographic quantum-resistant protocols and security proofs tools and mechanisms for confidentiality in 6G.
P-2.8 Beyond 5G Benefits on eHealth and Emergency
Andrea Di Giglio (Telecom Italia, Italy); Pasquale Bufano (National Research Council, Italy); Francesco Sansone (Institute of Clinical Physiology (IFC) National Research Council of Italy (CNR), Italy); Marco Laurino (National Research Council, Italy); Carmela Calabrese and Marco Randazzo (Istituto Italiano di Tecnologia, Italy); Elisa Maiettini and Lorenzo Natale (IIT, Italy); Aruna Prem Bianzino (Universidad Carlos III de Madrid, Spain); Gianna Karanasiou and Vera Stavroulaki (WINGS ICT SOLUTIONS, Greece); Dimitrios Plakas (WINGS ICT Solutions, Greece); Nikos Sintoris (WINGS ICT Solutions, European Union); Paola Iovanna (Ericsson, Italy); Giulio Bottari (Ericsson Telecomunicazioni, Italy); Silvia Villafranca (Scuola Sant’Anna, Italy); Franco Tecchia (Scuola Superiore Sant’Anna, Italy); Nicolò Boccardo (IIT, Italy); Dario Di Domenico and Giulia Caserta (Istituto Italiano di Tecnologia, Italy)
This paper delves into the application of 5G and Beyond 5G (B5G) networks in addressing critical challenges within the eHealth and emergency domains through four distinct use cases: Mass Casualty Incident (MCI) and Emergency Rescue in Populated Areas, Remote Proctoring, Smart Ambulance, and Adaptive Control of the Hannes Prosthetic Device. By leveraging key technological pillars such as the Internet of Things (IoT), cloud/edge continuum architecture, and advanced radio performance, this study demonstrates the transformative potential of these technologies in enhancing healthcare delivery and emergency response. The integration of these advanced networks facilitates real-time data transmission, improved communication, and efficient resource management, ultimately contributing to more effective and timely interventions in critical situations. Through detailed analysis and evaluation, this paper highlights the significant benefits and practical applications of 5G/B5G networks in revolutionizing the eHealth and emergency response landscape.
P-2.9 Enhancing Critical Infrastructure Resilience: Smart Gas Metering Through LTE/5G Communications
Uzay Bengi and Semiha Koşu (Turkcell Technology, Turkey)
The integration of Internet of Things (IoT) and cellular network technologies is pivotal for enhancing energy management systems, improving communication reliability, and operational efficiency in managing critical infrastructures. This paper evaluates smart metering systems that utilize IoT and cellular networks, particularly in disaster scenarios, to provide sustainable and resilient urban infrastructure. We focus on the application of network slicing in 5G technology to improve critical IoT communications for emergency management, such as during gas leakages. This approach enables prioritized and simultaneous communications, facilitating rapid response and effective deployment of prevention measures. Thus, we propose a green, robust, and efficient network architecture that merges various IoT and cellular technologies to maintain connectivity across diverse communication standards, supporting uninterrupted service and adaptive response capabilities.
P-2.10 Interconnecting the European Health Data Space with Quantum-Robust Cellular Architectures
Callum Turino (Edinburgh Napier University, United Kingdom (Great Britain) & 6G Health Institute GmbH, Germany); Ashish Sundar, Unal Sakar and Pinar Eren (6G Health Institute GmbH, Germany); Vladislav Chesnokov (6G Health Institute & No, Germany); Angelica Avila Castillo and Sara El Gaily (6G Health Institute GmbH, Germany); Bill Buchanan (Edinburgh Napier University, United Kingdom (Great Britain)); Christoph Thümmler (6G Health Institute GmbH, Germany); Owen Lo (Edinburgh Napier University, United Kingdom (Great Britain))
The European Health Data Space (EHDS) aims to establish secure and interoperable data-sharing mechanisms across European healthcare networks. However, its full-scale adoption is hindered by technical complexities and interoperability challenges, primarily due to a lack of a universal digital communication infrastructure in the EHDS specification. Addressing these challenges requires the development of a unified communication framework to interconnect the various components of the EHDS. This study introduces the European Health Data Slice (EHDSL), a novel approach for connecting Health Data Spaces (HDSs) through a universal digital communication infrastructure. The EHDSL utilises private and public 5G network slicing alongside national and international roaming to enable real-time, secure, end-to-end data aggregation in a patient-centric healthcare model. Additionally, it presents strategies for securing the EHDSL through quantum-robust techniques, leveraging the latest standardised post-quantum cryptography (PQC) algorithms.
