Path Loss Model Research Articles

Development of a Multilayer Perceptron Neural Network for Optimal Predictive Modeling in Urban Microcellular Radio Environments

Modern cellular communication networks are already being perturbed by large and steadily increasing mobile subscribers in high demand for better service quality. To constantly and reliably deploy and optimally manage such mobile cellular networks, the radio signal attenuation loss between the path lengths of a base transmitter and the mobile station receiver must be appropriately estimated. Although many log-distance-based linear models for path loss prediction in wireless cellular networks exist, radio frequency planning requires advanced non-linear models for more accurate predictive path loss estimation, particularly for complex microcellular environments. The precision of the conventional models on path loss prediction has been reported in several works, generally ranging from 8–12 dB in terms of Root Mean Square Error (RMSE), which is too high compared to the acceptable error limit between 0 and 6 dB. Toward this end, the need for near-precise machine learning-based path loss prediction models becomes imperative. This work develops a distinctive multi-layer perception (MLP) neural network-based path loss model with well-structured implementation network architecture, empowered with the grid search-based hyperparameter tuning method. The proposed model is designed for optimal path loss approximation between mobile station and base station. The hyperparameters examined include the neuron number, learning rate and hidden layers number. In detail, the developed MLP model prediction accuracy level using different learning and training algorithms with the tuned best values of the hyperparameters have been applied for extensive path loss experimental datasets. The experimental path loss data is acquired via a field drive test conducted over an operational 4G LTE network in an urban microcellular environment. The results were assessed using several first-order statistical performance indicators. The results show that prediction errors of the proposed MLP model compared favourably with measured data and were better than those obtained using conventional log-distance-based path loss models.

Meta-learning approaches for indoor path loss modeling of 5G communications in smart factories

With the growing interest in both public and private 5G services based on millimeter wave (mmWave) communication for indoor usage scenarios such as smart factories, site design specialists are seeking sophisticated methods and tools for simulating indoor radio coverage based on highly accurate path loss prediction models. Although machine learning approaches can be used in path loss modeling thanks to the highly accurate prediction capability, their performance can be limited by the size of available measurement data set used for training. In this paper, we propose new approaches to train path loss models in the few-shot learning scenarios of smart factories. The proposed approaches are based on meta-learning with slight modifications to perform fine-tuning over an entire train data set rather than a meta-test data set. It is shown that the indoor path loss models based on convolutional neural networks (CNNs) trained by meta-learning based on three different meta-train task assignment schemes outperform both a conventional CNN model and an empirical model.

A 3-D Indoor Analysis of Path Loss Modeling Using Kriging Techniques

This study proposes a novel measurement-based method to predict and model three-dimensional (3-D) path loss in indoor scenarios, which first regresses 28 GHz measurements via median path loss modeling and then includes ordinary Kriging to interpolate shadowing. The performance of this method is evaluated by investigating the spatial structure that follows shadowing through the semivariogram, covariance function, and correlogram as variography tools. It is shown that semivariogram outperforms the other statistics to describe shadowing spatial continuity in path loss modeling in terms of the mean absolute error.

Path Loss Modeling for Wireless Network Deployment in Water Surface Environments

The efficient channel model is an extremely important cornerstone for the deployment and optimization of wireless networks. At present, many channel models are presented and discussed extensively. However, the channel model for water surface is rarely mentioned. To meet the need for water environmental protection, more and more sensors will be deployed on the water surface to establish the sensor network so as to monitor water quality in real time. Therefore, wireless channel modeling of water surface is becoming more and more exigent. Nevertheless, the propagation of signal on the water surface may meet more complicated reflection and scattering, and this aggravates the difficulty of channel modeling. In order to tackle this problem, we analyze related propagation theory and some representative path-loss models in detail, then propose a channel model in water surface environments. To validate the efficiency of the channel model, we set up an experimental platform and measure the attenuation of the RF signal above a pool filled with fresh water. The experimental result shows that the proposed model is more consistent with the characteristics of the attenuation of the signal on water surface. This result can be applied into the deployment of wireless sensor networks on the water surface.

