Radio Propagation Predictions Research Articles

A Comprehensive Review of Radio Signal Propagation Prediction for Terrestrial Wireless Communication Systems

The subject of study known as radio propagation prediction refers to predicting the behaviour and characteristics of radio waves as they propagate through the atmosphere. It is a basic element of all wireless communication systems, including satellite communications, broadcasting and cellular networks. While there is no study covers all the techniques used to predict the radio signal prediction, this study presents a review of the propagation prediction models between 2018-2023 used for terrestrial wireless communication systems; the classic empirical models were briefly explained, followed by the deterministic propagation models that have been developed using ray-tracing with deep and machine learning techniques. Recent studies on an improvement of the computational efficiency and accuracy of propagation prediction models were also reviewed, in addition to an overview of some of the traditional statistical models. Furthermore, some of the new techniques in propagation prediction were described. The results of these studies explain that techniques using ray tracing produce better results than other techniques.

Efficient propagation modelling for open‐confined mixed space using a hybrid ray‐tracing/waveguide theory method

AbstractThe accurate prediction of radio wave propagation along highways plays an important role in the design of high‐speed highway communication systems. The open‐confined mixed space is a connection zone between the open space and confined tunnel along the highway. A hybrid propagation model is presented based on ray‐tracing (RT) and waveguide theory, for the efficient modelling of open‐confined mixed space. The proposed model employs RT to predict radio waves emitted by sources with arbitrary radiation patterns in open space sections, and utilises the waveguide theory approach to accelerate the prediction process in the confined tunnel sections. Both two‐ray and six‐ray RT models are included to take into account the suburban and urban environments. Numerical results are compared with simulated results generated by RT as well as experimental measurements. The validity and usefulness of the proposed model are demonstrated in an actual open‐confined mixed space environment.

Three-Dimensional Ray-Tracing-Based Propagation Prediction Model for Macrocellular Environment at Sub-6 GHz Frequencies

This paper presents a 3D ray-tracing model using geometrical optics and the uniform theory of diffraction in radio channel characterizations of macrocellular environments. On the basis of the environmental information obtained from a digitized map, the model is effectively applied. A technique considering multiple reflections and diffractions through the ray path classification is utilized in this model. Ray paths belonging to each ray category are determined using different methods. The proposed model is justified (the prediction accuracy of the model is better than 6.5 dB) with measurement data for the two scenarios and can provide reliable theory as a basis for radio wave propagation prediction and network planning in urban macrocellular environments.

An SVR-Based Radio Propagation Prediction Model for Terrestrial FM Broadcasting Services in Beijing and Its Surrounding Area

An SVR-Based Radio Propagation Prediction Model for Terrestrial FM Broadcasting Services in Beijing and Its Surrounding Area

Path Clustering for Radio Propagation Prediction With Moving Object in Indoor Environment

Path Clustering for Radio Propagation Prediction With Moving Object in Indoor Environment

Comparative Analysis for Radio Channel Propagation Models in the City of Tripoli/ Libya for 4G/LTE Networks

The accurate prediction of radio channel propagation is one of the most important factors for the design and optimization of wireless communication systems. Path loss is the key element to design appropriate propagation model. There are different propagation models used to predict path loss, however, inaccurate propagation model may result in unsatisfactory services for the Global System for Mobile communications (GSM) network. These include low data rates, high co-channel interference, and wasted received power.This paper presents a comparative analysis of radio channel propagation models specifically for urban environment. The aim is to evaluate and compare the performance of various models and accurately predict path loss and signal behavior in urban settings. The research focuses on adapting the Okumura-Hata model which is used by the Libya Telecom and Technology Company (LTT) for Long Term Evolution (LTE) technology in the GSM Band. This study is based on real time measurements collected in downtown Tripoli in Libya. It suggests considering other models, such as Standard Propagation Model (SPM), Cost-231, and Ericsson. Using MATLAB for simulation, the findings indicate that SPM model is the best fit especially when distance is over ~22 Km.

