Gianluca Rizzo

Berk Buzcu, Marcin Pakula, Gevher Yesevi Keskin et al.

Future 6G networks will operate across distributed and heterogeneous domain infrastructures, making conventional single-domain management insufficient for proactive, trustworthy automation. Network Digital Twins (NDTs) enable what-if analysis, AI-assisted optimization, and risk-free validation of control actions before deployment, yet monolithic end-to-end twins remain impractical due to scalability, fidelity, and cross-domain coordination challenges. Accordingly, this paper proposes a Digital Twin-enabled 6G architecture that exposes NDT capabilities as a specialized service domain within a multi-domain orchestration framework built on a state-of-the-art service-based 6G architecture. A DT Orchestrator interprets \textit{predictive} and \textit{prescriptive} what-if queries and composes domain-specific DT modules and simulators on demand, while decision authority remains with the requesting entity. Furthermore, a generalized workflow covers telemetry synchronization, simulation-based decision support, and closed-loop execution. The framework is demonstrated through a green-networking use case that couples a system-level O-RAN cellular digital twin component with a two-stage solar-allocation simulator, evaluated over a 105-base-station deployment in Poznan using simulative datasets. Joint coverage and renewable optimization reduces daily grid consumption by 28.5\% with 32 solar panels at the diminishing-returns threshold, with 17 base stations identified as both coverage-active and high-priority solar candidates as evidence that cross-domain NDT coordination enables sustainable, intent-driven 6G network management.

Gaetano Manzo, Sebastian Otalora, Marco Ajmone Marsan et al.

Opportunistic communications are expected to playa crucial role in enabling context-aware vehicular services. A widely investigated opportunistic communication paradigm for storing a piece of content probabilistically in a geographica larea is Floating Content (FC). A key issue in the practical deployment of FC is how to tune content replication and caching in a way which achieves a target performance (in terms of the mean fraction of users possessing the content in a given region of space) while minimizing the use of bandwidth and host memory. Fully distributed, distance-based approaches prove highly inefficient, and may not meet the performance target,while centralized, model-based approaches do not perform well in realistic, inhomogeneous settings. In this work, we present a data-driven centralized approach to resource-efficient, QoS-aware dynamic management of FC.We propose a Deep Learning strategy, which employs a Convolutional Neural Network (CNN) to capture the relationships between patterns of users mobility, of content diffusion and replication, and FC performance in terms of resource utilization and of content availability within a given area. Numerical evaluations show the effectiveness of our approach in deriving strategies which efficiently modulate the FC operation in space and effectively adapt to mobility pattern changes over time.