Propagation loss model for neighborhood area networks in smart grids

Currently the power sector is striving for the efficient utilization of its generation capabilities in existing distributed energy networks. In future smart grids, applications like automated meter reading, direct load control with demand side management, and charging points for electric vehicles in multistory parking plazas will pose a strong communication challenge to the RF planners. One of the main objectives is to design a realistic link budget for outdoor-to-deep-indoor wireless communication scenarios while utilizing the existing grid infrastructure. This paper presents a realistic two-slope empirical path loss model to predict power requirements for the wireless link between smart meters in prosumer premises and remote utility/grid devices presumably available in near vicinity. The proposed model is based on measurements performed over three sub-gigahertz frequency bands (200 MHz, 434 MHz, and 868 MHz) and the 2.4 GHz band. It supports outside, inside and in-basement deployment of the smart meter communication unit.

Wireless Propagation in a Metallic Pipe for the Transmission of Sensory Oil and Gas Well Data

The feasibility of a wireless system is examined for propagation inside an oil and gas well pipeline. The well bore environment is modeled as a circular aluminium pipe and treated as a metallic microwave waveguide. The experimentation test bed was based on the nominal diameters of commercially available oil and gas well pipelines. At the transceivers, half-wave dipole antennas operating at 2.5 GHz were employed. Due to the excitation frequency and the enclosed pipe environment, parameters such as the propagating mode, the directivity, and the realized effective gain of the antennas also needed to be investigated for this scenario. Additionally, a numerical transmission path loss model was developed and verified using full-wave simulations and measurements. The communications link facilitated the continuous monitoring of sensory data with real-time temperature and pressure levels transmitted using N210 universal software radio peripheral modems by National Instruments and symbol coding using orthogonal frequency division multiplexing. This study and experimental work can be a basis for the implementation of a high-speed wireless communications system for deployment inside oil and gas well pipelines for real-time data transmission at microwave frequencies.

To Develop a Model for 4g LTE and Predictable 5G at 3500 Mhz that Would Predict the Path Loss for the Environment in Semi-Urban or Mixture of Urban and Rural Surroundings at Specific Geographical Locations

Abstract: In wireless communication, a planned network is given by the path loss model. Link budgeting, coverage prediction, and system performance optimization are indispensable in developing an accurate, simple, and general path loss model. To predict path loss in a particular environment each type of path loss propagation model is designed, it may be inaccurate in another different environment. In this paper, we are trying to predict a path loss model of Durgapur considering a particular place with a mixture of high-rise buildings, sub-urban, open, and foliage environments. For this proposal, the model area has been divided into twelve sectors taking 30° sectoring of radii 5 km and applying the path loss model for the calculation of path loss. Index Terms: Path loss, Predicted model, Outdoor propagation model consideration, Cell site selection, results, and discussions.

Probabilistic based Optimal Node Localization in Wireless Sensor Networks

Localization is one of the most important technologies for many applications in wireless sensor networks (WSNs). Node localization is the process of discovering the exact location of the node. If the number of nodes and network size increase, it becomes very arduous to localize the nodes whose result leads to complexity and path loss. In this paper, we proposed an approach called probabilistic based optimal node localization to obtain the location of node in the WSNs. This approach provides an enhanced channel pathloss model by capturing the features of the additive noise in WSN. In addition, the complexity has been minimized by discovering a lower bound of the non-convex function. The problem of non-convex optimization and subsequent nonlinear is solved with the help of relaxation to achieve a sub-optimal solution. Simulation results show that our proposed localization approach has got better performance for considered scenario settings.

Enhanced Path Loss Model by Image-Based Environmental Characterization

Accurate path loss (PL) models are critical for the design and optimization of wireless communication systems. The selection between empirical models and deterministic models are usually a tradeoff between accuracy and efficiency. In this letter, a novel environmental characterization method is defined by considering clutter information in the vector-based and image-based structures. Three PL models are proposed with different artificial neural network (ANN) architectures and two environmental characterizations. Comparative analysis shows that an image-based environmental characterization, which can reduce the influence of interference types, is better than a vector-based characterization. The combination of an image-based environmental characterization method and ANNs can enhance PL prediction in both accuracy and efficiency.

