Vicent Miquel Rodrigo Penarrocha

The accurate identification of line-of-sight (LOS) and non-line-of-sight (NLOS) conditions is a critical challenge in wireless communication, particularly in the vehicle-to-vehicle (V2V) channels, owing to the high dynamics inherent to V2V environments. This study explores the use of machine learning (ML) techniques for LOS/NLOS classification based on wireless channel features extracted from both the measured received power level and the difference between the measured attenuation and free space loss (FSL). Feature correlation, importance analysis, and cumulative scoring are applied to derive a compact and informative feature set. A performance analysis with varying numbers of input features, along with an ablation study, shows that reliable classification can be maintained even with a reduced feature set. In addition, per-class evaluation is performed to capture distinct LOS and NLOS behaviors, which is particularly relevant when prioritizing specific outcomes such as minimizing false LOS or false NLOS detection. Balanced datasets were created through random sampling from the LOS and NLOS classes, with training and testing performed using K-fold cross-validation (CV). Three models, random forest (RF), support vector machines (SVM), and LogitBoost, were evaluated based on recall, specificity, precision, and the F1-score metric. The results indicate that the RF model outperforms the others and that a minimal yet balanced training dataset from both LOS and NLOS classes is sufficient to achieve high classification reliability. The proposed method offers interpretability, a low computational load framework, and flexibility addressing specific classification needs. This aspect provides valuable insights into improving the performance of vehicular communication systems.

This paper, taking as reference channel data previously obtained by using a rigorous and well-tested ray-tracing method for a concentrated massive multiple-input multiple-output (mMIMO) system, focuses on the optimization of the set of potential antennas required in a distributed mMIMO system to achieve the same channel spectral efficiency as the concentrated system. Concerning the optimizer, a binary particle swarm optimization algorithm was considered to decide whether to activate or deactivate any of the antennas within the original mesh, taking into account, in order to direct the search, the total spectral efficiency, the equality between the spectral efficiency of users, and the number of receiver antennas at the distributed base station. The analysis was carried out in a large indoor environment at the 5G n258 frequency band (26 GHz), concentrating on the up-link and considering a set of 20 uniformly distributed active users. The results obtained show that, in the distributed mMIMO system, an arrangement with fewer than half the number of receiver antennas of the initial mesh is required to achieve a similar performance to that of the concentrated one taken as a reference.

Vehicle-to-vehicle (V2V) wireless communication systems are crucial for intelligent transportation applications such as traffic load control, autonomous vehicles, and collision avoidance. Therefore, it is essential to thoroughly understand the realistic V2V channel propagation conditions to develop suitable V2V communication systems and standards. However, due to high costs, most existing V2V studies focus on characterizing the vehicular channel theoretically and comparing it with simulation data or studying the empirical V2V channel in a specific scenario. In this work, we investigate the time variability of the received signal based on experimental data collected in an extensive measurement campaign at 5.9 GHz in a large set of propagation environments: urban-high traffic density, urban-low traffic density, suburban, highway, and rural environments. The time-variability of the received signal is characterized by time and frequency. The relationship between coherence time and the terminal velocity, coherence time and transmitter-receiver separation distance, and the relationship between Doppler spread and both effective velocity and separation distance, have been investigated. Furthermore, the correlation between coherence time and separation distance and effective velocity was investigated, as well as the correlation between Doppler spread and separation distance and effective velocity. The results provide a deeper understanding of the temporal variability of the channel and how it is influenced by factors such as traffic density, unique/multiple driving directions, terminal speed, separation distance, characteristics of the propagation environment, and visibility conditions. This knowledge is crucial for developing vehicular communication systems and can be applied to design strategies that reduce temporal variability in received signals.

