Path Loss Model Research Articles

Extended Weighted ABG: A Robust Non-Linear ABG-Based Approach for Optimal Combination of ABG Path-Loss Propagation Models

This paper proposes a robust non-linear generalized path-loss propagation model, the Extended Weighted ABG (EWABG), which efficiently allows generating a path-loss propagation model by combining several available path-loss datasets (from measurements campaigns) and other previously proposed state-of-the-art 5G path-loss propagation models. The EWABG model works by integrating individual path-loss models into one single model in the least-squares sense, allowing to extend knowledge from frequencies and distances covered by path-loss datasets or path-loss propagation models. The proposed EWABG model is the first non-linear extension of the common ABG-based approach, which surpasses the non-uniformity problem between the low and high 5G frequencies (as most measurements campaigns have taken place in low frequencies). The EWABG also addresses the problem of removing outlier measurements, a step not included in previous propagation path-loss models. In this case, we have compared the most recent techniques for avoiding outliers, and we have adopted the Theil-Sen method, due to its strong robustness demonstrated in the experiments carried out. In addition, the proposed model specifically considers non-linear attenuation by atmospheric gases, in order to improve its estimations. The good performance of the proposed EWABG model has been tested and compared against recent 5G propagation path-loss models including the ABG and WABG models. The exhaustive experimentation carried out includes the 5G non-line-of-sight environment in different 5G scenarios, UMiSC, UMiOS and UMa. The proposed EWABG obtains the best accuracy, specially in noisy environments with outliers, reporting negligible increment error rates (with respect to the non-outliers situation), lower than 1%, compared to the ABG and WABG.

An Improved Path-Loss Model for Reconfigurable-Intelligent-Surface-Aided Wireless Communications and Experimental Validation

Recently, a reconfigurable intelligent surface (RIS) that provides alternative wireless propagation paths (by reflecting an electromagnetic wave from a base station in desired directions) has attracted attention as a key facilitating technology for Beyond 5G and 6G wireless communications. This paper presents an improved path-loss model suitable for RIS-aided wireless communications. Unlike previously reported RIS path-loss models, the proposed model considers more practical electromagnetic phenomena (e.g., the incident and reflected gain patterns of the RIS unit cell, the effective received power at the RIS, the reflection phase error of each unit cell, and the specular reflection loss), by applying rigorous analysis. Furthermore, the accuracy of the proposed path-loss model is validated by extensive experimental measurements using our developed RIS prototype. The prototype consists of 576 phase-reconfigurable unit cells operating at 29 GHz, each of which features two PIN diodes. Each PIN diode is independently controlled to produce the desired phase distributions on the RIS. It is shown that the calculated received power in the proposed path-loss model matches the measurement results within a 1 dB margin across various practical test environments.

Path Loss Model Based on Machine Learning Using Multi-Dimensional Gaussian Process Regression

For beyond the fifth-generation (5G) and future wireless communications, spatial consistency that represents the correlation between propagation channel characteristics in close proximity has become one of the major issues in channel modeling to describe channels more realistically in emerging scenarios, such as device-to-device (D2D). In this study, we propose a novel path loss model based on multi-dimensional Gaussian process regression (GPR) that provides spatial consistency to channels in propagation environment by predicting local shadow fading while fitting large-scale path loss from measured data. The proposed model has a special structure consisting of a radial mean function and local shadow fading term, which are modeled by independent Gaussian Processes. In contrast to the log-distance path loss model and other regression-based models, the proposed model trains two functions simultaneously; thus, predicts path loss well by capturing both global tendency and local correlation. Moreover, because the proposed model is based on GPR, it provides uncertainty of the predicted path loss. We validated the performance of the proposed model in terms of prediction accuracy with the measurement datasets from two different indoor environments. Our experiments showed that the proposed model predicts better than the log-distance path loss model, especially when spatial correlation becomes significant. The proposed model can be used to simulate path loss in a general environment after training the measurement data.

A Fuzzy-Logic Based Path Loss Model at 3.4 GHz for LTE Networks

Empirical and deterministic models have not proven to be effective in path loss predictions because of the problems of computational complexities, low accuracies, and inability to generalize. To solve these problems relating to path loss predictions, this article presents an optimal path loss propagation model developed at 3.4 GHz with the use of fuzzy logic. We introduced Fuzzy logic to accurately represent all forms of uncertainties in the data spectrum as the signal propagates from the transceiver to the receiver, thereby producing accurate results. Experimental data were collected across Cyprus at 3.4 GHz and compared with three existing path loss models. The fuzzy-logic path loss prediction model was then developed and compared with the experimental data and with each of the theoretical empirical models, the newly developed model predicted signal loss with the greatest accuracy as it gives the lowest root-mean-square error. The newly developed model is very efficient for signal propagation and path loss prediction.