Near Ground Pathloss Propagation Model Using Adaptive Neuro Fuzzy Inference System for Wireless Sensor Network Communication in Forest, Jungle and Open Dirt Road Environments.

In Wireless Sensor Networks which are deployed in remote and isolated tropical areas; such as forest; jungle; and open dirt road environments; wireless communications usually suffer heavily because of the environmental effects on vegetation; terrain; low antenna height; and distance. Therefore; to solve this problem; the Wireless Sensor Network communication links must be designed for their best performance using the suitable electromagnetic wave behavior model in a given environment. This study introduces and analyzes the behavior of the LoRa pathloss propagation model for signals that propagate at near ground or that have low transmitter and receiver antenna heights from the ground (less than 30 cm antenna height). Using RMSE and MAE statistical analysis tools; we validate the developed model results. The developed Fuzzy ANFIS model achieves the lowest RMSE score of 0.88 at 433 MHz and the lowest MAE score of 1.61 at 433 MHz for both open dirt road environments. The Optimized FITU-R Near Ground model achieved the lowest RMSE score of 4.08 at 868 MHz for the forest environment and lowest MAE score of 14.84 at 868 MHz for the open dirt road environment. The Okumura-Hata model achieved the lowest RMSE score of 6.32 at 868 MHz and the lowest MAE score of 26.12 at 868 MHz for both forest environments. Finally; the ITU-R Maximum Attenuation Free Space model achieved the lowest RMSE score of 9.58 at 868 MHz for the forest environment and the lowest MAE score of 38.48 at 868 MHz for the jungle environment. These values indicate that the proposed Fuzzy ANFIS pathloss model has the best performance in near ground propagation for all environments compared to other benchmark models.

Indoor Positioning with CNN and Path-Loss Model Based on Multivariable Fingerprints in 5G Mobile Communication System.

Many application scenarios require indoor positioning in fifth generation (5G) mobile communication systems in recent years. However, non-line of sight and multipath propagation lead to poor accuracy in a traditionally received signal strength-based fingerprints positioning system. In this paper, we propose a positioning method employing multivariable fingerprints (MVF) composed of measurements based on secondary synchronization signals (SSS). In the fingerprint matching, we use MVF to train the convolutional neural network (CNN) location classification model. Moreover, we utilize MVF to train the path-loss model, which indicates the relationship between the distance and the measurement. Then, a hybrid positioning model combining CNN and path-loss model is proposed to optimize the overall positioning accuracy. Experimental results show that all three positioning algorithms based on machine learning with MVF achieve accuracy improvement compared with that of Reference Signal Receiving Power (RSRP)-only fingerprint. CNN achieves best performance among three positioning algorithms in two experimental environments. The average positioning error of hybrid positioning model is 1.47 m, which achieves 9.26% accuracy improvement compared with that of CNN alone.

Single-collision-induced path loss model of reflection-assisted non-line-of-sight ultraviolet communications.

By considering both scattering and reflection events as collision-induced events (CIEs), an analytical path loss model of reflection-assisted non-line-of-sight (NLOS) ultraviolet communications (UVC) is proposed with single CIE incorporated, which refers to the single-collision-induced (SCI) path loss model. More specifically, the analytical expressions of the received optical energy resulting from single-scatter and single-reflection events in reflection-assisted NLOS UVC are respectively derived. Then, in terms of those two expressions, the expression of the proposed SCI path loss model is obtained. Finally, Monte-Carlo (MC) simulations and experimental results are given to verify the correctness and effectiveness of the proposed SCI path loss model. The results manifest that the proposed SCI path loss model can work well in both coplanar and noncoplanar geometry of the reflection-assisted NLOS UVC.

