Radio Wave Propagation Model Research Articles

Use of wireless technologies in IoT projects

When it comes to creating projects based on the use of the Internet of Things (IoT), wireless sensor networks are often used. Edge computing in IoT technology reduces system response delays to sensor output signals and increases the network throughput. At the same time, a short-range sensor network can work locally without access to the Internet, while long-range networks, as a rule, require access to the Internet and use both edge and cloud computing. The paper shows the features of the application of both peripheral and cloud computing in the practical implementation of IoT projects with various methods of wireless data transmission. Specific examples show the possibility of data transmission over a short distance using ESP-NOW technology, nRF24L01 radio modules and creating a local Wi-Fi access point. The range of sensor data transmission between microcontrollers is practically determined for each proposed option. The calculation of the range of the LoRa radio line is carried out for the actual sensitivity values of the receiver. The Okamura-Hata radio wave propagation model is proposed to estimate the LoRa radio line’s total loss. The operational range of LoRa modules with different types of signal modulation is practically determined. The essence of edge computing combined with digital and analogue sensors is shown. Peculiarities of peripheral computing during the implementation of IoT projects for remote control of blood saturation of patients using wireless technologies are considered. An example of data transmission of the air quality control system through a gateway based on an ESP8266 microcontroller with a graphical display of measurements in the IoT cloud service ThingSpeak and Blynk was provided.

The extraction method used for English–Chinese machine translation corpus based on bilingual sentence pair coverage

Abstract To improve the effect of corpus extraction in bilingual English–Chinese machine translation (ECMT), this article combines a machine learning algorithm with the ECMT corpus extraction method based on bilingual sentence pair coverage. Aiming at the propagation characteristics of medium- and short-range speeches, and employing multi-band speech waveforms in the troposphere, the actual requirements of remote machine translation are combined with the free-space, 1546, and the dual-path models, respectively, to analyze three typical air-to-ground wave propagation models. A simulation is conducted for both predictions and comparisons. In addition, a radio wave propagation model within the line of sight in the troposphere is established. The results suggest that the improved dual-path model is consistent with the propagation characteristics of radio waves in the troposphere. Moreover, the experiments show that the proposed ECMT corpus extraction method based on bilingual sentence pair coverage can play an important role in more accurate English–Chinese translations.

МЕТОДИКА РОЗРАХУНКУ ЗОН ПОКРИТТЯ ЦИФРОВОГО ТЕЛЕВІЗІЙНОГО МОВЛЕННЯ

The introduction of DVB-T2 digital television broadcasting in the VHF waveband is currently underway, and test trials have already been conducted in some regions of Ukraine. To assess the effectiveness of the projects, it is of particular interest to compare the radio coverage areas of digital TV transmitters operating in the VHF and UHF wave bands. This requires extensive calculations for specific locations. For comparison, the city of Kyiv was chosen, where there is one multiplex MX-7 (8 TV channels) in the VHF band, and the following four multiplexes are represented in the UHF band: MX 1 (26 TV channels) and MX 3 (49 TV channels), which operate at the lowest and highest frequencies of the band. In this article, to predict the coverage areas of digital television broadcasting transmitters, the justification of the selected Longley-Rice radio wave propagation model is provided, which allows calculating the radio signal attenuation compared to free space propagation using terrain profiles (digital elevation maps) and the electrical characteristics of the Earth's surface, Taking into account the main mechanisms of radio wave propagation (interference on the line of sight, diffraction on irregular terrain, tropospheric scattering), it is more accurate and its results are closer to actual measurements. The calculations will be carried out using Radio Mobile software based on the Longley-Rice Irregular Terrain Model (ITM) and using Terrain Mission SRTM satellite maps with the possibility of further adding map data and roads from various sources. Based on the ITU R Recommendations on the design of DVB-T2 digital television broadcasting networks, the minimum required field strength to ensure reception with a given quality was calculated and the initial data for predicting the coverage areas of DVB-T2 digital television transmitters were prepared using the example of Kyiv. The proposed methodology and the data obtained will allow to calculate the boundaries of the coverage areas of DVB-T2 digital standard television transmitters in the VHF and UHF wave bands for selected television channels

A Radio Wave Propagation Modeling Method Based on High-Precision 3-D Mapping in Urban Scenarios

A Radio Wave Propagation Modeling Method Based on High-Precision 3-D Mapping in Urban Scenarios

Evaluation of rainfall rates and rain-induced signal attenuation for satellite communication in the South-South Region of Nigeria

