Juan Antonio Romo

Bio: Juan Antonio Romo is an academic researcher from University of the Basque Country. The author has contributed to research in topics: Radar & GSM. The author has an hindex of 3, co-authored 18 publications receiving 34 citations.

Application of neural networks to field strength prediction for indoor environments

Abstract: The following paper presents an application software for field strength prediction for mobile communication networks inside buildings. The considered technique is based on a proposal that tried to benefit from the advantages of using "dominant paths" (an alternative to ray tracing), and neural networks, trained with measurements. Some graphic and numeric results are presented, but the paper is mainly focused on studying the influences that the variation of some parameters (and consideration of new ones) have on processing time of the training phase for the neural network, and the accuracy of the results.

GSM/GPRS signal strength measurements in aircraft flights under 3,000 meters of altitude

Abstract: Nowadays an increasing demand to use mobile telephones devices in aircraft flights is being acknowledged, both in commercial and in aviation general flights. 2G and 3G mobile communications networks have a great penetration in terrestrial surface of populated areas. Nevertheless land mobile networks have not been planned to operate within the air space. The main objective of this project has been to collect GSM/GPRS signal level measurement samples in the air space used by general aviation, transmitted by terrestrial base stations. Subsequently the values of obtained signal have been analyzed in order to extract conclusions on the applicability of the current mobile terrestrial communications on board of aircraft in general aviation.

Characterization of Maximum Radar Reflectivity Height During Stratiform Rain Events

Abstract: The effects of rain on terrestrial and satellite communication systems, especially at frequencies above 10 GHz, have been statistically dealt with at length. It is also well known that rain height plays an important role in signal fading and co-channel interference due to scattering. The rain height is directly related to the 0°C isotherm. At this height hydrometeors change from solid to liquid state in the melting layer, increasing their reflectivity and causing the bright band effect in radar measurements. The bright band is defined by the top, bottom and maximum reflectivity heights. The peak reflectivity height can be obtained from radar volume scans. This paper presents a statistical characterization of the time and space variability of the maximum reflectivity height during stratiform rain events. This paper also focuses on the dependence between simultaneous meteorological parameters derived from weather radar and from surface automatic weather observation stations at local scale in a temperate climate region. The data used in this study was obtained from 2006 to 2011.

Estimation of 0°C isotherm height from vertical profiles of reflectivity in raining earth-space paths

Abstract: In order to evaluate precisely the additional rain loss due to precipitation in the design process of Earth-Space telecommunication systems, it is necessary to obtain the effective rain length with the major possible resolution. The aim of the study is the precise determination of 0°C isotherm height and the boundaries of the bright band in rain situations, based on meteorological radar measurements. In order to identify data affected by bright band in weather radars, as well as to determine bright band height, some algorithms have been applied for the previously characterized Vertical Profiles of Horizontal and Differential Reflectivity.

Improvement of Outdoor Signal Strength Prediction in UHF Band by Artificial Neural Network

Abstract: Starting from measurements performed at 1140 MHz in an urban environment (Rio de Janeiro, Brazil), the signal strength measured is compared with the usual predictions calculated using the methods of International Telecommunication Union-Radiocommunication (ITU-R) Rec. 526-11/Cascade Knife Edge, and ITU-R Rec. 526-12/Delta-Bullington. The first model presented better adjustment to the data and, by using artificial neural networks (ANNs), this paper deals with the signal coverage prediction in a mobile channel calculated by the ITU-R model, which is used to train the ANN in order to reduce the average deviations. This hybrid application (ITU-R/ANN) showed efficiency, resulting in higher accuracy for the signal strength prediction, and an average improvement of 8 dB was achieved.

Artificial Intelligence Enabled Radio Propagation for Communications—Part II: Scenario Identification and Channel Modeling

Abstract: This two-part paper investigates the application of artificial intelligence (AI) and, in particular, machine learning (ML) to the study of wireless propagation channels. In Part I of this article, we introduced AI and ML and provided a comprehensive survey on ML-enabled channel characterization and antenna-channel optimization, and in this part (Part II), we review the state-of-the-art literature on scenario identification and channel modeling here. In particular, the key ideas of ML for scenario identification and channel modeling/prediction are presented, and the widely used ML methods for propagation scenario identification and channel modeling and prediction are analyzed and compared. Based on the state of the art, the future challenges of AI-/ML-based channel data processing techniques are given as well.

Influence of Training Set Selection in Artificial Neural Network-Based Propagation Path Loss Predictions

Abstract: This paper analyzes the use of artificial neural networks (ANNs) for predicting the received power/path loss in both outdoor and indoor links. The approach followed has been a combined use of ANNs and ray-tracing, the latter allowing the identification and parameterization of the so-called dominant path. A complete description of the process for creating and training an ANN-based model is presented with special emphasis on the training process. More specifically, we will be discussing various techniques to arrive at valid predictions focusing on an optimum selection of the training set. A quantitative analysis based on results from two narrowband measurement campaigns, one outdoors and the other indoors, is also presented.

Holes-in-the-Sky: A field study on cellular-connected UAS

Abstract: Small unmanned aerial systems (UAS) require constant, safety-critical connectivity for telemetry, command-and- control, and collision avoidance. Today, dedicated, short-range pilot-to-aircraft links provide this connectivity for UAS operation. For UAS operating in fleets and beyond line-of-sight, a robust multi-transmitter network to provide connectivity over a wide area will be needed. However, networks that could serve this purpose, such as the ubiquitous broadband cellular networks, were planned and deployed for terminals on the ground. Hardening multi-transmitter networks for aerial use remains an open problem. In this paper, we demonstrate through field measurement that a typical cellular deployment could result in low-coverage areas for UAS — what we call the “hole-in-the-sky” phenomenon. Furthermore, many of the propagation models and assumptions commonly used in terrestrial network planning fail to accurately predict aerial signal strength. From first principles, we identify and model the predominant contributors to path loss, and form a combined propagation model that more accurately reflects reality for the tested scenarios. Motivated by this study, we identify a new research direction towards avoiding holes-in-the-sky during flight.