Indoor Genetic Algorithm-Based 5G Network Planning Using a Machine Learning Model for Path Loss Estimation

Accurate wireless network planning is crucial for the deployment of new wireless services. This usually requires the consecutive evaluation of many candidate solutions, which is only feasible for simple path loss models, such as one-slope models or multi-wall models. However, such path loss models are quite straightforward and often do not deliver satisfactory estimations, eventually impacting the quality of the proposed network deployment. More advanced models, such as Indoor Dominant Path Loss models, are usually more accurate, but as their path loss calculation is much more time-consuming, it is no longer possible to evaluate a large set of candidate deployment solutions. Out of necessity, a heuristic network planning algorithm is then typically used, but the outcomes heavily depend on the quality of the heuristic. Therefore, this paper investigates the use of Machine Learning to approximate a complex 5G path loss model. The much lower calculation time allows using this model in a Genetic Algorithm-based network planning algorithm. The Machine Learning model is trained for two buildings and is validated on three other buildings, with a Mean Absolute Error below 3 dB. It is shown that the new approach is able to find a wireless network deployment solution with an equal, or smaller, amount of access points, while still providing the required coverage for at least 99.4% of the receiver locations and it does this 15 times faster. Unlike a heuristic approach, the proposed one also allows accounting for additional design criteria, such as maximal average received power throughout the building, or minimal exposure to radiofrequency signals in certain rooms.

Sub-terahertz propagation characteristics up to 150 GHz for 6G mobile communication systems

AbstractExtreme-high-speed communication exceeding 100 Gbps is one requirement for 6G. To satisfy extreme-high-speed-communication, one solution is to utilize terahertz bands above 100 GHz. To determine the new radio-interface technologies and service frequency bands for 6G, terahertz propagation characteristics above 100 GHz need to be understood. In this paper, we introduce our new radio-network topology for 6G and then show the frequency dependency of key propagation phenomena such as the characteristics of path loss in an urban environment, human blockage, and scattering from a rough building surface up to 150 GHz. Human blockage loss increases and the scattering is more diffused as the frequency increases. In the path-loss characteristics, it was found that path-loss frequency dependency is stronger than that given by conventional path-loss model because of scattering effects from a rough building surface.

FSO Path Loss Model Based on the Visibility

Free-space optics (FSO) is attracting renewed attention in the frame of 5G, which demands wireless backhauling technologies with extremely high data rates over distances up to a few kilometers. Link availability is a FSO well-known issue due to the large path loss expected during poor visibility, such as in case of fog. Several path loss models, based on the visibility, are assessed through an analytical approach. The simple relationship valid in the visible spectrum can be extended to the first optical window, while, at 1.550 μm, the effect of fog microphysics cannot be ignored. Fog is definitively less challenging in the mid-IR (10.6 µm) even at very low visibility values (less than 0.5 km), which was not evident from older studies. Lower and upper bounds for the extinction coefficient are calculated and substituted into the FSO link budget equation. The combined effect of the microphysics and of the accuracy of visibility measurements may result in large uncertainties on the maximum achievable path length. The latter is also influenced by the microclimate, as links are usually deployed in urban areas while visibility data are collected outside. Path lengths in city areas can double, for the same link availability.

A Stochastic Approach for Analysis of Shadowing Effects in Urban Area

5G networks are started to be deployed in urban areas in the dynamic spectrum sharing mode. Therefore, the analysis of Non-Line-of-Sight (NLOS) shadowing effects behind buildings of different heights becomes particularly important. The shadowing of GSM (900 MHz), UMTS (2100 MHz) and LTE (1800 MHz) technologies was investigated in the urban area. The results are examined by using the log-normal path loss model and four different stochastic equations, specifically: normal, log-normal, Rayleigh, and Nakagami. The results showed that the fluctuations of path losses in the urban area correspond to the model of log-normal path losses. However, path losses for individual buildings do not fit this model. Therefore, stochastic methods were used to analyse shadowing effects. A path loss model based on a differential stochastic equation was proposed. This model includes the following values: free space losses, path losses disturbance due to the shadowing effect, standard deviation σ, and a freely chosen parameter b. This model allows a very accurate estimate of the path losses of the three technologies. A clear correlation was observed between parameter b and standard deviation σ.

