This paper presents VaN3Twin—the first open source, full-stack Network Digital Twin (NDT) framework for simulating the coexistence of multiple Vehicle-to-Everything (V2X) communication technologies with accurate physical-layer modeling via ray tracing. VaN3Twin extends the ms-van3t simulator by integrating Sionna Ray Tracer (RT) in the loop, enabling high-fidelity representation of wireless propagation, including diverse Line of Sight (LoS) conditions with a focus on LoS blockage due to other vehicles’ meshes, Doppler effect, and site-dependent effects—e.g., scattering and diffraction. Unlike conventional simulation tools, the proposed framework supports realistic coexistence analysis across Dedicated Short Range Communication (DSRC) and Cellular-V2X (C-V2X) technologies operating over a shared spectrum. A dedicated interference tracking module captures cross-technology interference at the time-frequency resource block level and enhances Signal to Interference plus Noise Ratio (SINR) estimation by eliminating artifacts such as the bimodal behavior induced by separate LoS/NLoS propagation models. Compared to field measurements, VaN3Twin reduces application-layer disagreement by 50% in rural and over 70% in urban environments with respect to current state of the art simulation tools, demonstrating its value for scalable and accurate digital twin–based V2X coexistence simulation.

The growing demand for high-speed, reliable wireless connectivity in 6 G networks necessitates innovative approaches to overcome the limitations of traditional Radio Access Network (RAN). Reconfigurable Reconfigurable Intelligent Surface (RIS) and Network-Controlled Repeater (NCR) have emerged as promising technologies to address coverage challenges in high-frequency millimeter wave (mmW) bands by enhancing signal reach in environments susceptible to blockage and severe propagation losses. In this paper, we propose an optimized deployment framework aimed at minimizing infrastructure costs while ensuring full area coverage using only RIS and NCR. We formulate a cost-minimization optimization problem that integrates the deployment and configuration of these devices to achieve seamless coverage, particularly in dense urban scenarios. Numerical results confirm that this framework significantly reduces the network planning costs while guaranteeing full coverage, demonstrating RIS and NCR's viability as cost-effective solutions for next-generation network infrastructure.

The widely adopted satellite-based positioning systems have shown limitations in meeting the needs of emerging mobile radio network services, which require consistent, high-quality, real-time positioning data. This study introduces RUN-O-RAN, an innovative network-based ranging system integrated as a micro-service within the 5th generation (5G) Open Radio Access Network (O-RAN). RUN-O-RAN makes opportunistic use of uplink reference signals and is robust against hardware and network impairments. It offers seamless deployment, user transparency, and adaptability to varying application requirements, leveraging the programmability of the network. Through a custom-built testbed based on software-defined 5G base stations (gNBs) and commercial user equipment, we comprehensively evaluate this solution across diverse scenario sets and compare its accuracy against satellite-based positioning. The achieved results demonstrate how this system represents the first effective localization O-RAN micro-service.

The increasing complexity of 6G networks demands advanced tools for network management and simulation. This demo pioneers the integration of ns-3 and NVIDIA Sienna®RT, laying the foundation for the first multi-Radio Access Technologies (RAT) full-stack, open-source Digital Network Twin (DNT). The introduction of a deterministic ray tracer for an accurate channel modeling into ns-3 enables realistic and site-specific simulation which cannot be achieved via stochastic channel models. Tested in a challenging vehicular urban scenario, the proposed framework demonstrates significant improvements in predicting dynamic wireless channels and its impact at higher network layers.

Smart Radio Environment (SRE) is a central paradigm in 6 G and beyond, where integrating Smart Radio Environment (SRE) components into the network planning process enables optimized performance for high-frequency Radio Access Network (RAN). This paper presents a comprehensive planning framework utilizing realistic urban scenarios and channel models to analyze diverse SRE components, including Reconfigurable Intelligent Surface (RIS), Network-Controlled Repeater (NCR), and advanced technologies like Simultaneous Transmitting and Reflecting RIS (STAR-RIS) and Trisectoral NCR (3SNCR). We propose two optimization strategies—Full Coverage Minimum Cost (FCMC) and Maximum Budget-Constrained Coverage (MBCC)—that address key cost and coverage objectives by considering both physical characteristics and scalable costs of each component, influenced by factors such as NCR amplification gain and RIS dimensions. Extensive numerical results demonstrate the significant impact of these models in enhancing network planning efficiency for high-density urban environments.