Rain fade causes signal attenuation which can relatively results in signal quality performance degradation, increased pathloss and coverage area reduction in satellite communication networks, most especially in the tropics and at higher operational frequencies above 10GHz. Analysis of rain attenuation provides useful insight for satellite communication engineers for efficient network planning and design within the region of study. Hence this paper presents threeyear analysis of signal-induced attenuation in the South-South States of Nigeria for the cities of Port-Harcourt, Uyo, Calabar and Eket, whose terrain characteristics could be generalized to represent urban, sub-urban and rural terrains in Nigeria. The International Telecommunications Union Radio-wave propagation (ITU-R P618-13) model was used to estimate the long-term rain attenuation at 10GHz, 20GHz and 30GHz for satellite communication applications. Results obtained at 10GHz, 20GHz and 30GHz operational frequencies show that at high frequency, rain fade can cause signal attenuation levels in the south-south region of Nigeria. In all, Calabar recorded the highest rain rate and corresponding highest attenuation level of 96.13dB at 30GHz compared to 50.29dB at 20GHz for the period under review.

Comparative Study of VPE-Driven CNN Models for Radio Wave Propagation Modeling in Tunnels

Comparative Study of VPE-Driven CNN Models for Radio Wave Propagation Modeling in Tunnels

Two‐Component Phase Scintillation Spectra in the Auroral Region: Observations and Model

AbstractThe random amplitude and phase fluctuations observed in trans‐ionospheric radio signals are caused by the presence of electron density irregularities in the ionosphere. Ground‐based measurements of radio wave signals provide information about the medium through which these signals propagate. The Canadian High Arctic Ionospheric Network (CHAIN) Global Position System (GPS) receivers record radio signals emitted by the GPS satellites, enabling the study of their spectral characteristics. This study presents examples of phase spectra with two power‐law components. These components exhibit steeper spectral slopes at higher frequencies and shallower ones at lower frequencies. To be more specific, we conducted a spectral characterization of sixty one (61) events recorded by the CHAIN Churchill GPS receiver, which is located in the auroral oval. When fluctuations above the background level are only observed in the phase, the spectra tend to be systematically steeper. Conversely, the power increase in higher frequency fluctuations accompanying amplitude scintillation tends to result in shallower spectra. A basic yet powerful model of radio wave propagation through a turbulent ionosphere, characterized by a power law electron density spectrum, can help to explain the two power laws observed in the scintillation events presented in this study by identifying the role played by small‐scale ionospheric irregularities in diffraction.

Is the phone mightier than the virus? Cellphone access and epidemic containment efforts

This paper examines the impact of cellphone access on the containment of an epidemic. We study this question in the context of the 2014 Ebola Virus Disease (EVD) outbreak in Liberia. Combining novel data on cellphone towers and EVD cases, we estimate a high-resolution radio-wave propagation model that uses variations in terrain topography and the spatial distribution of cellphone towers to predict signal strength on the ground. We then employ a regression discontinuity design that compares villages at the margin of the signal strength threshold required for coverage. We find that having access to cellphone coverage leads to a 10.8 percentage point reduction in the likelihood that a village has an EVD case. Results from a novel survey collected following the epidemic suggest that this may be explained by cellphone access facilitating treatment provision. However, we cannot fully exclude that improving access to preventive care or information could have also contributed to containment.

Comparative analysis of finite‐difference and split‐step based parabolic equation methods for tunnel propagation modelling

AbstractRadio wave propagation modelling in railway environments is of fundamental importance in designing reliable train communication systems. Parabolic equation (PE) methods have been widely applied to the modelling of wave propagation in tunnels due to their high computational efficiency and fidelity. The finite‐difference parabolic equation (FDPE) and the split‐step parabolic equation (SSPE) methods are two commonly used approaches to solve PE numerically. However, the relevant literature is still missing a comprehensive study of their performance, including the selection of parameters such as discretisation steps and the tradeoffs involved in terms of their accuracy and efficiency, especially as current wireless systems shift to high frequencies. In this study, a systematic analysis of the error and computational complexity of the FDPE and SSPE methods for radio wave propagation modelling in tunnels is provided. Guidelines for the choice of their parameters are provided, and their performance is demonstrated through both numerical examples and experimental measurements in actual tunnel cases.

Satellite to buoy IoT communications in the Arctic Ocean

Internet of things (IoT), to Arctic Ocean areas using Low Earth Orbit (LEO) satellites for communication to and from sensor units on the sea, has become increasingly important. It is challenging to close the link budget from a buoy on sea to a LEO satellite due to restrictions on the availability of power in such installations. Phenomena as scattering from the sea surface and small-scale fading, ionospheric scintillations, diffraction loss at low elevation angles, and power availability in small LEO satellites and models for analysis are described. It is of great importance to have a radio wave propagation model that accurately predicts path loss between an installation on the sea and a LEO satellite taking all loss mechanisms into account. In this paper, a path-loss model for satellite to buoy communications over the sea is described. In this model, maritime propagation phenomena in the radio link include free space loss, scintillation loss caused by the ionosphere, diffraction loss at low elevation angle, and scattering at the sea surface depending on the wave height and small-scale fading. Furthermore, a buoy on the sea surface with strong angular movement will cause a varying receive signal level depending on the antenna diagram. These phenomena are assessed. A link budget for the frequencies 433, 868, and 3,400 MHz is calculated for a LEO satellite at a height of 800 km.