The evaluation of EMI characteristics of 4G-IMT system on FAST around its RQZ

Mobile communication base stations have become ubiquitous with the popularization and development of mobile communication services. Although the Chinese government has designated a radio quiet zone (RQZ) specifically for Five-hundred-meter Aperture Spherical radio Telescope (FAST), the 4G International Mobile Telecommunications (4G-IMT) system is still heavily used to meet the communication needs around the RQZ. To assess the Electromagnetic Interference (EMI) caused by 4G-IMT base stations on the performance of FAST, this paper employs the International Telecommunication Union (ITU) deterministic radio wave propagation prediction model. The interference of 4G-IMT base stations on FAST is evaluated based on relevant parameters, such as the frequency used by both FAST and the base station, in conjunction with the FAST threshold protection requirements. The results indicate that 47.73 % of the data exceed the radio astronomy protection threshold of FAST. In addition, a field experiment was conducted to verify the accuracy of the radio wave propagation prediction method used. The root-mean-square error between the predicted value and the measured value was found to be 6.94 dB, supporting the accuracy of the predicted results. The findings of this study can help suppress interference from mobile communication services around the RQZ of FAST and optimize the electromagnetic environment around FAST.

Prediction of indoor radio propagation using deep neural network

Prediction of indoor radio propagation using deep neural network

An Entropy Weight‐Based Method for Path Loss Predictions for Terrestrial Services in the VHF and UHF Bands

AbstractVHF and UHF are widely used frequency bands, and accurate path loss prediction in these bands is of great significance. In order to improve the prediction accuracy and stability of radio wave propagation, we propose a combined modeling method based on the entropy weight method. This method assigns appropriate weights to the predicted values of sub‐models using entropy, and calculates the final predicted value through weighted aggregation. To validate the feasibility of our proposed method, we conduct modeling using data from Denver, Colorado. We compare our method with the ITU‐R models and a model established using the coefficient of variation method. The results demonstrate that our proposed model outperforms the compared models. Specifically, the root‐mean‐square error (RMSE) of our proposed model is reduced by 5.94 and 5.51 dB compared to the ITU‐R P.1546 and 2001 models, respectively. Moreover, our proposed model exhibits better stability than the ITU‐R models. We also use the improvement percentage (γ) to evaluate the degree of improvement of our model over the sub‐models, which is 49.13% and 47.86% for the ITU‐R P.1546 and 2001 models, respectively. Additionally, by comparing the RMSE and absolute error, we find that the entropy weight‐based method outperforms the coefficient of variation‐based method. This research demonstrates the superiority of the entropy weight‐based modeling method, providing a valuable channel modeling approach for path loss prediction and supporting the regional assimilation of radio propagation prediction methods.

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.

Preliminary Investigation of the Potentialities of a Mesoscale Meteorological Model to Reproduce Experimental Statistics of Rain Attenuation on Earth-Space Links

Current spatial resolutions achieved by mesoscale weather forecast models allow them to be used to generate the state of the lowest layers of the atmosphere over areas as small as a few square kilometers which corresponds to the typical size of the tropospheric area crossed by Earth-space links. Furthermore, they allow the evolution of the troposphere to be predicted with a time stamp of five minutes instead of every hour with large-scale weather forecast models which makes them attractive for radio propagation predictions for satellite communication applications. This paper aims at studying the capability of the Weather Research and Forecast (WRF) model coupled with an electromagnetic physical model to reproduce rain attenuation statistics for Earth-space paths at Ka-band. To this purpose, one year of propagation measurements collected at 20 GHz in different places at midlatitudes in Toulouse and Salon de Provence (France), Spino d’Adda (Italy), Aveiro (Portugal), and Madrid (Spain), at high latitudes in Svalbard (Norway) and at low latitudes in Kourou are used to make comparisons between simulations and measurements. Comparisons between the simulated and the experimental annual statistics considered in this paper provide encouraging results, with a similar accuracy as Recommendation ITU-R P.618–13 for midlatitude European locations and with better accuracy for a high latitude area in Svalbard and for an equatorial location in French Guiana.

Field strength prediction based on deep learning under small sample data

The accurate prediction of radio wave propagation is extremely important for wireless network planning and optimization. However, inexact matching between the traditional empirical model and actual propagation environments, as well as the insufficiency of the sample data required for training a deep learning model, lead to unsatisfactory prediction results. This paper proposes a field strength prediction model based on a deep neural network that is aimed at a tiny dataset composed of the geographic information and corresponding satellite images of a target area. This model connects two pretrained networks to minimize the parameters to be learned. Simultaneously, a convolutional neural network (CNN) model is constructed for comparison based on a previous advanced study in this field. Experimental results show that the proposed model can obtain the same accuracy as that of previously developed CNN models while requiring less data.

