Emerging domains such as wireless industrial control, vehicular communications, smart grids, and augmented reality demand low latency, high throughput, and high reliability from wireless communication systems. Unfortunately, single connectivity (SC) communications frequently fail to fulfill these stringent requirements. To address these challenges, employing a multi-connectivity (MC) solution appears to be a promising technique. In this paper, in the context of Horizon Europe COMMECT project, we seek to develop a multi-connectivity solution that intelligently integrates cellular and satellite networks for the purpose of monitoring livestock transport in rural regions where 5G coverage is limited. Multi-connectivity can be helpful for meeting EU regulations requiring seamless communication between transport units and the operational center to ensure animal welfare during transit. To achieve this, we employ machine learning (ML) models within a Classification and Regression framework in the proposed muti-connectivity solution. The ML models process radio-related key performance indicators (KPIs) as inputs to estimate network throughput and latency. The outputs of the model are used to decide whether to continue with the cellular link or activate the backup satellite link in the multi-connectivity setup, ensuring an almost uninterrupted connection. This capability is particularly crucial in regions where 5G coverage is limited, and maintaining a reliable connection is essential. To evaluate the proposed framework, we used a hybrid emulation setup based on experimental data collected in the northern part of Denmark. The emulation results demonstrate that the MC solution significantly outperforms the cellular SC. Although our solution is designed for livestock transport monitoring, it can be adapted for other applications, such as precision farming, in areas with insufficient 5G availability.

Accurate prediction of network key performance indicators (KPIs), such as throughput and latency, is important for maintaining Quality of Service (QoS) and enabling intelligent, proactive optimization in mobile applications to support reliable and ubiquitous connectivity. One application is the monitoring of livestock transport, a key focus of the Horizon Europe project COMMECT, which aims to develop a smart multi-connectivity solution to ensure seamless connectivity throughout the transport route. Although beneficial, predicting network KPIs based on radio KPIs is particularly challenging in dynamic environments due to the intricate nature of their relationship. In our previous study, this weak relationship led to limited performance in applying a supervised Random Forest (RF) model. Aiming to improve the prediction of network KPIs, we investigate the convolutional neural network-long short-term memory (CNN-LSTM) time-series model, which combines convolutional layers for spatial feature extraction with LSTM layers to learn temporal dependencies. Unlike the supervised RF model, the current approach predicts network KPIs based on past information about the network KPI itself, bypassing the need for a mapping between radio and network KPIs. In our comparison, the CNN-LSTM time series and RF models have been trained and tested based on measured 5G throughput and latency data collected in northern Denmark. The CNN-LSTM model outperforms the RF model, achieving an R2score improvement of 26.17% for latency and 15.81% for throughput, highlighting its ability to adapt to varying network conditions in a realistic setting.

Cellular mobile networks have enabled several new use cases with their rapid development, and they are now considered for mission-critical and even military applications with 5G, their latest commercially available version. However, it is of utmost importance that communication channels operate in a secure and reliable manner for these purposes. Smart jammers pose a significant threat due to their accessibility, strong potential for causing service disruptions, and their difficulty in detection. This issue can be reduced by hopping between frequencies, making it harder for a malicious actor to disrupt communication. In this study, the handover mechanism, which is a supported feature in all commercial devices according to 3GPP standards, is evaluated as a frequency hopping technique in terms of feasibility and performance at preventing the attack. The proposed method is proven effective, as it can switch frequencies every second while keeping data interruptions below 75 ms for 99% of the time.

Ensuring reliable connectivity in rural areas remains a challenge, especially for mobile applications requiring realtime data exchange. In this study, we investigate the feasibility of multi-connectivity solutions combining terrestrial and satellite networks to improve communication reliability for livestock transport trucks in Europe, addressing both short-term and mid-term requirements. We conducted real-world mobility experiments using vehicles equipped with narrowband satellite access for tracking needs, alongside broadband cellular ($\mathbf{4 G} / \mathbf{5 G}$) and satellite connectivity, simultaneously transmitting duplicated traffic to ensure seamless access for future services. Our results show that satellite-assisted duplication significantly reduces packet loss in scenarios where cellular networks are unreliable. Additionally, we analyze end-to-end latency, packet arrival times, and derive Gilbert-Elliott model parameters from real-world measurements. We conclude that while the duplication approach is highly viable, it comes with trade-offs, including increased bandwidth consumption and the need for improved congestion control mechanisms.

Although the deployment of 5G networks in urban environments is currently underway, rural areas suffer from cellular coverage limitations due to the poor deployment of 5G infrastructure. This fact directly affects use cases related to technology development in rural communities. As a result, key performance indicators may not meet minimums to satisfy a certain service, such as connectivity for livestock transport. As a strategy to improve the reliability of communications, the use of packet duplication through several cellular networks is proposed in order to increase robustness in areas with coverage issues. This study, which emulates livestock transport conditions through an experimental drive test across Denmark and Germany, demonstrates how packet duplication improves reliability for $4 \mathrm{G} / 5 \mathrm{G}$ networks concerning latency Round-Trip Time and DL/UL throughput, on routes driven by livestock transport trucks.