Generalisable convolutional neural network model for radio wave propagation in tunnels

AbstractPropagation models are essential for the prediction of received signal strength and the planning of wireless systems in a given environment. The vector parabolic equation (VPE) method has been widely applied to the modelling of radio wave propagation in tunnels. However, carrying out simulations for large‐scale environments is still computationally expensive. A convolutional neural network (CNN)‐based propagation model, which can provide high‐fidelity received signal strength prediction based on results from low‐cost VPE simulations, is proposed. A thorough study of the generalisability, including both interpolation and extrapolation capabilities, of the proposed CNN model is conducted. It is found that the proposed model can achieve significant computational savings while maintaining acceptable accuracy, and its performance is validated in both simulations and actual tunnel cases.

A Comprehensive Prediction Model for VHF Radio Wave Propagation by Integrating Entropy Weight Theory and Machine Learning Methods

In order to improve the prediction accuracy and robustness of radio wave propagation in the very high-frequency (VHF) band, we proposed a combination prediction model (hereinafter referred to as CPM) based on the entropy weight theory and the machine learning method. The improved entropy weight method is used to determine the initial weights of recommendation ITU-R P.1546 and ITU-R P.2001 in the combination prediction model based on the systematic analysis of the prediction performance of the two ITU-R models. Furtherly, we modeled the weight mapping using the machine learning method. The statistical comparison shows that the prediction accuracy and robustness of the proposed model are better than ITU-R P.1546 and P.2001 models, in which the root mean square error (RMSE) of path loss is reduced by 3.56dB and 7.35dB, respectively. In addition, the relative error (RE) of path loss is reduced by 0.0157 dB and 0.0255 dB, respectively, corresponding to a 40.53% and 52.62% reduction in the RE of path loss. The above research can provide basic support for the regional assimilation of radio propagation prediction methods.

The Analysis and Verification of IMT-2000 Base Station Interference Characteristics in the FAST Radio Quiet Zone

In this study, we aim to analyze the electromagnetic interference (EMI) regarding the Five-hundred-meter Aperture Spherical radio Telescope (FAST) caused by base stations in the International Mobile Telecommunications-2000 (IMT-2000) frequency band. By analyzing the frequency bands used by the transmitting and receiving devices and the surrounding environmental parameters and utilizing an approach to predicting radio wave propagation loss that is based on deterministic methods, we conclude by comparing the predicted received power at the FAST with its interference protection threshold. Our analysis demonstrates that, currently, only 55.31% of IMT-2000 base stations in the FAST radio quiet zone (RQZ) meet the protection threshold. Additionally, this article verifies the applicability and accuracy of the radio wave propagation model used in the research based on field strength measurements. Overall, this study provides valuable insights for improving the electromagnetic environment surrounding FAST and reducing the EMI caused by mobile communication base stations. It also provides corresponding analysis methods and useful suggestions for analyzing electromagnetic radiation interference in other radio telescopes.

Efficient physics‐based recurrent neural network model for radio wave propagation in tunnels at 2.4 GHz

AbstractThe vector parabolic equation (VPE) method has been widely applied to the modelling of radio wave propagation in tunnel environments. However, dense spatial discretizations are generally required for VPE simulations to achieve acceptable accuracy. This results in high computational costs for large‐size tunnels and high‐frequency wireless systems. This letter presents an efficient physics‐based recurrent neural network (RNN) model for modeling radio wave propagation in tunnels. The proposed RNN‐based model can leverage coarse‐mesh VPE to generate high‐fidelity received signal strength and achieve significant computational savings. The validity of the proposed approach is demonstrated against fine‐mesh VPE simulations in various tunnel cases.

CNN‐based received signal strength prediction: A frequency conversion scheme

AbstractTo support the design and deployment of emerging high‐frequency wireless systems in various applications such as intelligent transportation, advanced received signal strength (RSS) simulation tools for radio coverage prediction are needed. In railway transportation, the vector parabolic equation (VPE) method is a commonly used approach for radio wave propagation modelling in large guiding structures such as tunnels. However, running VPE simulations at high frequencies in such an environment is computationally expensive, as the discretization parameters are closely related to the wavelength. In this letter, the authors propose a convolutional neural network‐based model that can efficiently provide RSS prediction at high frequencies by leveraging data obtained from VPE simulations at a lower frequency as an anchor. This proposed model can significantly improve the efficiency of VPE for high‐frequency RSS predictions and has been validated against conventional VPE in various tunnel cases.

