AIU1 – B5G/6G Applications
Wednesday, 4 June 2025, 16:00-17:30, room 1.B
Session Chair: Carles Antón-Haro (Centre Tecnologic de Telecomunicacions de Catalunya (CTTC), ES)
Modelling 6DoF VR Gaming Traffic for Next-Generation Networks
Ahmed Chyad, Syed Danial Ali Shah, Mohammed M. H. Qazzaz, Maryam Hafeez and Syed Ali Raza Zaidi (University of Leeds, United Kingdom (Great Britain))
The increasing adoption of Extended Reality (XR) applications demands advanced traffic modelling to optimise next-generation wireless networks. Existing models often rely on fixed data rates (e.g., 50 Mbps) and frame rates (e.g., 60 Hz), which are insufficient for modern VR systems. This paper introduces an enhanced traffic modelling framework for highly interactive 6-degree-of-freedom (6DoF) VR gaming, incorporating video, audio, and control streams with support for higher frame rates (up to 120 Hz) and data rates (up to 251.9 Mbps). Built on empirical data from a high-fidelity VR setup, the model uses statistical analysis to fit data rate and inter-arrival times to probability distributions. The generalised logistic distribution is identified as the best fit for data rates, while the generalised extreme value distribution accurately represents inter-arrival times. Model accuracy is validated using Kullback-Leibler divergence. The findings offer critical insights for traffic generation, network planning, and resource allocation, supporting scalable and immersive XR experiences in future wireless networks.
Digital Twin-Based 6G Testbed for Immersive Outdoor Robotics: Capacity and Latency Analysis of the Interaction with Real and Virtual Objects
Raul Lozano (Universitat Politecnica de Valencia, Spain); Nuria Molner (Universitat Politècnica de València & iTEAM Research Institute, Spain); David Gomez-Barquero (Universitat Politecnica de Valencia, Spain); Gerardo Martinez-Pinzon and Manuel Fuentes (Fivecomm, Spain); Miguel Saura, Sandra Moreno and Angel Soriano (Robotnik, Spain)
This paper presents an experimental Digital Twin platform developed as a Beyond 5G testbed for immersive remote and autonomous driving applications for mobile robots. The testbed, deployed in UPV facilities, integrates use cases involving the immersive and cyber-physical remote driving of mobile robots, which interact with real and virtual objects. In this controlled environment, we study the capacity and latency requirements of future autonomous driving use cases based on sensor offloading to the Edge. Our study focuses on evaluating the influence of 5G communication across multiple latency sources, including video streaming, AI-assisted perception, and telecontrol. By identifying key bottlenecks in mobile robot teleoperation, this work provides insights into the latency constraints that must be addressed by future 6G networks to enable the large-scale deployment of autonomous driving across different industry verticals. The results show that, while the bottleneck of the system are the robot’s motors (up to 589.65 ms), the 360 video latency (up to 433.3 ms) and the telecontrol latency (122.5 ms) could be improved by network enhancements.
Design and Implementation of Point Cloud-Based Space Integration with Quality-Aware Optimization
Yui Maruyama, Tatsuya Amano and Hirozumi Yamaguchi (Osaka University, Japan)
Hybrid-metaverses, integrating physical and virtual spaces, require robust system architecture to effectively manage shared 3D object representations across multiple users. This paper presents a comprehensive implementation strategy of hybrid-metaverse system using the Robot Operating System (ROS) framework, focusing on real-time point cloud processing and transmission. We describe the system architecture consisting of multiple ROS nodes for point cloud acquisition, processing, and distribution, along with a sophisticated communication layer for managing multi-user interactions. Our implementation incorporates distributed optimization techniques using Input Convex Neural Networks (ICNN) and the Alternating Direction Method of Multipliers (ADMM) to efficiently handle bandwidth allocation and quality adjustment of 3D objects. We operated simulation of optimization method and evaluated performance under 6G setting. Our optimization method shows promising results in resource utilization, achieving 93-94.6% accuracy in modeling user utility and up to 60% faster convergence compared to baseline centralized approaches, contributing to the balance between high-fidelity representation and efficient data management in hybrid-metaverses.
Low-Latency Rate-Distortion-Perception Trade-off: a Randomized Distributed Function Computation Application
Onur Günlü (Linköping University, Sweden); Maciej Skorski (Czech Technical University in Prague, Czech Republic); H. Vincent Poor (Princeton University, USA)
Semantic communication systems, which focus on transmitting the semantics of data rather than its exact reconstruction, redefine the design of communication networks for transformative efficiency in bandwidth-limited and latency-critical applications. Addressing these goals, we tackle the rate-distortion-perception (RDP) problem for image compression, a critical challenge in achieving perceptually realistic reconstructions under rate constraints. Formulated within the randomized distributed function computation (RDFC) framework, we establish an achievable non-asymptotic RDP region, providing finite blocklength trade-offs between rate, distortion, and perceptual quality, aligning with semantic communication objectives. We extend this region to also include a secrecy constraint, providing strong secrecy guarantees against eavesdroppers via physical-layer security methods, ensuring resilience against quantum attacks. Our contributions include (i) establishing achievable bounds for non-asymptotic RDP regions under realism and distortion constraints; (ii) extending these bounds to provide strong secrecy guarantees; (iii) characterizing the asymptotic secure RDP region under a perfect realism constraint; and (iv) illustrating significant reductions in rates and the effects of secrecy constraints and finite blocklengths. Our results provide actionable insights for designing low-latency, high-fidelity, and secure image compression systems with realistic outputs, advancing applications, e.g., in privacy-critical domains.
Empirical Analysis of 5G TDD Patterns Configurations for Industrial Automation Traffic
Oscar Adamuz-Hinojosa and Felix Delgado-Ferro (University of Granada, Spain); Núria Domènech (Neutroon Technologies, Spain); Jorge Navarro-Ortiz and Pablo Muñoz (University of Granada, Spain); Seyed Mahdi Darroudi (Neutroon Technologies & IEEE Member, Spain); Pablo Ameigeiras and Juan M. Lopez-Soler (University of Granada, Spain)
The digital transformation driven by Industry 4.0 relies on networks that support diverse traffic types with strict deterministic end-to-end latency and mobility requirements. To meet these requirements, future industrial automation networks will use time-sensitive networking, integrating 5G as wireless access points to connect production lines with time-sensitive networking bridges and the enterprise edge cloud. However, achieving deterministic end-to-end latency remains a challenge, particularly due to the variable packet transmission delay introduced by the 5G system. While time-sensitive networking bridges typically operate with latencies in the range of hundreds of microseconds, 5G systems may experience delays ranging from a few to several hundred milliseconds. This paper investigates the potential of configuring the 5G time division duplex pattern to minimize packet transmission delay in industrial environments. Through empirical measurements using a commercial 5G system, we evaluate different TDD configurations under varying traffic loads, packet sizes and full buffer status report activation. Based on our findings, we provide practical configuration recommendations for satisfying requirements in industrial automation, helping private network providers increase the adoption of 5G.