As new wireless standards are developed, the use of higher operation frequencies comes in hand with new use cases and propagation effects that differ from the well-established state of the art. Numerous stochastic fading models have recently emerged under the umbrella of generalized fading conditions to provide a fine-grain characterization of propagation channels in the mmWave and sub-THz bands. For the first time in literature, this work carries out an experimental validation of a class of such ray-based models in a wide range of propagation conditions (anechoic, reverberation and indoor scenarios) at mmWave bands. These models allow to characterize the communication channel with a reduced number of physically interpretable parameters. In specific, we show that the independent fluctuating two-ray (IFTR) model has good capabilities to recreate rather dissimilar environments with high accuracy and only four parameters. We also put forth that the key limitations of the IFTR model arise in the presence of reduced diffuse propagation, and also due to a limited phase variability for the dominant specular components.

Wi-Fi is one of the WLANs technologies with the highest penetration in society worldwide. In recent years, Wi-Fi 6E has been defined, which uses the 6 GHz band with higher bandwidth and resource availability. However, the range for this band decreases due to the increase in frequency compared to 2.4 GHz and 5 GHz bands, hence the proposal for the use of mesh networks to overcome the limited range. Therefore, this work empirically assesses the network performance through different setups in indoor scenarios to determine the optimal placement of extender nodes in a Wi-Fi 6E mesh network. The experimental methodology follows a dual approach, studying the channel propagation conditions in the 6 GHz band versus 2.4 GHz and 5 GHz, as well as the throughput reported through connectivity with Wi-Fi 6E. The results quantitatively show the reduction in effective network range in the 6 GHz band, due to the increase in frequency, and its influence on effects such as blocking or around-corner propagation. Based on this analysis, a set of guidelines is developed for positioning the extender nodes in the network to maximize network performance according to the propagation channel conditions.

In the context of the Horizon Europe COMMECT project, we seek to develop a multi-connectivity solution that intelligently integrates cellular and satellite networks for the purpose of monitoring livestock transport in rural regions, where 5G coverage is limited. To achieve seamless connection in the multi-connectivity solution, we use machine learning (ML) based on a Random Forest (RF) classifier to efficiently integrate 5G and satellite links. The binary output of the classifier is used to activate the satellite link, in addition to the cellular link, to ensure uninterrupted connection according to the targeted performance criteria, often necessary in rural areas with limited 5G coverage. The input to the ML model are radio-related key performance indicators (KPIs). In our emulation, using experimental data, we demonstrate that our proposed solution fulfills the seamless connectivity requirements of the COMMECT project in most cases through the combined utilization of cellular networks and satellite links. This is achievable because the RF classifier, based on pre-processed radio KPIs, can accurately predict when the cellular network is unable to provide satisfactory service with a success rate of 94.3 %. The proposed solution achieves the level of application throughput required to support monitoring of livestock transport, thereby advancing communication systems for various scenarios of limited 5G connectivity.

In this paper, we present an analytical framework for the joint characterization of the 3D direction of arrival (DoA), i.e., azimuth and elevation components, and time of arrival (ToA) in multipath environments. The analytical framework is based on the use of nearly frequency-invariant beamformers (FIB) formed by toric arrays. The frequency response of the toric array is expanded as a series of phase modes, which leads to azimuth–time and elevation–time diagrams from which the 3D DoA and the ToA of the incoming waves can be extracted over a wide bandwidth. Firstly, we discuss some practical considerations, advantages and limitations of using the analytical method. Subsequently, we perform a parametric study to analyze the influence of the method parameters on the quality of the estimation. The method is tested in single-path and multipath mm-wave environments over a large bandwidth. The results show that the proposed method improves the quality of the estimation, i.e., decreases the level of the artifacts, compared to other state-of-art FIB approaches based on the use of single/concentric circular and elliptical arrays.

This paper presents a general technique for the joint Direction-of-Arrival (DoA) and Time-of-Arrival (ToA) estimation in multipath environments. The proposed ultra-wideband technique is based on phase-mode expansions and the use of nearly frequency-invariant elliptical arrays. New possibilities open with the present approach, as not only elliptical, but also circular and linear (highly flattened) arrays can be considered with the same implementation. Systematic selection/rejection of signals-of-interest/signals-not-of-interest in smart wireless environments is possible, unlike with previous approaches based on circular arrays. Concentric elliptical arrays of many sizes and eccentricities can be jointly considered, with the subsequent improvement that entails in DoA and ToA detection. This leads to the realization of pseudo-random array patterns; namely, quasi-arbitrary geometries created from the superposition of multiple elliptical arrays. Some simulation and experimental tests (measurements in an anechoic chamber) are carried out for several frequency bands to check the correct performance of the method. The method is proven to give accurate estimations in all tested scenarios, and to be robust against noise and position uncertainty in sensor placement.