Clutter Loss of Low-Altitude UAV Channel in Suburban Scenario At 5.8 GHz

Low-altitude communication has attracted lots of attention in recent years, and it is very important to obtain an accurate clutter loss model for evaluating and designing such a communication system. For this purpose, we have conducted a channel measurement campaign at 5.8 GHz for suburabn low-altitude scenario. Based on the measured pathloss, we extract and analyze the clutter loss for different link distances and elevation angles between the transceiver. The measured clutter loss is validated by the ITU clutter loss model with respect to the link distance. Moreover, we compare the measured clutter loss with the the ITU clutter loss model with respect to the elevation angle, and find the obvious difference between them. Therefore, in this letter, we propose a modified clutter loss model and a pathloss model with respect to the elevation angle factor for the suburban low-altitude scenario.

Research on Modeling of Underground Path Loss Prediction Based on SA-SVM

In view of the serious refraction and reflection of radio wave transmission in the complex environment of coal mines, serious multipath fading occurs, which affects the performance of the communication system. Therefore, we presents a simulated annealing algorithm to optimize the kernel function and regularization function parameters in LS-SVM, and establishes a wireless channel path loss model based on LS-SVM. Comparing the optimized and improved model based on the SA algorithm with the traditional model, the MATLAB simulation experiment results show that the SA+LS-SVM model is significantly superior to the pre-optimized model and the traditional model in prediction accuracy, and can more effectively predict the fading change to the channel Condition.

Generalized Modeling and Propagation Characterization of THz Wireless Links in Computer Desktop Environment

AbstractIn this paper presents terahertz (THz) channel propagation characterization and generalized channel model for a desktop environment. Path loss and power delay profiles (PDPs) measured on the motherboard in both free space and desktop‐like metal cavity are compared. To characterize the large scale fading of the channel, a mean path loss model as a function of antenna height is proposed by treating the motherboard desktop environment as a partially dielectric filled resonant cavity. The measured and modeled mean path loss achieve 98.5% R‐squared goodness of fit. For the shadowing, a Gamma‐mixture model is applied to characterize the oscillations of in‐cavity measured path loss. Results show that with proper choice of the number of mixed Gamma distributions k, the goodness of fit between the model and the probability density function (PDF) can be greater than 97%. Multipath components are characterized by cluster‐based channel modeling. Modifications were made on the conventional Saleh‐Valenzuela (S‐V) model to accurately characterize the channel by rewriting the cluster power decay with step‐wise functions and each sub‐function is expressed exponentially in dB, and the ray power decay with power law approach. It is shown that measured and simulated multipath components match well with each other with at least 98.2% goodness of fit.

Millimeter-Wave Power Transmission for Compact and Large-Area Wearable IoT Devices Based on a Higher Order Mode Wearable Antenna

Owing to the shorter wavelength in the millimeter-wave (mmWave) spectrum, miniaturized antennas can receive power with a higher efficiency than UHF bands, promising sustainable mmWave-powered Internet of Things (IoT) devices. Nevertheless, the performance of a mmWave power receiver has not been compared, numerically or experimentally, to its sub6-GHz counterpart. In this article, the performance of mmWave-powered receivers is evaluated based on a novel wearable textile-based higher order mode microstrip antenna, showing the benefits of wireless power transmission (WPT). First, a broadband antenna is proposed maintaining a stable wearable measured bandwidth from 24.9 to 31.1 GHz, over threefold improvement compared to a conventional patch. The proposed antenna has a measured 8.2 dBi co-polarized gain with the highest thickness-normalized efficiency of a wearable antenna. When evaluated for compact power receivers, the measured path gain shows that WPT at 26 GHz outperforms 2.4 GHz by 11 dB. A rectenna array based on the proposed antenna is then evaluated analytically showing the potential for up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6.3\times $ </tex-math></inline-formula> higher power reception compared to a UHF patch, based on the proposed antenna’s gain and an empirical path-loss model. Both use cases demonstrate that mmWave-powered rectennas are suitable for area-constrained and large-area wearable IoT applications.