Programa del congreso

The vision for 6G is built on the need for predictability, reliability, and time-sensitiveness (i.e. deterministic networking), yet achieving deterministic communication across diverse network domains remains challenging. PREDICT-6G addresses this gap by pioneering an AI-driven, multi-domain framework that integrates deterministic networking principles across heterogeneous infrastructures. By combining a Multi-Domain Data Plane (MDP) with an AI-driven Multi-Stakeholder Inter-Domain Control Plane (AICP), the project enables seamless orchestration of end-to-end deterministic services, ensuring stringent latency, reliability, and synchronization across 3GPP (B5G), TSN, DetNet, and Wi-Fi technologies.

This paper reviews the key highlights of PREDICT-6G and introduces the results obtained.

Next-generation wireless networks will integrate sensing and communication, enabling intelligent and perception-driven connectivity. The MultiX approach introduces the MultiX Fusion Perceptive 6G-RAN (MP6R), a framework that leverages multi-band, multi-static, multi-sensor, and multi-technology capabilities to enhance situational awareness and network adaptability. To address network heterogeneity, MultiX proposes a distributed architecture comprising the MultiX Perception System (MPS) for embedded sensing, the MP6R Controller (MP6RC) for network orchestration, and the Data Access and Security Hub (DASH) for secure multi-sensor data processing. As a proof of concept, we present the Multi-Layer Digital Twin for Industrial Manufacturing, demonstrating how contextual data optimizes decision-making and real-time network adaptation. By integrating sensing across the RAN, MultiX transforms wireless networks into active perception platforms, supporting applications such as autonomous systems, smart manufacturing, and environment-aware wireless optimization,paving the way for future 6G networks.

This work presents a perspective on addressing the underutilization of computing resources in FPGA SoC devices deployed in 5G radio and edge computing infrastructure. The initial step in this approach involves developing a resource management layer capable of dynamically migrating and scaling functions within these devices in response to contextual events. This layer serves as the foundation for designing a hierarchical, data-driven micro-orchestrator responsible for managing the lifecycle of functions in FPGA SoC devices. In this paper, the proposed resource management layer is utilized to reconfigure a function based on events identified by a computer vision edge application.

Vehicular communications are expected to become one of the services that will benefit the most from 5G networks. The low latency and unprecedented high data rates 5G is expected to provide are a key enabler for new vehicular services. However, there are still different topics hindering its global adoption. For instance, certain cellular coverage holes may prevent having continuous V2X services as tele-operated driving. Similarly, situations such as traffic congestions can drain radio resource availability. In this context, the 6GTWINROAD-MNO project aims to investigate how to design the control plane of future 6G V2X network slices. Particularly, in this paper we merge the network exposure APIs defined in the 5G Core and the Open-Radio Access Network (O-RAN) architecture to provide a system that offers advanced exposure functions for future V2X services. In this context, we present two different exposure functions: predictive quality of service (QoS) for tele-operated driving and a digital twin for cooperative perception.

This paper provides an overview of the TRAINER-6G project, targeted to a special session devoted to spreading awareness within the URSI community on current research projects in 6G conducted by national academia and research centers. Since the project has just recently started, the paper describes its starting hypothesis and objectives. Besides, the link of the envisaged research with previous results is highlighted.