Morphological and Spectral Features of Ionospheric Structures at E- and F-Region Altitudes over Poker Flat Analyzed Using Modeling and Observations

Electron density irregularities in the ionosphere modify the phase and amplitude of trans-ionospheric radio signals. We aim to characterize the spectral and morphological features of E- and F-region ionospheric irregularities likely to produce these fluctuations or "scintillations". To characterize them, we use a three-dimensional radio wave propagation model-"Satellite-beacon Ionospheric scintillation Global Model of upper Atmosphere" (SIGMA), along with the scintillation measurements observed by a cluster of six Global Positioning System (GPS) receivers called Scintillation Auroral GPS Array (SAGA) at Poker Flat, AK. An inverse method is used to derive the parameters that describe the irregularities by estimating the best fit of model outputs to GPS observations. We analyze in detail one E-region and two F-region events during geomagnetically active times and determine the E- and F-region irregularity characteristics using two different spectral models as input to SIGMA. Our results from the spectral analysis show that the E-region irregularities are more elongated along the magnetic field lines with rod-shaped structures, while the F-region irregularities have wing-like structures with irregularities extending both along and across the magnetic field lines. We also found that the spectral index of the E-region event is less than the spectral index of the F-region events. Additionally, the spectral slope on the ground at higher frequencies is less than the spectral slope at irregularity height. This study describes distinctive morphological and spectral features of irregularities at E- and F-regions for a handful of cases performed using a full 3D propagation model coupled with GPS observations and inversion.

A Deep Learning Model of Radio Wave Propagation for Precision Agriculture and Sensor System in Greenhouses

The production of crops in greenhouses will ensure the demand for food for the world’s population in the coming decades. Precision agriculture is an important tool for this purpose, supported among other things, by the technology of wireless sensor networks (WSN) in the monitoring of agronomic parameters. Therefore, prior planning of the deployment of WSN nodes is relevant because their coverage decreases when the radio waves are attenuated by the foliage of the plantation. In that sense, the method proposed in this study applies Deep Learning to develop an empirical model of radio wave attenuation when it crosses vegetation that includes height and distance between the transceivers of the WSN nodes. The model quality is expressed via the parameters cross-validation, R2 of 0.966, while its generalized error is 0.920 verifying the reliability of the empirical model.

A Potential Game Approach to Multi-UAV Accurate Coverage Based on Deterministic Radio Wave Propagation Model in Urban Area

A Potential Game Approach to Multi-UAV Accurate Coverage Based on Deterministic Radio Wave Propagation Model in Urban Area

CONSIDERATION OF MULTIPATH IN MODELING THE FIELD OF SECONDARY ELECTROMAGNETIC RADIATION ON INFORMATIZATION OBJECTS

The paper considers the features of the formation of the resulting field of spurious electromagnetic radiation at informatization objects, taking into account the multipath propagation of radio waves, evaluates the achievable error values for determining the attenuation coefficient using known methods for determining the attenuation of the field of spurious electromagnetic radiation based on a deterministic model of radio wave propagation inside the building. The need for a significant refinement of the method for determining the attenuation coefficient is shown, since, in a known form, it is suitable only for measurements in free space and gives very significant errors in urban conditions and inside buildings, or the use of adaptive reception methods when receiving signals (for example, diversity reception), which allows to significantly reduce the level of interference dips in the resulting electromotive force.

Первый сравнительный анализ метеорного эхо и спорадического рассеяния, идентифицированных самообучившейся нейронной сетью по данным радаров EKB и MAGW ИСЗФ СО РАН

The paper describes the current version (v.1.1) of the algorithm for automatic classification of signals received by ISTP SB RAS decameter coherent scatter radars. The algorithm is a self-learning neural network that determines the type of scattered signals from the results of physical modeling of radio wave propagation, using radar data and international reference models of the ionosphere and geomagnetic field. According to MAGW and EKB ISTP SB RAS radar data for 2021, the algorithm self-learns to classify scattered signals into initially unknown classes based on physically interpreted parameters of radio wave propagation and data measured by the radar, with 15 frequently observed out of 20 possible hidden classes identified, 14 of which can be interpreted from a physical point of view. To demonstrate the operation of the algorithm, we present the first statistical analysis of observations of signals assigned by the algorithm to classes which we interpret as scattering by meteor trails and scattering with the sporadic E layer respectively. Through a statistical analysis of EKB and MAGW radar data during 2021–2022, we demonstrate the range-altitude characteristics of signals of these types. A correlation is shown between the hourly average numbers of observations of both classes, as well as between the hourly average line-of-sight velocities obtained for both classes. The results obtained make it possible to interpret these classes as a meteor echo and sporadic scattering respectively, and to use radar data to study the interaction between the neutral atmosphere (studied from meteor scattering data) and the lower ionosphere (studied from observations of sporadic scattering). Currently, this classification algorithm works in ISTP SB RAS radars in automatic mode.