Path Loss Modeling: A Machine Learning Based Approach Using Support Vector Regression and Radial Basis Function Models

Path loss prediction models are vital for accurate signal propagation in wireless channels. Empirical and deterministic models used in path loss predictions have not produced optimal results. In this paper, we introduced machine learning algorithms to path loss predictions because it offers a flexible network architecture and extensive data can be used. We introduced support vector regression (SVR) and radial basis function (RBF) models to path loss predictions in the investigated environments. The SVR model was able to process several input parameters without introducing complexity to the network architecture. The RBF on its part provides a good function approximation. Hyperparameter tuning of the machine learning models was carried out in order to achieve optimal results. The performances of the SVR and RBF models were compared and result validated using the root-mean squared error (RMSE). The two machine learning algorithms were also compared with the Cost-231, SUI, Egli, Freespace, Cost-231 W-I models. The analytical models overpredicted path loss. Overall, the machine learning models predicted path loss with greater accuracy than the empirical models. The SVR model performed best across all the indices with RMSE values of 1.378 dB, 1.4523 dB, 2.1568 dB in rural, suburban and urban settings respectively and should therefore be adopted for signal propagation in the investigated environments and beyond.

Robust Channel Modeling of 2.4 GHz and 5 GHz Indoor Measurements: Empirical, Ray Tracing, and Artificial Neural Network Models

Robust channel models for indoor areas are a crucial part of network planning and are immensely valuable for the small cell and indoor 5G network evolution. As the main input for many resource allocation and network planning problems, the accuracy of the path loss model can improve the overall accuracy of these techniques. Previous measurement campaigns exist for outdoor areas and higher frequencies; however, extensive indoor measurements at these frequencies are missing from the literature. Both WLAN and LTE networks use 2.4- and 5-GHz bands. For this work, indoor measurements were carried out in two distinct indoor environments, at two frequencies, and various models were compared. The measurements were made at the Deutsches Museum Bonn and the ICT cubes, an office space at RWTH Aachen University. Both empirical and deterministic models are tested on the data; the free-space path loss model, the single and dual-slope models with line-of-sight and nonline-of-sight, ray tracing models, and artificial neural network models were all tested and evaluated. Overall, the artificial neural network combined with the free-space path loss model proved to be the most robust model, which accurately predicted the propagation in the indoor environments, at both frequencies.

A Novel Mining Approach for Data Analysis and Processing Using Unmanned Aerial Vehicles

In the mining industry, smart surveying and exploration operations for the minerals are essential during mining missions. Usually, these missions are performed in remote areas that do not have a wireless communications infrastructure. This paper proposes to use the unmanned aerial vehicle (UAV) as a relay communication node between the exploration team and the ground control station (GCs). UAV can act as a relay node to provide mobile, flexible, and reliable communication links in remote environments and complex topologies. In this work, the pathloss models in millimeter‐wave technology are considered because they provide massive data rates for line of sight scenarios. The optimization problem of identifying a 3D location and trajectory of the UAV relay node is formulated to maximize the total team members’ data rate. Because the problem is non‐convex, the particle swarm optimization algorithm is used to solve it and determine an efficient location and trajectory of the UAV.

Interference Distribution for Directional Beamforming Mobile Networks

In this paper, we model the aggregate interference power in directional beamforming mobile networks. The work considers the random waypoint model to describe the mobility of the nodes and adopts directional beamforming for communication. The major contribution of this paper is the statistical characterization of the aggregate interference caused by directional beamforming transmissions of mobile interferers to a given node positioned at a reference point. The analysis assumes Rayleigh and Rician small-scale fading channels, a distance-based path-loss large-scale fading model, and a three gain levels sectored antenna model. The quality of the proposed approximations has been confirmed through various simulations for different mobility scenarios, channel conditions, and beamforming parameters, highlighting the effect of directional communication along with mobility on the aggregate interference. To demonstrate the practical application of the work, we use two different estimators for the interference characterization. The results confirm the effectiveness of the estimators even when adopting a small set of samples.