The development of intelligent transportation systems (ITS), vehicular ad hoc networks (VANETs), and autonomous driving (AD) has progressed rapidly in recent years, driven by artificial intelligence (AI), the internet of things (IoT), and their integration with dedicated short-range communications (DSRC) systems and fifth-generation (5G) networks. This has led to improved mobility conditions in different road propagation environments: urban, suburban, rural, and highway. The use of these communication technologies has enabled drivers and pedestrians to be more aware of the need to improve their behavior and decision making in adverse traffic conditions by sharing information from cameras, radars, and sensors widely deployed in vehicles and road infrastructure. However, wireless data transmission in VANETs is affected by the specific conditions of the propagation environment, weather, terrain, traffic density, and frequency bands used. In this paper, we characterize the path loss based on the extensive measurement campaign carrier out in vehicular environments at 700 MHz and 5.9 GHz under realistic road traffic conditions. From a linear dual-slope path loss propagation model, the results of the path loss exponents and the standard deviations of the shadowing are reported. This study focused on three different environments, i.e., urban with high traffic density (U-HD), urban with moderate/low traffic density (U-LD), and suburban (SU). The results presented here can be easily incorporated into VANET simulators to develop, evaluate, and validate new protocols and system architecture configurations under more realistic propagation conditions.

This letter presents wideband measurements and simulations ranging from 2 to 28 GHz conducted in a parking lot, with and without cars. The measurements have been carried out considering a transmitter placed in an elevated position, with an omnidirectional antenna. The receivers have been distributed in the parking area also using omnidirectional antennas. Furthermore, the OPAL open-source ray launching tool has been used to simulate the different propagation mechanisms. The CI, FI, CIF and ABC propagation models have been considered, showing a consistent behavior of the propagation path loss exponent with the frequency. The results suggest that the presence of cars has a minimal impact on the path loss along all frequencies.

In this letter, the K -factor is estimated in a typical office scenario based on wideband channel measurements carried out at millimeter-wave (mmWave) frequencies, covering the 25-40 GHz spectrum, in both line-of-sight (LOS) and obstructed-LOS (OLOS) propagation conditions. The K -factor is estimated from the method of moments, applied directly over the frequency samples of the measured complex channel transfer function (CTF), and from the power delay profile (PDP) based method. Although both methods provide similar results, the method of moments is more appropriate from a practical point of view, especially in OLOS and non-LOS (NLOS) conditions where the correct identification of dominant components can be difficult. The results are particularized to the potential 26, 28, 33 and 38 GHz frequency bands for the design and deployment of the future wireless networks at mmWave frequencies. The mean value of the K -factor ranges from -1.87 to 1.27 dB for LOS condition, and from -3.79 to -2.31 dB for OLOS condition.

The usage scenarios defined in the ITU-M2150-1 recommendation for IMT-2020 systems, including enhanced Mobile Broadband (eMBB), Ultra-reliable Low-latency Communication (URLLC), and massive Machine Type Communication (mMTC), allow the possibility of accessing different services through the set of Radio Interface Technologies (RITs), Long-term Evolution (LTE), and New Radio (NR), which are components of RIT. The potential of the low and medium frequency bands allocated by the Federal Communications Commission (FCC) for the fifth generation of mobile communications (5G) is described. In addition, in the Internet of Things (IoT) applications that will be covered by the case of use of the mMTC are framed. In this sense, a propagation channel measurement campaign was carried out at 850 MHz and 5.9 GHz in a covered corridor environment, located in an open space within the facilities of the Pedagogical and Technological University of Colombia campus. The measurements were carried out in the time domain using a channel sounder based on a Universal Software Radio Peripheral (USRP) to obtain the received signal power levels over a range of separation distances between the transmitter and receiver from 2.00 m to 67.5 m. Then, a link budget was proposed to describe the path loss behavior as a function of these distances to obtain the parameters for the close-in free space reference distance (CI) and the floating intercept (FI) path loss prediction models. These parameters were estimated from the measurements made using the Minimum Mean Square Error (MMSE) approach. The estimated path loss exponent (PLE) values for both the CI and FI path loss models at 850 MHz and 3.5 GHz are in the range of 2.21 to 2.41, respectively. This shows that the multipath effect causes a lack of constructive interference to the received power signal for this type of outdoor corridor scenario. These results can be used in simulation tools to evaluate the path loss behavior and optimize the deployment of device and sensor network infrastructure to enable 5G-IoT connectivity in smart university campus scenarios.