P-2.11 Poster: 6G-PATH Project Connected and Sensing City 6G Testbed
João Gameiro (University of Aveiro, Portugal); Pedro Valente (Instituto de Telecomunicações, Portugal); Francisco C Ribeiro (University of Aveiro, Portugal); José Vicente de Oliveira (Instituto de Telecomunicações, Portugal & Universidade de Aveiro, Portugal); Pedro Rito (University of Aveiro, Portugal & Instituto de Telecomunicações, Portugal); Duarte Raposo (Instituto de Telecomunicações, Portugal); Susana Sargento (Universidade de Aveiro, Portugal); Carlos Marques and Miguel Mesquita (Altice Labs, Portugal); Filipe Pinto (Alticelabs, Portugal)
This paper presents a Beyond 5G testbed developed within the 6G-PATH project, a collaboration between Instituto de Telecomunicações (IT) and Altice Labs (ALB). The testbed serves as a foundation for conducting experiments, running pilots, and enabling interoperability between multiple partners, both within and beyond 6G-PATH through open calls. A key aspect of this initiative is its integration with the 6G-PATH portal, facilitating seamless interaction with other testbeds. Designed to support future 6G networks, the testbed provides an open, flexible, and scalable environment for testing mobility, network automation, extensive monitoring, and edge computing in a smart city setting. Located in Aveiro, Portugal, the infrastructure features an open-source 5G core and a combination of open-source and proprietary Radio Access Technologies (RAT), ensuring multi-vendor interoperability. The city’s connected ecosystem includes autonomous vehicles, distributed edge computing, and real-time data processing, enabling innovative use cases for connected and sensing cities.
P-2.12 IoT Environmental Sensing for Crop Yield Modelling and Optimisation: 6G Use Case
Temitope Odedeyi (University College London, United Kingdom (Great Britain)); Olalekan R Kolawole, Chike Ugoji, Toye Ayankanmi, Oziegbe Okhuoya and Ismail Rabbi (International Institute of Tropical Agriculture, Nigeria); Kwaku Onwona-Hwesofour Asante (Council for Scientific and Industrial Research – Crops Research Institute, Ghana); Izzat Darwazeh (University College London, United Kingdom (Great Britain))
This paper presents the design and implementation of an IoT-based environmental monitoring system to enable data-driven crop yield modelling and optimisation, leveraging multi-source data acquisition and scalable communication technologies. The system integrates in-situ sensors, drone imagery, remote sensing data and crop repository datasets to capture critical agronomic factors across multiple geo-ecological zones in West Africa. A key bottleneck in large-scale agricultural sensing is establishing reliable and adaptive communication networks, particularly across remote farming locations. This study presents the integration of mutiple communication technologies. including LoRaWAN, cellular (3G/4G) and software-defined radio (SDR)-enabled UAVs, ensuring seamless data transmission. While the system effectively utilises current communication infrastructure, the research highlights the potential of 6G networks to revolutionise agricultural sensing applications, such as crop yield modelling and optimisation through extended coverage, and joint communication-sensing capabilities.
P-2.13 Design and Experimental Demonstration of Non-Orthogonal Signal Transmission at 153 GHz
Yalin Zhou and Izzat Darwazeh (University College London, United Kingdom (Great Britain))
The bandwidth-compressed spectrally efficient FDM (SEFDM) multicarrier waveform, has been experimentally demonstrated, for the first time, in the D-band (110-170 GHz) region. SEFDM is a promising candidate for modern wireless communication systems, which enhances spectral efficiency and conserve spectral resource. This demonstrated system employs a low-complexity transceiver design, comparable to those used in conventional OFDM systems and utilizes LDPC coding with QPSK and 8PSK constellations to achieve error-free data tranmission at 10 Gbps and 15 Gbps, respectively when tested with data of length of 2^18 − 1 bits. Compared to OFDM, SEFDM demonstrates a spectral efficiency improvement of 50% with QPSK mapped OFDM and 11.3% with 8PSK mapped OFDM, and achieves maximum spectral efficiency of 1.67 bits/s/Hz. The paper details the system and experimental design and illustrates the potential for energy-efficient millimeter-wave (mmWave) communications. close to the power region of receiver sensitivity, for the same received power SEFDM is shown to achieve higher spectral efficiency for no-error transmission with LDPC code under current standard.
P-2.14 DIGIPHY: XR Communication and Interaction Within a Dynamically Updated Digital Twin of a Smart Space
Lea Skorin-Kapov, Maja Matijasevic, Ivana Podnar Zarko and Mario Kusek (University of Zagreb, Croatia); Darko Huljenić (University of Zagreb Faculty of Electrical Engineering an Computing, Croatia); Vedran Skarica and Darian Skarica (Delta Reality, Croatia); Andrej Grguric (Ericsson Nikola Tesla d. d., Croatia)
The integration of emerging eXtended Reality (XR) technologies, digital twins (DTs), smart spaces, and advanced mobile and wireless networks is set to enable novel forms of immersive interaction and communication. We present aims and research challenges for immersive and intuitive XR communication and interaction within a virtual reality-based representation of a DT. This DT is dynamically updated and synchronized, both spatially and temporally, with a physical smart space that is equipped with sensors and actuators. Two selected use cases illustrate communication and collaboration scenarios employing state-of-the-art wireless networks.