The increasing demand for mobile bandwidth is driving 5G networks toward the use of high-frequency spectrum, particularly the upper-6 GHz and mmWave bands. While these bands offer vast bandwidth potential, their propagation characteristics raise critical deployment challenges. This paper presents the first direct, on-field comparative evaluation of 5G standalone (SA) macro-cell deployments operating in these two bands, conducted in Milan, Italy. We show that the upper-6 GHz band can deliver wide-area urban coverage (up to 600 meters) with stable gigabit-level downlink throughput, even in (NLoS) scenarios. mmWave, traditionally deemed unsuitable for NLoS, exhibits strong performance via urban reflections, achieving up to 1.3 Gbps in downlink and 250 Mbps in uplink. Furthermore, outdoor-to-indoor connectivity at mmWave frequencies proves viable through glass facades, challenging pessimistic assumptions about penetration losses. These findings, derived from synchronized deployments and extensive measurements, provide new insights into the complementary roles of these bands and the practical feasibility of their integration into future 5G networks.

The advancement of Connected and Autonomous Vehicle (CAV) technology promises to revolutionize transportation systems, but robust and effective communication among CAVs is needed to ensure safety and efficiency. Vehicle-to-everything (V2X) communication, particularly vehicle-to-vehicle (V2V) communication, offers direct vehicular data exchange without burdening network infrastructure. However, the dynamic nature of vehicular scenarios and the strict application requirements pose critical challenges in the radio resource allocation domain. To address these challenges, this paper proposes an Open RAN (O-RAN)-based solution, leveraging O-RAN’s flexibility and programmability. The proposed solution employs standardized interfaces to collect and analyze traffic data, enabling centralized cross-base station resource allocation. Implemented as an O-RAN xApp, the solution demonstrates superior performance in large-scale vehicular simulations compared to existing radio allocation schemes, showcasing effectiveness in managing diverse traffic profiles and minimizing allocation collisions with negligible overhead. Evaluation against Mode 2 demonstrates the solution’s efficacy with respect to the standard. Overall, the study highlights for the first time O-RAN’s potential in managing radio resources for V2V communication.

Edge computing has been recently introduced to bring computational capabilities closer to end-users of modern network-based services, supporting existing and future delay-sensitive applications by effectively addressing the high propagation delay issue that affects cloud computing. However, the problem of efficiently and fairly managing the system resources presents particular challenges due to the limited capacity of both edge nodes and wireless access networks and the heterogeneity of resources and services’ requirements. To this end, we propose a techno-economic market where service providers act as buyers, securing both radio and computing resources to execute their associated end-users’ jobs while being constrained by a budget limit. We design an allocation mechanism that employs convex programming to find the unique market equilibrium point that maximizes fairness while ensuring that all buyers receive their preferred resource bundle. Additionally, we derive theoretical properties that confirm how the market equilibrium approach strikes a balance between fairness and efficiency. We also propose alternative allocation mechanisms and give a comparison with the market-based mechanism. Finally, we conduct simulations to numerically analyze and compare the performance of the mechanisms and confirm the market model's theoretical properties.

Millimeter wave (mmWave) communications have the potential to dramatically increase the throughput of 5G-and-beyond wireless networks. However, the challenging propagation conditions typical of higher frequencies require expensive base station densification to guarantee reliable Radio Access Networks (RANs). Integrated Access and Backhaul (IAB), a solution where wireless access and backhaul use the same waveform, spectrum, and protocol stack, has been proposed and standardized as a highly effective means of decreasing these costs. While IAB is considered a key enabler for high-frequency RANs, experimental research in this context is hampered by the lack of accessible testing platforms. In this demonstration, we showcase IABEST, a large-scale end-to-end IAB testbed based on open-source software and compatible with off-the-shelf hardware. We show how to deploy IABEST capabilities at scale on Colosseum, a publicly available massive channel emulator. Finally, we show how IABEST can support researchers in data collection and algorithm testing from the highest levels of network abstraction down to scheduling decisions.

