Plastras Stefanos

Abstract:
UNITY-6G introduces a AI-natively framework that unifies terrestrial (TN), non-terrestrial (NTN), and non-public networks (NPN), treating connectivity, computing, and intelligence as interdependent resources. The architecture utilizes an Inter-Domain Management Orchestrator (IDMO) based on Service-Based Management Architecture (SBMA) principles to coordinate services across heterogeneous domains. A core pillar of the framework is its AI-native design through autonomous agentic AI workflows following a standardized MS-AE-DE-ACT (Monitoring, Analytics, Decision, and Actuation) logical patterns. To enhance resource efficiency and sustainability, the architecture integrates Digital Twins (DT) for proactive system modeling and semantic communications to prioritize task-relevant information transfer. Security is addressed through a Trust Architecture leveraging Distributed Ledger Technology (DLT) for cross-domain auditability. The framework's utility is validated through proof-of-concepts targeting sustainable disaster handling, immersive XR/holographic communications, and time-sensitive services for Industry 4.0. The presented advances establish a foundation for the continuous development of high-performance, autonomous 6G systems.

Abstract:
Network Exposure Application Programming Interfaces (APIs) are pivotal for 5G/6G ecosystems, enabling third-party developers to access granular network data via standardized interfaces. This paper presents a proof-of-concept (PoC) implementation that exposes 5G Core (5GC) User Equipment (UE) location data by integrating the Common API Marketplace and Repository Architecture (CAMARA) Device Location API, the Common API Framework (OpenCAPIF) framework and a 3rd Generation Partnership Project (3GPP)-compliant Network Exposure Function (NEF). We introduce a Transformation Function (TF) that bridges the gap between high-level CAMARA requests and 3GPP MonitoringEvent operations, simplifying developer interaction with complex core network functions. The framework is validated using an open-source 5 GC environment through an Artificial Intelligent (AI)-native crowd-mobility application. Results demonstrate the feasibility of standardized network exposure and provide design insights for future 6G exposure platforms.

Abstract:
The integration of Non-Terrestrial Networks (NTN) with terrestrial 5G networks (TNs) presents unprecedented opportunities for achieving ubiquitous connectivity, particularly for data-intensive and latency-sensitive applications. However, effectively managing multiple network interfaces across heterogeneous terrestrial and non-terrestrial Radio Access Technologies (RATs) remains challenging. In this paper, a Multi-RAT Dual Connectivity enabler for SUNRISE-6G experimentation platform is proposed, that relies on MultiPath TCP (MPTCP) as a key enabler for ubiquitous connectivity over TN-NTN infrastructure. The approach encompasses two complementary experimental setups: a fully virtualized emulation environment and a hybrid setup combining physical devices with emulated NTN elements. Results demonstrate that while MPTCP successfully enables multi-connectivity across terrestrial and satellite networks, optimal performance requires careful consideration of path characteristics and protocol parameters.

Abstract:
The Network Exposure Function (NEF) is a key component of the 5G and Beyond 5G (B5G) network, providing standardized access for third-party applications to network data and capabilities. Among these capabilities is the Event Monitoring API, which allows external applications to subscribe to specific network events. One such event is the reporting of user equipment (UE) location changes, enabling services that rely on real-time location awareness. In this context, this paper presents the design and implementation of an open-source, cloud-based NEF Event Monitoring Application Programming Interface (API) that adheres to relevant 3rd Generation Partnership Project (3GPP) standards. The proposed architecture minimizes integration complexity and provides a flexible framework for efficient deployment and utilization of future 6G location services, fostering innovation in intelligent environments and enabling a wide range of third-party applications.

Abstract:
The rapidly increasing number of mobile devices and resource-intensive applications poses substantial obstacles to traditional centralized mobile cloud computing, resulting in increased latency and decreased service quality. Edge computing, which places server capabilities at access nodes, provides a promising solution to the aforementioned issues. However, maintaining operational edge service nodes at each access node can be costly and inefficient. We propose and evaluate a scheme that combines a heuristic service node selection algorithm with machine learning based computational load prediction, with the goal of minimizing latency and balancing load among service nodes. Simulation experiments demonstrate that the proposed scheme substantially enhances system performance, paving the way for a more efficient and responsive edge network infrastructure, particularly in 5G and 6G mobile communication environments.