An Accurate Maritime Radio Propagation Loss Prediction Approach Employing Neural Networks

The radio propagation loss prediction model is essential for maritime communication. The oceanic tropospheric duct is much more complicated than the atmospheric structure on land due to the rough sea surface influence, and it leads to difficulties in loss prediction. Classical radio wave propagation loss prediction models are either based on complicated electromagnetic wave theories or rely on empirical data. Consequently, they suffer from low accuracy and a limited range of application. To address this issue, a novel maritime propagation loss prediction approach is proposed, which fully exploits the data for training. In this new approach, 3D sea surface contour profiles are generated based on the Pierson–Moskowitz spectrum theory and the direction expansion formula Stereo Wave Observation Program (SWOP), by sweeping the parameters. The full-wave propagation procedure of radio signals over the sea surface is simulated by commercial EM analysis software CST Studio Suite. Based on the large quantity of simulated data, the BP neural network is employed to fit the radio propagation loss and obtain the sea surface radio wave propagation prediction model. Other classical machine learning methods are also compared to validate the proposed approach. Traditional empirical model construction relies on observation data. This approach, for the first time, proposed an automatic scheme which covers the whole procedure from the data generation to prediction model training. It avoids the requirements for on-site observation, as well as significantly decreases the cost of experiments. The application scope of the prediction model such as propagation distance range and working frequency range could be adaptive through adjustments for simulated sea size and simulated working frequency. This approach is validated to save 99% of prediction time in comparison with the full-wave simulations. The prediction model trained via our proposed method could obtain the coefficient of determination R2 which is over 0.92, demonstrating the superiority of this method.

3D RT adaptive path sensing Method: RSSI modelling validation at 4.5 GHz, 28 GHz, and 38 GHz

This paper explains a new Adaptive Path Sensing Method (APSM) for indoor radio wave propagation prediction. Measurement campaigns, which cover indoor line-of-sight (LoS), non-line-of-sight (NLoS) and different room scenarios, are conducted at the new Wireless Communication Centre (WCC) block P15a) of Universiti Teknologi Malaysia (UTM), Johor, Malaysia. The proposed APSM is evaluated through a computerized modelling tool by comparing the Received Signal Strength Indicator (RSSI) with measurement data and the conventional Shooting-Bouncing Ray Tracing (SBRT) method. Simulations of the APSM and SBRT are performed with the same layout of the new WCC block P15a by using the exact building dimensions. The results demonstrate that the proposed method achieves a better agreement with measured data, compared to the conventional SBRT outputs. The reduced computational time and resources required are also important milestones to ray tracing technology. The proposed APSM method can assist engineers and researchers to reduce the time required in modelling and optimizing reliable radio propagation in an indoor environment.

Narrow-Band Radio Propagation Prediction Based on a Highly Accurate Three-Dimensional Railway Environment Model

In this paper, radio propagation predictions were performed by a self-developed ray tracing simulator based on highly accurate three-dimensional (3D) environment models obtained by oblique aerial photography (OAP). In order to support ray tracing computations, the highly complex environment models were managed by three steps, including model separation, electromagnetic parameter assignment, and model lightweighting. Simulations and real-world experiments were conducted to test the proposed method. The results show that the root mean square errors (RMSE) in the predicted received signal strength curves were achieved 4 dB with comparably low computational complexity in the selected railway scenarios.

Performance Analysis of Propagation in VHF Military Tactical Communication System

The main challenge of military tactical communication systems is the accessibility of relevant information on the particular operating environment required for the determination of the waveform's ideal use. The existing propagation model focuses mainly on broadcasting and commercial wireless communication with a highs transceiver antenna that is not suitable for numerous military tactical communication systems. This paper presents a study of the path loss model related to radio propagation profile within the suburban in Kuala Lumpur. The experimental path loss modeling for VHF propagation was collected from various suburban settings for the 30-88 MHz frequency range. This experiment was highly affected by ecological factors and existing wave propagation effects such as reflection, diffraction, scattering, and Doppler effect. Radio propagation performance is evaluated by collecting received power at the allocated substation and comparing it against existing propagation models. The existing propagation model also will be tuned close to the measurement value by identifying the best path loss exponent to perform a suitable model for a suburban area. Theoretical assessments and analysis of the initial measurement stage for radio propagation show the extensive contribution of radio field from potential obstacles at lower VHF frequencies for both short and medium ranges around there. The explanation indicates the standard radio propagation prediction models that are generally reasonable for the suburban area. From the general error analysis, it is seen that, the performance of the LDPL with adjusting path loss exponent is the suitable model since it has least value of error metrics.