P-2.15 Beyond 5G Network Exposure for the Automotive Sector: the ENVELOPE Approach
Dimitrios Fragkos (Institute of Communication and Computer Systems (ICCS), Greece & University of Peloponnese, Greece); Konstantinos V. Katsaros (Institute of Communication and Computer Systems (ICCS), Greece); Pavlos Basaras and Angelos Amditis (Institute of Communication and Computer Systems, Greece); Vasilis Pitsilis and Harilaos Koumaras (NCSR Demokritos, Greece); Apostolis K. Salkintzis (Lenovo, Greece); Edoardo Bonetto and Daniele Brevi (Fondazione LINKS, Italy); Ramon S. Schwartz (TNO, The Netherlands); Gabriele Scivoletto and Giacomo Bernini (Nextworks, Italy); Foteini Setaki (Hellenic Telecommunications Organization, Greece); Apostolos Siokis (Iquadrat Informatica, Spain)
The rapid evolution of 5G networks is transforming various industries, with the automotive sector emerging as a key beneficiary of advanced connectivity. In this context, this paper introduces the ENVELOPE project architecture, which aims to expose 5G network application programming interfaces (APIs) to the automotive sector in a simplified, semi-automatic manner.
P-2.16 Performance Evaluation of QoS-Aware SDN-Based Narrowband Tactical Networks
Mikołaj Wysocki (ITTI, Poland); Henryk Gierszal (Adam Mickiewicz University, Poland); Piotr Tyczka (ITTI, Poland)
This paper takes insight into mechanisms offered by the network controller in a Software Defined Network (SDN) to manage Quality of Services (QoS) in an efficient way. The controller is based on Open Network Operating System (ONOS). A set of QoS-control mechanisms has been implemented and tested to evaluate their influence on network management in challenging conditions resulted from smaller capacity of the link, traffic unpredictability, and network failures. Experiment results highlight the importance of QoS mechanisms in modern SDN deployments to maintain quality, continuity, and performance standards.
P-2.17 FedORA: Resource Allocation for Federated Learning in ORAN Using Radio Intelligent Controllers
Abdelaziz Salama, Mohammed M. H. Qazzaz, Syed Danial Ali Shah, Maryam Hafeez and Syed Ali Raza Zaidi (University of Leeds, United Kingdom (Great Britain))
This paper proposes an integrated approach for optimising Federated Learning (FL) communication in dynamic and heterogeneous network environments. Leveraging the modular flexibility of the Open Radio Access Network (ORAN) architecture and multiple Radio Access Technologies (RATs), we aim to enhance data transmission efficiency and mitigate client-server communication constraints within the FL framework. Our system employs a two-stage optimisation strategy using ORAN’s rApps and xApps. In the first stage, Reinforcement Learning (RL) based rApp is used to dynamically select each user’s optimal Radio Access Technology (RAT), balancing energy efficiency with network performance. In the second stage, a model-based xApp facilitates near-real-time resource allocation optimisation through predefined policies to achieve optimal network performance. The dynamic RAT selection and resource allocation capabilities enabled by ORAN and multi-RAT contribute to robust communication resilience in dynamic network environments. Our approach demonstrates competitive performance with low power consumption compared to other state-of-the-art models, showcasing its potential for real-time applications demanding both accuracy and efficiency. This robust and comprehensive framework, enabling clients to utilise available resources effectively, highlights the potential for scalable, collaborative learning applications prioritising energy efficiency and network performance.
P-2.18 Latency Analysis in Real-Time 3D Volumetric Streaming
Seungwoo Hong (Convergence Networking Research Team/ETRI, Korea (South)); HoSun Yoon (ETRI, Korea (South)); Seong Moon (Principal Researcher, Korea (South)); Inayat Ali (Electronics and Telecommunications Research Institute (ETRI), Korea (South))
Real-time 3D volumetric streaming enables high-fidelity 3D model transmission for applications such as VR, AR, gaming, telepresence, and remote collaboration. However, latency remains a critical barrier, affecting immersion and real-time interaction. This work presents a detailed latency analysis of a real-time volumetric streaming system by decomposing the process into application, transport, and network layers. We identify key bottlenecks, quantify their effects, and propose targeted optimizations to reduce latency. Our findings offer practical guidelines to improve system responsiveness, scalability, and user experience, advancing the development of immersive 3D environments.
P-2.19 A Comparative Analysis of Intrusion Detection Using Different Datasets in 5G Core Networks
Suranga Prasad, Yushan Siriwardhana and Tharaka Mawanane Hewa (University of Oulu, Finland); Kyungmin Park (Electronics and Telecommucations Institute, Korea (South)); Jonghyun Kim (Sejong University, Korea (South)); Mika E Ylianttila (University of Oulu, Finland)
The advancement of 5G networks and their evolution towards 6G has introduced new security challenges, necessitating advanced Intrusion Detection Systems (IDS) to mitigate emerging threats. Machine Learning (ML) based IDS efficiently adapt and respond to this dynamic threat landscape instead of conventional signature-based IDS. Capturing informative and accurate data quickly, feeding the data to the ML-based IDS without delay, and identifying malicious activities instantly are key elements of proper intrusion detection. Datasets play a crucial role in this activity and many datasets have been introduced for developing ML-based IDS. This study evaluates the performance of two different datasets: one derived from network traces and the other from monitoring system logs and metrics. In this work, we develop a testbed to simulate attack scenarios from user devices to the core network and compare the resource utilization, detection time, and accuracy of IDS trained on each dataset. Our comparison results show that, while trace-based data collection provides rich attributes to identify more attack types, it incurs high resource consumption, making it less effective in large-scale deployments and instances needing quick response times. Conversely, log and metric-based data collection offers a resource-efficient alternative with comparable detection accuracy. The findings emphasize the importance of balancing data richness and resource efficiency in 5G security solutions.