The introduction of the mm-Wave spectrum into 5G NR promises to bring about unprecedented data throughput to future mobile wireless networks but comes with several challenges. Network densification has been proposed as a viable solution to increase RAN resilience, and the newly introduced Integrated-Access-and-Backhaul (IAB) is considered a key enabling technology with compelling cost-reducing opportunities for such dense deployments. Reconfigurable Intelligent Surfaces (RIS) have recently gained extreme popularity as they can create Smart Radio Environments by EM wave manipulation and behave as inexpensive passive relays. However, it is not yet clear what role this technology can play in a large RAN deployment. With the scope of filling this gap, we study the blockage resilience of realistic mm-Wave RAN deployments that use IAB and RIS. The RAN layouts have been optimised by means of a novel mm-Wave planning tool based on MILP formulation. Numerical results show how adding RISs to IAB deployments can provide high blockage resistance levels while significantly reducing the overall network planning cost.

Mm-Wave 5G NR promises to provide unprecedented access throughput to mobile radio networks but comes with several challenges. Network densification is the only viable solution to increase robustness in front of link outages due to random obstacles. While Integrated-Access-and-Backhauling (IA B) architecture is commonly considered the enabling technology to reduce the cost of such dense deployments, Reconfigurable Intelligent Surfaces (RISs) and Smart Repeaters (SRs) are very recently emerging as promising tools to provide further capabilities to 6G networks in Smart Radio Environments. However, the impact of these devices on next-generation networks is yet to be fully understood. In this paper, we provide a first answer to the questions arising in the deployment of mmWave RAN equipped with SRs and RISs. By means of mathematical programming models for full network planning optimization, we produce optimal network layouts that we leverage to assess in which scenario, in which position, and with which configuration SRs and RISs can better improve the reliability of such networks.

Network slicingmight radically change the relations among different actors of the telecommunications ecosystem, where new players, active in different markets, could benefit of tailored connectivity services based on different business strategies. We argue that for fully exploiting the opportunities offered by network slicing, dynamic sharing of resources is crucial not only for efficiency and cost savings, but also for enabling a resource negotiation that can unleash the potential of new business relations. We develop an automated mechanism that allows tenants to take strategic decisions to optimize the management of their slices based on their instantaneous demands and model their interaction as in marketplace. We integrate our solution, based on game theory, on a 3GPP calibrated system level simulator, where a slice-aware scheduler enforces the tenants’ decisions at the Nash Equilibrium (NE). We compare our proposal with a static baseline, that assigns a fixed share of resources to each slice, and show that, by dynamically trading resources in the market, tenants achieve lower costs, and, therefore, higher profits. We provide an algorithmic implementation that guarantees the convergence to a single NE and test the computational complexity of our algorithm to an increasing number of slices in the system.

With the capability to support gigabit data rates, millimetre-wave (mm-Wave) communication is unanimously considered a key technology of future cellular networks. However, the harsh propagation at such high frequencies makes these networks quite susceptible to failures due to obstacle blockages. Recently introduced Reconfigurable Intelligent Surfaces (RISs) can enhance the coverage of mm-Wave communications by improving the received signal power and offering an alternative radio path when the direct link is interrupted. While several works have addressed this possibility from a communication standpoint, none of these has yet investigated the impact of RISs on large-scale mm-Wave networks. Aiming to fill this literature gap, we propose a new mathematical formulation of the coverage planning problem that includes RISs. Using well-established planning methods, we have developed a new optimization model where RISs can be installed alongside base stations to assist the communications, creating what we have defined as Smart Radio Connections. Our simulation campaigns show that RISs effectively increase both throughput and coverage of access networks, while further numerical results highlight additional benefits that the simplified scenarios analyzed by previous works could not reveal.

One of the disruptive innovations introduced by 5G networks is the opportunity for a new group of stakeholders to be actively involved in the management of network slices with the role of tenants. This allows to go beyond the user-centric QoS paradigm of 4G, and to include tools for handling the aggregate performance of multiple services and user groups and to focus on slice resource management, also at the new 5G NR interface. So far, research efforts have privileged a first solution based on the concept of isolation between slices. However, proposed solutions are not particularly efficient due to the loss of pooling gains, and not very reliable due to variable channel conditions that with slice limited resources make performance not easily predictable. We propose a slice management framework where the shared resources are negotiated by tenants in a real-time market based on slice instantaneous demands. Our model, based on game theory, allows tenants to optimize their service strategies acquiring resources when and where it is necessary, according to the level of quality and reliability requested by the specific traffic types they handle. In this paper, we focus on modeling the game theoretical framework and on characterizing its equilibria in a multi-tenant scenario.