3D shooting and bouncing ray approach using an artificial intelligence-based acceleration technique for radio propagation prediction in indoor environments

The shooting and bouncing ray approach is an important method used in ray-tracing simulations to estimate the propagation of radio waves. However, in this method, unnecessary rays, which are useless for follow-up and consume a large amount of simulation resources, may emerge. This study proposes an artificial intelligence-based acceleration solution, unlike geometry-based approaches in the literature. The proposed approach focuses on ray characteristics, not ambient geometry. An analysis of ray characteristics is carried out by trained simulation-decision engines at run-time. The decision engine training was carried out with datasets obtained from traditional shooting and bouncing simulations that use different parameters. The success of the proposed method was tested for three different real environments containing line-of-sight and non-line-of-sight receiver points, where the received signal strength measurements had been made earlier. For better training and more detailed analysis, a total of 63 simulations using different parameters were made in both training data and test simulations. The obtained results show the success of the proposed method.

Large-scale radio propagation path loss measurements and predictions in the VHF and UHF bands

For decades now, a lot of radio wave path loss propagation models have been developed for predictions across different environmental terrains. Amongst these models, empirical models are practically the most popular due to their ease of application. However, their prediction accuracies are not as high as required. Therefore, extensive path loss measurement data are needed to develop novel measurement-oriented path loss models with suitable correction factors for varied frequency, capturing both local terrain and clutter information, this have been found to be relatively expensive. In this paper, a large-scale radio propagation path loss measurement campaign was conducted across the VHF and UHF frequencies. A multi-transmitter propagation set-up was employed to measure the strengths of radio signals from seven broadcasting transmitters (operating at 89.30, 103.5, 203.25, 479.25, 615.25, 559.25 and 695.25 MHz respectively) at various locations covering a distance of 145.5 km within Nigerian urban environments. The measurement procedure deployed ensured that the data obtained strictly reflect the shadowing effects on radio signal propagation by filtering out the small-scale fading components. The paper also, examines the feasibilities of applying Kriging method to predict distanced-based path losses in the VHF and UHF bands. This method was introduced to minimize the cost of measurements, analysis and predictions of path losses in built-up propagation environments.

Prediction method of SSPE propagation based on improved terrain reconstruction

In view of the difficulty of wave propagation prediction and analysis in complex environment, a propagation prediction method of split-step parabolic equation (SSPE) based on terrain reconstruction is proposed. This method uses a two-way SSPE solution with a wide-angle propagation factor and variable step size, and uses the SSPE propagation prediction method based on improved terrain reconstruction to simulate and test the radio wave propagation characteristics of real terrain. Compared with the existing radio wave propagation prediction method, this method combines real terrain, which improves the terrain modeling effect by improving the reconstruction algorithm, and uses variable step size two-way SSPE solution to predict the propagation loss. Simulation test results show that the prediction method is within 6 dB. The accuracy meets the requirements of a high-precision radio wave propagation model, indicating that the method has practical significance and popularization value.

Accurate prediction of radio wave propagation in an environment of dielectric ground and obstacles based on the principle of domain decomposition

To study the problem of radio wave propagation in an environment of dielectric ground and obstacles, especially in low-lossy obstacles, to consider the propagation inside the obstacle, a new method is proposed to solve the two-way parabolic equation based on the principle of domain decomposition. First, a mathematical model of parabolic equations for reflection and refraction in an environment with obstacles is established. Then, the area of the obstacle is decomposed into two different subdomains; the discrete mixed Fourier transform is used to calculate the field value in the upper subdomain from the top of the obstacle to the absorption boundary, the finite difference method is used to calculate the field value of the lower subdomain inside the obstacle, and the field strengths and phases of the two subdomains coordinate with the boundary conditions. In the process of calculation, we emphatically consider the multiple reflection and transmission paths inside obstacles to make the algorithm results more accurate. In addition, we verified the stability and convergence of the method. Finally, the simulation results show that the proposed method and the method of moments have good consistency, so the algorithm has the characteristics of strong applicability and fast calculation speed, which provide a novel and reliable method to research the wave propagation problem in a modified environment of obstacles.