Analysis and Modeling of Path Loss for Terrestrial Television in Nigeria

Analysis and Modeling of Path Loss for Terrestrial Television in Nigeria

Directionally Resolved Measurement and Modeling of THz Band Propagation Channels

Design and performance assessment of THz communications systems, which will form an essential part of 6G, require an understanding of the propagation channels the systems will operate in. This paper presents investigations of the channel characteristics in various scenarios at 145 GHz, which is the band currently envisioned for the first round of deployments. In particular, we review several extensive measurement campaigns performed by the University of Southern California in both outdoor and indoor environments. We present the measurement and evaluation methodology and sample results that illustrate the dominant propagation effects in different environments. We then summarize the parameters of the statistical channel models for path loss, delay dispersion, and angular dispersion. Based on these results, we find that even in NLoS (non-line-of-sight) situations, Gbit/s communications can be sustained over a 100 m distance; that (for an antenna gain of 20 dB), there is considerable delay dispersion, requiring tens of equalizer taps, and that angular dispersion is significant in both LoS and NLoS situations. The channel parameters can be thus used as a basis for system design and evaluation under realistic operating conditions.

Characterisation of path loss models in wireless sensor networks: scenarios, variables, and open problems

Characterisation of path loss models in wireless sensor networks: scenarios, variables, and open problems

Millimetre-Wave Propagation Channel Based on NYUSIM Channel Model With Consideration of Rain Fade in Tropical Climates

The impact of atmospheric attenuation on wireless communication links is much more severe and complicated in tropical regions. That is due to the extreme temperatures, intense humidity, foliage and higher precipitation rain rates with large raindrop sizes. This paper investigates the propagation of the mm-waves at the 38 GHz link based on real measurement data collected from outdoor microcellular systems in Malaysia. The rainfall rate and received signal level have been measured simultaneously in 1-minute time intervals for one year over a 300 m path length. The rain attenuation distributions at different percentages of exceedance time have been compared with the modified distance factor of the ITU-R P.530-17 model. The average link availability calculated with the measured rain rates has been analysed. Additionally, the key propagation channel parameters such as the path loss, path loss exponent, Rician K-factor, root mean square, delay spread and received power have been investigated considering the rain attenuation. These propagation channel parameters have been analysed using MATLAB software and explained with the help of the latest NYUSIM channel model software package (Version 2.0). The analysis results have been classified considering rain attenuation, antenna setup, link distances, antenna height and antenna gain. The outcomes revealed that the rain fade predicted by applying the modified distance factor provides high consistency with the measured fade in Malaysia and several available measurements from different locations. The large-scale path loss model in the NYUSIM simulation result was around 126.23 dB by considering the rain attenuation effects on the 300m path length. This work shows that the NYUSIM channel model offers more accurate rendering results of path loss for omnidirectional and directional antenna transmissions without rain fade. This study proves that the ability to provide good coverage and ultra-reliable communication for outdoor and outdoor-to-indoor applications during rain in tropical regions must be sufficiently addressed.

Characterisation of path loss models in wireless sensor networks: scenarios, variables, and open problems

Characterisation of path loss models in wireless sensor networks: scenarios, variables, and open problems

Tuning Selection Impact on Kriging-Aided In-Building Path Loss Modeling

How do you know you select enough tuning dataset from measurements to guarantee model prediction accuracy? Tuning datasets are often selected based on simple random sampling with predefined rates. Usually, these rates are determined as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</i> / <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</i> , where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</i> % of the data goes to training and the remaining <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</i> % goes to testing. But it is not clear to what extent tuning dataset in order to minimize the estimation path loss errors. It is, thus, required to analyze the performance of channel modeling by selecting—among all measurement samples—appropriate tuning dataset. Using radio measurements and deterministic Ray Launching techniques to collect enough reliable samples, this letter analyzes the impact of tuning dataset selection—expressed in terms of the mean absolute error and cost—on a novel Kriging-aided in-building measurement-based path loss prediction model.

Log Distance Path Loss Model: Application and Improvement for Sub 5 GHz Rural Fixed Wireless Networks

The study of radio signal propagation path loss (PL) is important for planning, designing and evaluating the performance of radio communication networks. However, the state-of-the-art in PL modelling for fixed wireless networks in rural environments is still ill-equipped for making accurate predictions. This paper explores the application of the log distance PL model to heterogeneous fixed wireless networks in harsh rural propagation conditions. This model is then extended and optimized to improve its accuracy. In particular, the dataset is classified according to many criteria, radio links are split into many intervals according to their distances, antenna heights and elevations are integrated into its formula and long-term extreme seasonal variations are considered. Our study uses a wide set of measurements from the fixed wireless networks of a wireless internet service provider in rural regions of Canada. The proposed modifications improve the accuracy by 7 to 15 dB in terms of the root mean squared error.