P-2.20 RIS-Aided Wireless Communication with Movable Elements: Geometry Impact on Performance
Yan Zhang (Trinity College Dublin, Ireland); Indrakshi Dey (South East Technological University, Waterford, Ireland); Nicola Marchetti (Trinity College Dublin, Ireland)
Reconfigurable Intelligent Surfaces (RIS) are known as a promising technology that can improve the performance of wireless communication networks and have been extensively studied. Movable Antennas (MA) are a novel technology that fully exploits the antenna placement to enhance the system performance. This article aims to evaluate the impact of transmit power and number of antenna elements on the outage probability performance of an MA-enabled RIS structure (MA-RIS), compared to existing Fixed-Position Antenna RIS (FPA-RIS). The change in geometry caused by the movement of antennas and its implications for the effective number of illuminated elements, are studied for 1D and 2D array structures. Our numerical results confirm the performance advantage provided by MA-RIS, achieving 24% improvement in outage probability, and 2 dB gain in Signal-to-Noise Ratio (SNR), as compared to FPA-RIS.
P-2.21 Closed-Form BER for Distributed Antenna Systems in Partially Blocked Rayleigh Fading Channels
Thibaut Rolland (Orange, France); Matthieu Crussière (Univ Rennes, INSA Rennes, CNRS, IETR, France); Marie Le Bot (Orange Labs, France)
This paper introduces the theoretical derivations for establishing the bit error rate (BER) expressions of distributed antenna systems (DAS) over partially blocked Rayleigh fading channels. Clustered delay line models from 3GPP are considered with a subset of blocked propagation components. Assuming the receiver cannot update its equalizer during blockage events, a statistical analysis of the erroneously equalized channel gains is performed to provide the probability density functions of the received symbols depending on the blockage. BER derivations are then obtained and validated through simulations. The provided expressions allow to predict the performance of DAS and accurately analyze the impact of various blockage scenarios.
P-2.22 Risk Analysis of Frequency Synchronization in WLAN-Based OFDM Systems Under Narrowband Interference
Brian Leeman and Hans Hallez (KU Leuven, Belgium); Davy Pissoort (KU Leuven Bruges Campus, M-Group, Belgium); Tim Claeys (KU Leuven, Belgium)
Synchronization algorithms in Orthogonal Frequency Division Multiplexing (OFDM) based wireless communication systems are a crucial element in the wireless system. Without them, it would be impossible to communicate efficiently. However, these algorithms are vulnerable to Narrowband Interference (NBI). This is especially worrisome for Ultra-Reliable Low-Latency Communication (URLLC) systems, which target mission and safety-critical applications. This paper investigates the effect of a Continuous Wave (CW) NBI on three Carrier Frequency Offset (CFO) synchronization algorithms used in tandem in a complete WLAN-based OFDM system. This investigation aims for a holistic analysis of this impact and aims to analyze the worst case performance which produces the highest risk for URLLC system reliability. The analysis shows a significantly higher impact on the CFO synchronization estimation performance due to a CW interference than from Additive White Gaussian Noise (AWGN). It also identified a frequency domain CFO estimator to result in the highest frequency synchronization error risk, warranting caution for using such estimators in URLLC systems.
P-2.23 Real-Valued Genetic Algorithm-Based Hybrid Beamforming Optimization in Multi-IRSs-Aided mmWave MIMO-OFDM Systems
Yanan Xin and Qilie Liu (Chongqing University of Posts and Telecommunications, China); John Cosmas (Brunel University, United Kingdom (Great Britain))
Multiple-input multiple-output (MIMO) is well-suited for millimeter (mm)-wave communications, however, beamforming faces challenges because the number of RF chains, constrained by cost, cannot be matched one-to-one with the transmit antennas, while intelligent reflecting surfaces (IRS) enhance performance using low-cost passive elements, transforming mmWave communication with efficiency and cost-effectiveness. This paper studies the spectral efficiency (SE) optimization problem in a multi-IRSs-aided mmWave MIMO-orthogonal frequency-division multiplexing (OFDM) systems employing hybrid beamforming architectures. An innovative optimal scheme based on a real-valued genetic algorithm (RVGA) is proposed to jointly optimize the transmitter hybrid beamforming vector and the IRS phase shift matrix. Simulation results indicate that the proposed scheme with 1/2 IRSs improves SE by 9.09/10.95 bits/s/Hz compared to without IRS, with an additional 1.85 bits/s/Hz improvement for 2 IRSs, and RVGA outperforms baseline algorithms.
