Network Digital Twins (NDTs) offer a structured framework for modeling, predicting, and optimizing wireless networks. This paper presents a modular NDT implementation based on the GreyCat platform, integrating graph-based data models and external functional algorithms for indoor radio coverage prediction. For the first time, we implement an NDT system aligned with ITU-T Recommendation Y.3090, covering both basic and functional model instantiation from modular and interoperable abstract structures. We generated a practical dataset using a software-defined radio (SDR)-based OpenAirInterface5G setup, with a gNB and commercial UE deployed in a controlled environment. This real-world dataset was used to benchmark Gaussian Process Regression (GPR) and Convolutional Neural Network (CNN) models for predicting RSRP-based radio coverage. Our results show that CNN outperforms GPR in under-sampled conditions, and we demonstrate how the modular architecture supports flexible model integration and benchmarking. This work represents a significant step toward practical, data-driven NDT deployments for wireless systems.

The concept of Digital Twin (DT) holds great potential for all sectors seeking to monitor, automate, and optimize their processes. This paper focuses specifically on Digital Twin Networks (DTNs) and City Digital Twins (CDTs), offering an analysis of the relevant literature while exploring their interrelationship, which is essential for urban planners and network operators. Although DTN is foreseen as a pillar of future 5G and 6G network architectures, it is often addressed in isolation in the existing literature (i.e., with limited consideration of other domain-specific constraints), even though it constitutes an essential asset in urban environments. In contrast, CDT is often based on the idea of infallible connectivity, which is an optimistic assumption. This study details the benefits and challenges associated with the integration of DTNs into CDTs, paving the way for further research in this field.

Lower layer retransmission schemes in 5G Non-Terrestrial Networks (5G-NTN), in particular Hybrid Automatic Repeat Request (HARQ) from the Medium Access Control (MAC) layer, are severely hampered by the large Round Trip Times (RTTs) imposed by satellite components. In this demonstration, we show that enabling Radio Link Control (RLC) layer retransmissions can significantly increase throughput without additional processing complexity. Using the OpenAirInterface (OAI) 5G-NTN suite, we showcase the effectiveness of RLC Acknowledged Mode (AM) retransmissions in facilitating early recovery of lost packets and maintaining reasonable Quality of Service (QoS), even in the absence of HARQ feedback from the MAC layer.

The current Cellular V2X (C-V2X) standard for direct communication between vehicles is based on the so called Semi-Persistent Scheduling (SPS) algorithm. It is based on the multiple access structure of 5G New Radio (NR) and exploits sensing and persistence to realize a randomized multiple access. We consider the application of SPS to support periodic broadcast traffic where no acknowledgments are provided. Persistence is a key feature, which consists of a node using the same resource for its transmissions for multiple frames. The specific resource used is randomly selected from among those that are perceived to be idle, and reselected anew after a randomized number of frames. We define a model to gain insight into the interplay between persistence and key performance metrics for the type of traffic considered, namely probability of successful delivery and Age of Information (AoI). A core version of the model is validated against simulations and used to show that there exists an optimal level that minimizes AoI. The model is then extended to account for a distance-dependent propagation model, allowing further insight into the effects of the interplay between sender-receiver distance and persistence. Finally, an even more detailed model is investigated, using ns-3-based simulations. This further analysis confirms the qualitative behaviors revealed by the analytical model and provides more insight into the complex interactions of system parameters and channel characteristics. The obtained results help to identify the limits of SPS, opening the way to system-principled parameter optimization and design of more powerful variants of the multiple access scheme.

Medium Access Control (MAC) main functions encompass contention for channel access, packet scheduling, error control, and data integrity. Channel contention is a collective function involving all stations in the network, while data integrity pertains to data flows of each individual station. We propose a design where contention related functions are separated from other data management functions. The hinge connecting contention and other data management functions is a flow control algorithm, aiming at guaranteeing stability of contention queues and load on the MAC channel. With reference to Carrier-Sense Multiple Access (CSMA), we define an analytical model of contention queues under non saturated traffic. An asymptotic analysis of the model for large number of stations yields a closed form of the optimal flow control rate. The insight gained from the model is used to design an adaptive flow control algorithm that guarantees throughput optimality for all values of the number of stations.

Modern connected vehicles are equipped with a large number of sensors, which enable a wide range of services that can improve overall traffic safety and efficiency. However, remote access to connected vehicles also introduces new security issues affecting both inter and intra-vehicle communications. In fact, existing intra-vehicle communication systems, such as Controller Area Network (CAN), lack security features, such as encryption and secure authentication for Electronic Control Units (ECUs). Instead, Original Equipment Manufacturers (OEMs) seek security through obscurity by keeping secret the proprietary format with which they encode the information. Recently, it has been shown that the reuse of CAN frame IDs can be exploited to perform CAN bus reverse engineering without physical access to the vehicle, thus raising further security concerns in a connected environment. This work investigates whether anonymizing the frames of each newly released vehicle is sufficient to prevent CAN bus reverse engineering based on frame ID matching. The results show that, by adopting Machine Learning techniques, anonymized CAN frames can still be fingerprinted and identified in an unknown vehicle with an accuracy of up to 80 %.

Although many target applications in VANETs are information-centric, the performance of Named Data Networking (NDN) in vehicular ad-hoc networks is severely hampered by persistent network partitioning, typical of many vehicular scenarios. Existing approaches try to address this issue by relying on opportunistic communications. However, they leave open the crucial issue of how to guarantee content persistence and tight QoS levels while optimizing the resource utilization in the vehicular environment. In this work we propose DeepNDN, a communication scheme based on the joint application of NDN and of probabilistic spatial content caching, which enables content retrieval in fragmented and dynamic network topologies with tight delay constraints. We present a data-based approach to DeepNDN management, based on locally modulating content replication and delivery in order to achieve a target hit ratio in a resource-efficient manner. Our management algorithm employs a Convolutional Neural Network (CNN) architecture for effectively capturing the complex relations between spatio-temporal patterns of mobility and content requests and DeepNDN performance. Its numerical assessment in realistic, measurement-based scenarios suggest that our management approach achieves its target set goals while outperforming a set of reference schemes.

The main objectives of the Intelligent Transportation Systems (ITS) vision is to improve road safety, traffic management, and mobility by enabling cooperative communication among participants. This vision requires the knowledge of the current state of the road traffic, which can be obtained by collecting Floating Car Data (FCD) information using Dedicated Short-Range Communication (DSRC) based on the IEEE 802.11p standard. Most of the existing FCD collection protocols have been evaluated via simulations and mathematical models, while the real-world implications have not been thoroughly investigated. This paper presents an open-source implementation of two state-of-the-art FCD collection algorithms, namely BASELINE and DISCOVER. These algorithms are implemented in an open-source vehicular prototyping platform and validated in a real-world experimental setup.