German Castellanos

AI-native, programmable, and disaggregated 6G networks will be highly dynamic and distributed, demanding tools that can explain, predict, and safely optimize behavior across the edge–cloud continuum. Network Digital Twins (NDTs) promise this capability, yet current efforts in research and industry are fragmented and lack widely accepted formal definitions and architectural guidelines. This paper proposes a structured framework for NDTs in 6G, addressing these gaps by refining the conceptual foundations of NDTs, introducing a functional architecture, inherited from the 6G-TWIN EU consortium, and clarifying key components such as AI-driven workflows, the place of simulation, data management, and orchestration. Concrete examples illustrate how these components enable network automation, optimization, and predictive analytics. The paper proceeds by reviewing related work and standardization efforts, specifying functional and non-functional requirements, presenting the architecture and its various domains, and detailing lifecycle management across cloud to edge. We then report early implementations and evaluation results, and discuss security, privacy, and governance considerations, concluding with directions for validation and uptake. The key objective is to offer a cohesive reference model that guides the community in shaping NDT development, ensuring interoperability, scalability, adaptability, and seamless integration into AI-native 6G networks for improved intelligence and efficiency.

The large bandwidths that are available at millimeter-wave frequencies enable fixed wireless access (FWA) applications, in which fixed point-to-point wireless links are used to provide internet connectivity. In FWA networks, a wireless mesh is created and data are routed from the customer premises equipment (CPE) towards the point of presence (POP), which is the interface with the wired internet infrastructure. The performance of the wireless links depends on the radio propagation characteristics, as well as the wireless technology that is used. The radio propagation characteristics depend on the environment and on the considered frequency. In this work, we analyzed the network characteristics of FWA networks using radio propagation models for different wireless technologies using millimeter-wave (mmWave) frequencies of 28 GHz, 60 GHz, and 140 GHz. Different scenarios and environments were considered, and the influence of rain, vegetation, and the number of subscribers was investigated. A network planning algorithm is presented that defines a route for each CPE towards the POP based on a predefined location of customer devices and considering the available capacity of the wireless links. Rain does not have a considerable effect on the system capacity. Even though the higher frequencies exhibit a larger path loss, resulting in a lower power of the received signal, the larger bandwidths enable a higher channel capacity.

Future wireless communication networks can benefit from Unmanned Aerial Base Stations (UABSs) to provide enhanced capacity to ground users (GU) in large and remote locations. Connecting UABSs to the terrestrial network presents several challenges, such as the limited gain traditional antennas need to maintain suitable wireless links between the core network, UABS, and GU. A convenient solution is to use MaMIMO (Massive Multiple Input Multiple Output) since it improves spectral and energy efficiency, thus providing high data rates while reducing power consumption. This paper proposes a Multi-user MIMO (MuMIMO) model for UABS aided networks to increase service range and served capacity. It uses hybrid beamforming and beamsteering on Terrestrial Base Stations (TBSs) and UABSs to grant access to mobile GU in a bicycle race scenario. Results show that using the mobile operators’ locations will benefit the backhaul network’s performance by duplicating the capacity compared to using a private operator. Furthermore, user coverage increases by 400% if MuMIMO is used, compared to a single beam MaMIMO network. The proposed scenario could achieve a channel efficiency of 6.5 bit/s/Hz in the access network and 8.6 bit/s/Hz in the backhaul network. Finally, the average UABS transmitted power is reduced by 2/3, increasing the number of used beams.

The constant increase in the required user capacity and the evolution of wireless network technologies impact the exposure that users experience from wireless networks. This paper presents an evaluation of various 5G network topologies regarding human exposure, mobile communication quality, and sustainability. We evaluate human exposure, based on a novel Exposure Ratio (ER) metric, in 5G networks that include Massive Multiple-Input Multiple-Output (MaMIMO) and compare them with existing 4G deployments in three environments in Switzerland. Quality and sustainability of mobile communication is evaluated by extrapolating data rates from mobile operators to the year 2030. For designing the 5G network topologies, a multi-objective optimisation algorithm is implemented, maximising the user coverage while minimising the downlink (DL) and uplink (UL) exposure. An extensive set of simulations investigated three municipalities, three operators plus one unified network, three use cases (UL/DL data rates), three scenarios (indoor and outdoor coverage), and two optimisation methods. The study results confirm that the human exposure in a 5G network is dominated by the UL being ten times larger than the DL exposure. Furthermore, comparing a 5G deployment with 10 times the traffic capacity of a real 4G network, DL exposure increases by 36% on average and UL exposure decreases by up to 75% depending on the scenario. Regarding indoor coverage versus outdoor only, our results show that DL exposure can be reduced by a factor of 10 if only outdoor coverage is targeted. Finally, the study concludes that the ideal network, from the human exposure perspective, should use 5G MaMIMO and be optimised for both UL and DL exposure.

Unmanned Aerial Vehicles (UAV) are part of precision agriculture; also, their impact on fast deployable wireless communication is offering new solutions and systems never envisioned before such as collecting information from underground sensors by using low power Internet of Things (IoT) technologies. In this paper, we propose a (Narrow Band IoT) NB-IoT system for collecting underground soil parameters in potato crops using a UAV-aided network. To this end, a simulation tool implementing a gateway mounted on a UAV using NB-IoT based access network and LTE based backhaul network is developed. This tool evaluates the performance of a realistic scenario in a potato field near Bogota, Colombia, accounting for real size packets in a complete IoT application. While computing the wireless link quality, it allocates access and backhaul resources simultaneously based on the technologies used. We compare the performance of wireless underground sensors buried in dry and wet soils at four different depths. Results show that a single drone with 50 seconds of flight time could satisfy more than 2000 sensors deployed in a 20 hectares field, depending on the buried depth and soil characteristics. We found that an optimal flight altitude is located between 60 m and 80 m for buried sensors. Moreover, we establish that the water content reduces the maximum reachable buried depth from 70 cm in dry soils, down to 30 cm in wet ones. Besides, we found that in the proposed scenario, sensors' battery life could last up to 82 months for above ground sensors and 77 months for the deepest buried ones. Finally, we discuss the influence of the sensor's density and buried depth, the flight service time and altitude in power-constrained conditions and we propose optimal configuration to improve system performance.

Today’s wireless networks provide us reliable connectivity. However, if a disaster occurs, the whole network could be out of service and people cannot communicate. Using a fast deployable temporally network by mounting small cell base stations on unmanned aerial vehicles (UAVs) could solve the problem. Yet, this raises several challenges. We propose a capacity-deployment tool to design the backhaul network for UAV-aided networks and to evaluate the performance of the backhaul network in a realistic scenario in the city center of Ghent, Belgium. This tool assigns simultaneously resources to the ground users—access network—and to the backhaul network, taking into consideration backhaul capacity and power restrictions. We compare three types of backhaul scenarios using a 3.5 GHz link, 3.5 GHz with carrier aggregation (CA) and the 60 GHz band, considering three different types of drones. The results showed that an optimal UAV flight height (80 m) could satisfy both access and backhaul networks; however, full coverage was difficult to achieve. Finally, we discuss the influence of the flight height and the number of requesting users concerning the network performance and propose an optimal configuration and new mechanisms to improve the network capacity, based on realistic restrictions.