P-2.24 Flexible Payload Technologies for Resilient Satellite Networks
Hossein Rouzegar and Victor Montilla Gispert (I2CAT Foundation, Spain); Alessandro Villegas and Joan Adria Ruiz-de-Azua (i2CAT Foundation, Spain)
The high costs associated with equipment, testing, and hardware maintenance pose significant challenges for planning Low Earth Orbit (LEO) satellite missions. In response, the emerging NewSpace industry is adopting a novel business model focused on cost reduction through the use of commercial off-the-shelf (COTS) components rather than space-grade solutions, which typically require extensive testing. However, current CubeSat deployments are constrained by a fixed-service paradigm where upgrades are limited and primarily focused on software debugging. This paper introduces a flexible payload framework designed to overcome these limitations by enabling the deployment of software-controlled payloads. The proposed architecture considers three levels of flexibility including hardware reconfiguration, software virtualization and service orchestration, which create an environment that supports in-flight expansion and updates of satellite functionalities achieving a Network Function Virtualization (NFV) valid architecture. By incorporating these abstraction layers, the framework decouples services from platform resources, facilitating platform-independent application deployment. This approach optimizes resource utilization and service delivery based on factors such as geographic region, energy consumption, user demand, and other mission-specific parameters. The simulation results show that the flexible payload can facilitate multiple services while minimizing power consumption, which is considered as critical attributes of this technology that contributes to the sustainability of satellite networks.
P-2.25 Defending Against Adversarial Attacks in 6G: Practical Mitigation Approach
Sogo Pierre Sanon (German Research Center for Artificial Intelligence, Germany); Akshay Manoj Sant (University of Rochester, USA); Hans Dieter Schotten (Deutsches Forschungszentrum für Künstliche Intelligenz GmbH, Germany)
The integration of Artificial Intelligence (AI) into mobile networks has led to significant improvements in operational efficiency, resource optimization, and security monitoring. However, the increasing reliance on AI has also introduced vulnerabilities to Adversarial Machine Learning (AML) threats, which are expected to become even more critical with the advent of 6G networks. While numerous AML techniques have been identified, not all pose substantial risks to mobile communication systems. Implementing defenses against all possible attacks can be computationally expensive and may degrade system performance. This study critically examines adversarial threats in AI-driven mobile networks, and attacks are categorized based on their feasibility and real-world impact. A risk-based framework is presented to assist in efficiently prioritizing security investments. The most critical AML threats to mobile networks are identified, and targeted mitigation strategies that ensure a balance between security, computational efficiency, and system reliability are provided. The findings of this research serve as a guideline for AI security implementation in future networks, promoting a strategic approach to adversarial defense while maintaining high network performance.
P-2.26 Overview of Recent 6G Standardization Activities and Analysis of SNS Contributions
Carles Antón-Haro (Centre Tecnologic de Telecomunicacions de Catalunya (CTTC), Spain); Claudio De Majo (Trust-IT Services, Italy); Konstantinos Trichias (6G Smart Networks & Services Industry Association (6G SNS IA), Greece & National Technical University of Athens (NTUA), Greece); Jos Beriere (TNO, The Netherlands)
This paper provides a comprehensive update on global standardization activities and the involvement of Smart Networks and Services Joint Undertaking (SNS-JU) projects in shaping the future of 6G technologies. It explores key advancements in standardization efforts across major bodies, including 3GPP, ETSI, and ITU, highlighting their respective contributions to the evolution of 5G Advanced and the foundations of 6G. The report underscores the strategic importance of these collaborations, particularly in enabling interoperability, enhancing Europe’s leadership in the global telecommunications landscape, and fostering innovation in areas like AI, spectrum optimization, and infrastructure. Furthermore, it delves into the role of pre-standardization activities led by the 6G Industry Association (6G-IA), emphasizing their alignment with regulatory and industry priorities to support a unified global 6G standard. Contributions from SNS-JU projects are analyzed, showcasing their impact on standardization processes, open-source solutions, and emerging technologies like Terahertz communication and integrated sensing and communication. Finally, the paper presents tailored recommendations for stakeholders, focusing on strategic collaborations, policy alignment, and the acceleration of contributions to global standardization efforts.