El crecimiento de las aplicaciones de vehículos aéreos no tripulados (UAV) es innegable, ya que sus potencialidades han aumentado significativamente. Los sistemas UAV requieren comunicaciones confiables para controlar la navegación o transmitir datos confidenciales, lo que se vuelve crucial para el desarrollo de la tecnología UAV. Los sistemas de comunicaciones utilizan el canal inalámbrico para propagar un frente de onda entre el transmisor/receptor y el vehículo que puede obstruirse o desvanecerse. El desvanecimiento es causado por fenómenos de propagación, reflexiones, difracciones, dispersión y las características del escenario de propagación produciendo efectos multicamino y Doppler, dando lugar a sistemas de desvanecimiento selectivo tiempo-frecuencia y sistemas de desvanecimiento de dispersión en tiempo-frecuencia, que afectan a la señal transmitida por el canal. Comúnmente, la obstrucción se produce por la dispersión de objetos en el suelo, como edificios, la orografía del terreno y por la propia estructura del UAV. Los modelos de propagación permiten el desarrollo y selección de los sistemas de comunicación óptimos para cada aplicación. Proponer modelos de propagación es fundamental para lograr una caracterización adecuada del canal de comunicación para comprender su comportamiento. Las caracterizaciones de canales se pueden realizar a través de análisis teóricos o con simulaciones electromagnéticas computarizadas. Sin embargo, las caracterizaciones más confiables usan medidas experimentales en el campo para validar modelos teóricos o simulados. Este trabajo muestra el avance que han tenido los modelos de pérdidas de propagación desarrollados a través de la caracterización experimental de un canal de comunicación inalámbrico entre un UAV y una infraestructura terrestre. También se muestran los trabajos de implementación de sondas de canal en bandas bajas y medias de 5G.

In this paper, a path loss characterization at millimeter-wave (mmWave) frequencies is performed in a typical indoor office environment. Path loss results were derived from propagation channel measurements collected in the 25–40 GHz frequency band, in both line-of-sight (LOS) and obstructed-LOS (OLOS) propagation conditions. The channel measurements were performed using a frequency-domain channel sounder, which integrates an amplified radio over fiber (RoF) link to avoid the high losses at mmWave. The path loss was analyzed in the 26 GHz, 28 GHz, 33 GHz and 38 GHz frequency bands through the close-in free space reference distance (CI) and the floating-intercept (FI) models. These models take into account the distance dependence of the path loss for a single frequency. Nevertheless, to jointly study the distance and frequency dependence of the path loss, multi-frequency models were considered. The parameters of the ABG (A-alpha, B-beta and G-gamma) and the close-in free space reference distance with frequency path loss exponent (CIF) models were derived from the channel measurements in the whole 25–40 GHz band under the minimum mean square error (MMSE) approach. The results show that, in general, there is some relationship between the model parameters and the frequency. Path loss exponent (PLE) values smaller than the theoretical free space propagation were obtained, showing that there are a waveguide effect and a constructive interference of multipath components (MPCs). Since the measurements were obtained in the same environment and with the same configuration and measurement setup, it is possible to establish realistic comparisons between the model parameters and the propagation behavior at the different frequencies considered. The results provided here allow us to have a better knowledge of the propagation at mmWave frequencies and may be of interest to other researchers in the simulation and performance evaluation of future wireless communication systems in indoor hotspot environments.

Metro and railway systems are one of the most used transportation systems for people in almost all countries. Nevertheless, the access to high throughput wireless services is still very limited inside the wagons (cars). A deep analysis of the wireless channel inside wagons is needed to deploy new efficient and high throughput networks as the ones provided by fifth-generation (5G) systems. Although several works have analyzed the intra-wagon channel, some limitations are usually present: only certain user equipment-access point situations were considered, the number of studied propagation mechanisms was limited, and only some channel parameters were extracted. For these reasons, in this work the wireless channel in an intra-wagon environment is thoroughly analyzed using simulations performed with a ray-tracing tool calibrated and validated with wideband measurements. Thanks to the accurate ray-tracing tool the main replicas are identified in different typical user equipment-access point positions; the contribution of each propagation mechanism to the total power is extracted; and the angular spread in azimuth and elevation for the direction of arrival and departure are obtained. This analysis is performed in the frequency range from 25 to 40 GHz, where spectrum for several 5G bands has been already allocated.