Assessing Repeatable Accuracy Potential of LoRa: A Navigation Approach

Two main classes of radio navigation systems are satellite and ground-based systems. Examples of such systems are eLoran deployed as a terrestrial system, and BeiDou, Galileo, GPS and Quasi-Zenith Satellite System (QZSS) deployed as satellite Navigation Systems. These systems have been investigated in different navigation use-cases using a hybrid receiver capable of receiving any navigation signal. The goal of this paper is to demonstrate LoRa’s capability as a signal of opportunity (SoOP) to be used with other navigation technologies in order to improve repeatable accuracy. While researchers have investigated LoRa’s potential for navigation purposes, there is limited literature regarding the repeatable accuracy estimation using LoRa. Our work proposes using a weighted horizontal dilution of precision (HDOP) while incorporating SNR, spreading factors (SF) and the Ericsson 9999 path loss model. The gateway separation distances: 1500 m, 1000 m and 400 m respectively are employed at a bandwidth of 125kHz to determine repeatable accuracy within a 95% confidence limit. Our simulation results show that for 1500 m, 1000 m, and 400 m, gateway separation distances have a best repeatable accuracy of 1249 m, 284.2 m and 4.314 m, respectively, when the spreading factor (SF) is equal to 12. The initial repeatable accuracy results are promising and comparable to those obtained by other researchers. Our model’s limitation is that the best SNR was not used to determine position. A climatic model and stochastic channel interference observed in the 900MHz frequency band were also assumed to be negligible to provide a working model that can be modified as data becomes available.

Sub-6 GHz Channel Modeling and Evaluation in Indoor Industrial Environments

This paper presents sub-6 GHz channel measurements using a directional antenna at the transmitter and a directional or omnidirectional antenna at the receiver at 4.145 GHz in sparse and dense industrial environments for a line-of-sight scenario. Furthermore, the first measured over-the-air error vector magnitude (EVM) results depending on different 5G new radio modulation and coding schemes (MCSs of16 QAM, 64 QAM and 256 QAM) are provided. From the measurement campaigns, the path loss exponents (PLE) using a directional and an omnidirectional antenna at the receiver in the sparse and the dense environment are 1.24/1.39 and 1.35/1.5, respectively. PLE results are lower than the theoretical free space PLE of 2, indicating that indoor industrial environments have rich multipaths. The measured power delay profiles show the maximum root mean square (RMS) delay spreads of 11 ns with a directional antenna and 34 ns with an omnidirectional antenna at the receiver in a sparse industrial environment. However, in a dense industrial environment the maximum RMS delay spreads are significantly increased: maximum RMS delay spreads range from 226 to 282 ns for the omnidirectional and the directional antenna configuration. EVM measurements show that to increase coverage and enable higher MCS modes to be used for reliable data transmission, in both industrial environments using a directional antenna at the transmitter and the receiver is required. The large-scale path loss models, multipath time dispersion characteristics and EVM results provide insight into the deployments of 5G networks operating at sub-6 GHz frequency bands in different industrial environments.

A 3D Multi-rays Path Loss Model above 6 GHz under Indoor Environments with Regular Structures

A 3D Multi-rays Path Loss Model above 6 GHz under Indoor Environments with Regular Structures

Propagation Characteristics and Modeling Simulation of Signals in Wireless Channels

This thesis studies the related performance of the hot-research wireless communication systems based on the fading phenomenon of signals in wireless channels, mainly introduces several typical cases of signal fading in the channel, studies the problem of signal attenuation loss, and carries out the Free space propagation Model, the modeling and simulation of the logarithmic path propagation loss model and its modified logarithmic normal shadow path loss model, and analyze the results of these models. In order to realize the construction of a better wireless communication system, according to the analysis of the results of the fading model of the signal in the channel, this thesis also simulates the Okumura model to predict the field strength of different terrains, and combines the correction of the model by Hata to analyze the propagation of radio waves in the wireless channel.

Random waypoint mobility and two-ray path loss model to analyse multiple routing protocols in mobile ad hoc networks

Mobile ad hoc networks face many challenges due to the wireless transmissions and mobility. The topology of the network changes rapidly due to the mobility of the nodes, and the wireless transmissions endure many effects like path loss, fading, interference, etc. The routing protocols in the mobile ad hoc networks are designed to provide the route under the dynamic topology and constraints environment. The purpose of this study is to analyse multiple routing protocols using random waypoint mobility of the nodes and by considering the effect of the two-ray path loss model for channel propagation. The simulation-based approach has been utilised to study the behaviour of the routing protocols in a dynamic environment. The flow of control messages and routing overheads has been analysed. The comparative performance of a set of six routing protocols has been analysed that assist in choosing the appropriate protocols for real deployments.