P-2.27 Strategies and Recommendations for Global Consensus and EU R&D Cooperation in 6G
Carles Antón-Haro (Centre Tecnologic de Telecomunicacions de Catalunya (CTTC), Spain); Konstantinos Trichias (6G Smart Networks & Services Industry Association (6G SNS IA), Greece & National Technical University of Athens (NTUA), Greece); Alexandros Kaloxylos (6G Smart Networks and Services Industry Association (6G-IA), Belgium); Bernard Barani and Werner Mohr (6G Industry Association (6G-IA), European Union); Claudio De Majo (Trust-IT Services, Italy)
The Smart Networks and Services Joint Undertaking (SNS-JU) is driving Europe’s 6G research through phased R&I projects from 2023 to 2030, focusing on new network technologies, experimentation facilities, and global collaboration. The SNS-ICE Coordination and Support Action (CSA) project serves as its international ambassador, fostering partnerships, publishing position papers, and aligning European priorities with global 6G developments. This paper outlines the main elements of SNS-ICE’s strategy towards achieving global consensus and stimulating R&D cooperation around 6G, also highlighting synergies with other EU programs such as IPCEI-CIS, DEP, or CEF. The paper concludes with recommendations for policymakers, industry, and research communities, emphasizing investment in open technologies, strengthening European competitiveness, and fostering global cooperation to shape a unified 6G ecosystem.
P-2.28 Human Centric Private & Secure Onboarding for Network Applications in 6G
Diogo E. J. Santos (Instituto de Telecomunicações, Portugal); Catarina Silva (Universidade de Aveiro, Portugal); Vitor A Cunha and João Paulo Barraca (University of Aveiro & Instituto de Telecomunicações, Portugal); Rui L Aguiar (University of Aveiro, Portugal & Instituto de Telecomunicações, Portugal)
In the 5G and emerging 6G networks, Network Applications (NAs) are critical in delivering diverse and innovative services. NAs are groups of Network Services (NSs) that provide functionalities for verticals. They have been pivotal in domains like enhanced Mobile Broadband(+) (eMBB(+)), Ultra Reliable Low Latency Communications ((x)URLLC), and Internet of Everything (IoE), and their importance will only grow as we transition further into 6G. Therefore, understanding and addressing the challenges and opportunities posed by NAs are crucial as we advance towards 6G. This article proposes an approach for designing and onboarding secure and private NAs, ensuring their compliance with policies in closed loops (e.g., Security Orchestration, Automation and Response (SOAR) loops). The introduced architecture leverages OpenSlice and incorporates best practices from DevOps with SecOps and PrivOps (i.e., DevSecOps and DevPrivOps combined). These NAs are designed to be configurable both at the time of ordering and during runtime, with predefined characteristics that can be adjusted, for example, through Moving Target Defense (MTD) mechanisms. Furthermore, the definitions for privacy quantification services and their configuration are introduced to determine the privacy score of NAs and feed the loop continuously. At a high level, security and privacy policies can be manually defined or automatically generated in response to malicious attacks, and they can be dynamically translated and enforced within existing NAs. The early results show that the approach is sound, with promising results from the PoC and custom-built tooling.
P-2.29 Towards Seamless Mobility: Location Management in Satellite-Aerial-Ground Integrated Networks
Shama Noreen (RPTU, Germany); Ainur Daurembekova (University of Kaiserslautern-Landau (RPTU), Germany); Hans D. Schotten (University of Kaiserslautern, Germany)
In the context of sixth-generation (6G) networks, notable features include the substantial increase in connected devices and network densification. Future networks will consist of thousands of integrated terrestrial, aerial, and space networks, which are crucial for delivering ubiquitous communication and internet services in all scenarios and at any time. Effective location management is critical for these networks to accurately track each user within their expansive coverage areas, especially within Space-Air-Ground Integrated Networks (SAGIN). This article provides a concise overview of the current status and unique requirements for location management in SAGIN, identifying existing gaps. It explores 3GPP-based solutions and reviews relevant literature to address existing challenges. Furthermore, this research delves into location management issues in the context of architectural enhancements and key enabling technologies such as network slicing (NS) and edge computing. It assesses how advancements in these areas necessitate updates to location management practices to ensure they remain effective and efficient in the evolving network landscape.
P-2.30 Transforming Mage AI Workflows into Standardized CWL and Python Pipelines
Stefan-Cristian Jarcau, Jr (Siemens Company, Romania); Gabriel-Mihail Danciu (Siemens, Romania); Septimiu Nechifor (Siemens SRL, Romania); Erika Duriakova (Insight Centre for Data Analytics & University College Dublin, Ireland); Grigorios Koutantos (WINGS ICT Solutions, Greece); Tarek Elsaleh (University of Surrey, United Kingdom (Great Britain)); Panagiotis Vlacheas (WINGS ICT SOLUTIONS, Greece); Mihnea Ionut Lopataru (Siemens, Romania)
Mage AI provides a powerful platform for designing and executing data pipelines. Still, its integration with workflow standards such as the Common Workflow Language (CWL) and Python-based workflows is essential to ensure reproducibility, portability, and compatibility. This paper introduces a methodology for converting Mage workflows into CWL and Python standard flows using custom scripts and transformation logic. We detail the implementation, the challenges addressed, and the broader implications of this approach while situating it within the wider context of workflow management and execution. The experimental results demonstrate significant performance improvements that validate the benefits of adopting this approach in real-world scenarios.
P-2.31 FOR-FREIGHT: a Next-Generation Platform for Optimizing Multimodal Logistics and Enhancing Sustainability
Andreas Gavrielides (University of Antwerp, Belgium & IMEC, Belgium); Ngoc Quang Luong (University of Antwerpen – Imec, Belgium); Georgia Ayfantopoulou (Centre for Research and Technology Hellas – Hellenic Institute of Transport, Greece); Sofoklis Dais (Centre for Research and Technology Hellas (CERTH), Greece); Katerina Batzou (Centre for Research and Technology Hellas, Greece); Giota Lilli (eBOS Technologies Ltd & R&D Department eBOS Technologies Ltd, Cyprus); Jorge Feliu Escagüés and Alicia Enríquez (Fundación Valenciaport, Spain); Jorge Melero Corell and Gonzalo Sandiás Corbillón (Terminal Industry Committee 4. 0, Spain); Stefan Stefanescu (Beia Consult International SRL, Romania); Andra Luciana Marcu (ATG-Danubius University, Romania); Nicoleta Capbun (Beia Consult International SRL, Romania); George Suciu (Politehnica University of Bucharest & BEIA Consult International SRL, Romania); George Agapiou (WINGS ICT SOLUTIONS, Greece); Sokratis Barmpounakis and Grigorios Koutantos (WINGS ICT Solutions, Greece); Panagiotis Demestichas (University of Piraeus, Greece); Johann Marquez-Barja (University of Antwerpen & IMEC, Belgium)
The FOR-FREIGHT (Flexible, multi-mOdal and Robust FREIGHt Transport) project represents a pioneering initiative aimed at revolutionizing multimodal logistics through the optimization of transport capacity, sustainability, and efficiency. This paper explores the core functionalities of the FOR-FREIGHT platform, which integrates innovative solutions to enhance existing logistics systems and reduce the average cost of freight transport. Central to the project is the development of a cloud-based platform that combines advanced technologies such as Internet of Things (IoT), Artificial Intelligence (AI)/ Machine Learning (ML), and Big Data Management to streamline logistics processes, enable real-time decision-making, and optimize the end-to-end management of multimodal services. The core functionalities of the platform include advanced routing recommendations, real-time re-planning in response to disruptions, transport cost and emission predictions, resource optimization, and the integration of legacy systems, ensuring compatibility with current infrastructures. By addressing challenges in airports, ports, inland terminals, and various logistics nodes, FOR-FREIGHT enables the efficient management of goods and freight flows across these hubs. A key aspect of the project is its emphasis on standardization and interoperability, fostering an open marketplace that enables seamless communication and collaboration across diverse stakeholders. As the project advances, it aims to not only redefine multimodal logistics practices but also establish sustainable, efficient standards for the industry, facilitating the adoption of Information and Communication Technologies (ICT)-driven innovations in the logistics sector. This paper provides a detailed overview of the objectives, methodologies, and expected impacts on the logistics landscape, highlighting the transformative potential of the FOR-FREIGHT platform in the context of future multimodal transportation systems and possible extensions using future 6G networks.
P-2.32 Efficient Deployment of Cell-Free Massive MIMO with Coordinated Network-Controlled Repeaters
Shuto Fukue (KDDI Research Inc., Japan); Shunsuke Kamiwatari, Masaaki Ito and Issei Kanno (KDDI Research, Inc., Japan)
This paper discusses a network-controlled repeater (NCR)-assisted millimeter wave (mmWave) cell-free (CF)-massive multiple-input multiple-output (mMIMO) systems. CF-mMIMO is promising for high-quality wireless coverage but faces fronthaul challenges related to cost, deployment time, and installation limits. NCR, standardized in 3GPP, offers a solution with its beamforming capability, enabling coverage extension and reduced deployment costs. The paper explores integrating NCRs with centralized base stations (BSs) in realistic networks, proposing a simple analog/digital beamforming approach. Performance is compared with traditional centralized mMIMO and CF-mMIMO with wired fronthaul. Results indicate the proposed system surpasses benchmarks while being cost-efficient.
P-2.33 Computationally Effective Model for Power Density Simulations in the Region of Human Head and Eyes for 6G Wireless Systems
Lukasz Januszkiewicz (Lodz University of Technology, Poland & Institute of Electronics, Poland); Paolo Di Barba (University of Pavia, Italy); Jarosław Kawecki (Lodz University of Technology, Poland)
Assessing the impact of electromagnetic waves on the human head and eyes at the high frequencies used in 6G systems requires efficient computational tools. This article introduces a novel, two-dimensional numerical model designed for this purpose. Developed for 26 GHz and adaptable to higher frequencies, the model offers a significant reduction in computational resources compared to the Finite-Difference Time Domain (FDTD) method, from tens of hours to the hundreds of seconds.
P-2.34 Path Loss Optimization in RIS-Aided Networks
Adam Samorzewski (Poznan University of Technology, Poland & Rimedo Labs, Poland); Adrian Kliks (Poznan University of Technology, Poland)
This paper analyzes radio signal propagation in Poznan (Poland) to determine optimal placements of Reconfigurable Intelligent Surfaces (RISs) in a 5G/6G Radio Access Network with eight SISO/MIMO Base Stations (BSs). Simulations under LOS/NLOS conditions in a dense urban environment show that RISs can significantly reduce path loss and improve coverage, particularly where BSs alone are insufficient. The study highlights RISs as a cost-effective way to enhance network performance and offers practical guidance for future deployments.
P-2.35 Optimization of Actual EIRP Control for Massive MIMO Base Stations Leveraging Beam Broadening and Angular Spread
Marcin Rybakowski (Nokia Solution and Networks, Poland & Wroclaw University of Science and Technology, Poland); Kamil Bechta (Nokia, Poland); Christophe Grangeat (Nokia, France); Azra Zejnilagic (Nokia, Germany); Pawel Kabacik (Wroclaw University of Science and Technology, Poland)
We present a novel approach for controlling the actual EIRP transmitted by mMIMO base stations to comply with RF EMF human exposure limits. In the proposed method, actual EIRP control is performed by beamwidth broadening using the radio channel angular spread estimation. This approach results in a reduction of the actual EIRP and EMF exposure from the base station while minimizing the impact on the effective antenna gain in the direction of served user equipment. The analysis indicates that such beam broadening techniques offer significant benefits for extreme massive MIMO base stations envisioned for 6G deployments, particularly those employing very narrow beamwidths.
P-2.36 5G mmWave RIS-Based User Localization Using Side Link Communication Protocols
Ayoub Mohammed Toubal and Ahmad Shokair (Greenerwave, France); Julien de Rosny (CNRS, ESPCI Paris, PSL Research University, France); Youssef Nasser and Geoffroy Lerosey (Greenerwave, France)
Reconfigurable Intelligent Surfaces (RIS) offer a promising solution for enabling dynamic and software-based beam control in wireless communication systems. In this work, we introduce a beam management protocol integrated with a localization algorithm for RIS-assisted communication. Our approach is based on a user-controlled RIS, where feedback from the user dynamically governs the RIS operation. An SDR-based side link was designed specifically to implement communication protocols enabling signaling information exchange between the RIS controller and the user equipment (UE). To implement this approach, we deploy a 5G millimeter wave (mmWave) RIS-based communication system operating at 28 GHz with a side channel for UE positioning purposes. Our measurements demonstrate the system’s capacity for precise UE localization, exhibiting an angle mismatch ranging from 1◦to 2◦. To assess the performance, we also conduct Fisher Information (FI) and Cram´er-Rao lower bound (CRLB) analyses, which provide insights into the accuracy limits of the proposed positioning system.
P-2.37 Physical Layer Authentication Within 6G Campus Networks
Andreas Weinand (University of Kaiserslautern-Landau, Germany); Alireza Hasani (NXP, Germany); Abdullah Ahmad (PHYSEC GmbH, Germany); Sachinkumar Bavikatti Mallikarjun (University of Kaiserslautern (RPTU), Germany); Jonas Wilking (University of Kaiserslautern-Landau, Germany); Marc Hafner (Ruhr University Bochum, Germany); Javier Velasquez Gomez and Harsha Master (NXP, Germany); Hans D. Schotten (University of Kaiserslautern, Germany)
In this work, we propose the use of Physical Layer Security (PLS) schemes for authentication procedures, which we refer to as Physical Layer Authentication (PLA) schemes, in 6G campus networks. These schemes can be classified into two primary categories: passive and active. Passive schemes leverage physical device features or environmental characteristics, such as the radio channel, to authenticate network users or retrieve further context information. They are non-invasive and can be integrated into existing radio protocols without modifications. In contrast, active schemes involve the receiver authenticating the transmitter using a tag signal designed by the transmitter and embedded into the transmitted signal. Unlike passive schemes, active schemes modify the transmitted signal to facilitate authentication by the receiver, necessitating significant alterations to mobile network specifications such as 3GPP. Prior to presenting possible PLA solutions, we analyze potential 6G applications, focusing on Joint Communication and Sensing (JCAS) applications. We further propose architectural amendments to the 3GPP specifications to support JCAS and PLS functionalities.
P-2.38 Enhancing Security and Risk Mitigation Strategies for 5G O-RAN Deployment
Rick Lin (Morrison Academy, Taiwan); Ji-Zhen Wu (National Chung Hsing University, Taiwan); Iuon-Chang Lin (Chung Hsing University, Taiwan)
With the growing emphasis on Environmental, Social, and Governance (ESG) issues, enterprises increasingly use sustainability reports for information disclosure. Information security is a crucial component of ESG, especially in the context of 5G Open Radio Access Network (O-RAN) development. Telecom operators, network equipment vendors, system integrators, and application service providers collaborate based on various business models to meet the security, reliability, and specialization needs of mobile networks. However, the lack of real-world implementation cases and clear business models for multi-stakeholder supply chains results in cybersecurity uncertainties. This study identifies cybersecurity risks in 5G O-RAN private networks by verifying compliance with 3GPP security frameworks and conducting case studies and expert interviews. Based on the findings, risk management strategies are proposed for stakeholders such as field owners, system integrators, and telecom operators to enhance cybersecurity resilience.
