Path Loss Model Research Articles

Path-Loss Model for Wireless Sensor Networks in Air Pollution Environments Leveraging of Drones

Path-Loss Model for Wireless Sensor Networks in Air Pollution Environments Leveraging of Drones

Improved-RSSI-based indoor localization by using pseudo-linear solution with machine learning algorithms

With the rapid advancement of the Internet of Things and the popularization of mobile Internet-based applications, the location-based service (LBS) has attracted much attention from commercial developers and researchers. Received signal strength indicator (RSSI)-based indoor localization technology has irreplaceable advantages for many LBS applications. However, due to multipath fading, noise, and the limited dynamic range of the RSSI measurements, precise localization based on a path-loss model and multiliterate becomes highly challenging. Therefore, this study proposes a machine learning (ML)-based improved RSSI-based indoor localization approach in which RSSI data is first augmented and then classified using ML algorithms. In addition, we implement an experimental testbed to collect the RSSI value based on Wi-Fi using various reference and target nodes. The received RSSI measurements undergo pre-processing using pseudo-linear solution techniques for closed-form solutions, approximating the original system of nonlinear RSSI measurement equations with a system of linear equations. Finally, the RSSI measurement are trained using ML models such as linear regression, polynomial regression, support vector regression, random forest regression, and decision tree regression. Consequently, the experimental results express in terms of root mean square error and coefficient of determinant compared with various machine learning models with hyper-parameter tuning.

An improved path loss model for 5G wireless networks in an enclosed hallway

An improved path loss model for 5G wireless networks in an enclosed hallway

Investigation of Error Margin of Some Path Loss Models Over Digital Terrestrial Television Channel in Katsina Metropolis

Due to rapid development in mobile communication technology in recent times, strong signal coverage has become a major necessity. However, path loss is one of the major challenges against strong signal coverage. Several path loss models have been developed for predicting wireless signal coverage for urban, sub-urban and rural areas. However, the need to investigate which model can best predict losses in an environment becomes necessary. This work is aimed at comparing the Error Margin of some Path loss Models over Digital Terrestrial Television (DTTV) channel using the Star Times DTTV Channel within Katsina metropolis. Seven path loss predicting models for outdoor macro cell wireless communication were used. They are; Free Space model, COST-231 model, (developed by the European Union Co-operative for Scientific and Technical Research Team) Hata - Okurmura model, Plane Earth model, Okumura model, Ericson model and ECC model. The measurement campaigns were in two seasons, the wet season was in August and dry season in November, 2021. Both measured and predicted path loss values were computed using empirical models. Statistical error analysis based on the RMSE was carried out to determine the error margins between measured and predicted values. Based on the result of path loss assessment and error analysis, COST-231 with the lowest RMSE value of 13.49 dB which is within the acceptable range for sub-urban city is the most preferred amongst the investigated models for path loss prediction over digital UHF channel in Katsina city

Misurata Rural Area Path Loss Propagation Prediction and Optimization Using Machine Learning Techniques

Path loss prediction is of great importance in studies of designing, developing, and improving the performance of wireless networks and mobile communication systems of the 5G which requires of accuracy in performance and reduction in losses. Therefore, new models should be proposed to predict path loss propagation with high accuracy and less complexity. In this paper, a path loss model was designed and implemented using artificial intelligence and machine learning techniques, which training and testing on a real database created based on maximum measured distance and received power from the base station in a GSM system operating in 900 MHz band, in a rural environment, Misurata, Libya. Traditional methods like the "simple path loss model" performed poorly, with an average error of 5.87. In contrast, modern models based on artificial intelligence (AI) like GRNN, Fitnet, and Feedforward Net achieved significantly lower errors, ranging from 0.26 to 1.35. Furthermore, the AI models showed a strong correlation between the predicted and actual signal strengths (correlation coefficient R reaching 0.9962 for Fitnet and 0.99399 for Feedforward Net). This indicates highly specific and reliable predictions. Keywords: wireless communications, propagation loss prediction, ANN, machine learning, GRNN, urban area

A Tree Attenuation Factor Model for a Low-Power Wide-Area Network in a Ruby Mango Plantation

Ruby mangoes are a cultivar with a thick skin, firm texture, red color, no splinters, and thin seeds that is grown in eastern Thailand for export. Implementing a low-power wide-area network (LPWAN) for smart agriculture applications can help increase the crop quality or yield. In this study, empirical path loss models were developed to help plan a LPWAN, operating at 433 MHz, of a Ruby mango plantation in Sakaeo, eastern Thailand. The proposed models take advantage of the symmetric pattern of Ruby mango trees cultivated in the plantation by using tree attenuation factors (TAFs) to consider the path loss at the trunk and canopy levels. A field experiment was performed to collect received signal strength indicator (RSSI) measurements and compare the performance of the proposed models with those of conventional models. The proposed models demonstrated a high prediction accuracy for both line-of-sight and non-line-of-sight routes and performed better than the other models.

A Particle Swarm and Smell Agent-Based Hybrid Algorithm for Enhanced Optimization

The particle swarm optimization (PSO) algorithm is widely used for optimization purposes across various domains, such as in precision agriculture, vehicular ad hoc networks, path planning, and for the assessment of mathematical test functions towards benchmarking different optimization algorithms. However, because of the inherent limitations in the velocity update mechanism of the algorithm, PSO often converges to suboptimal solutions. Thus, this paper aims to enhance the convergence rate and accuracy of the PSO algorithm by introducing a modified variant, which is based on a hybrid of the PSO and the smell agent optimization (SAO), termed the PSO-SAO algorithm. Our specific objective involves the incorporation of the trailing mode of the SAO algorithm into the PSO framework, with the goal of effectively regulating the velocity updates of the original PSO, thus improving its overall performance. By using the trailing mode, agents are continuously introduced to track molecules with higher concentrations, thus guiding the PSO’s particles towards optimal fitness locations. We evaluated the performance of the PSO-SAO, PSO, and SAO algorithms using a set of 37 benchmark functions categorized into unimodal and non-separable (UN), multimodal and non-separable (MS), and unimodal and separable (US) classes. The PSO-SAO achieved better convergence towards global solutions, performing better than the original PSO in 76% of the assessed functions. Specifically, it achieved a faster convergence rate and achieved a maximum fitness value of −2.02180678324 when tested on the Adjiman test function at a hopping frequency of 9. Consequently, these results underscore the potential of PSO-SAO for solving engineering problems effectively, such as in vehicle routing, network design, and energy system optimization. These findings serve as an initial stride towards the formulation of a robust hyperparameter tuning strategy applicable to supervised machine learning and deep learning models, particularly in the domains of natural language processing and path-loss modeling.

Multi-Frequency Channel Measurement and Characteristic Analysis in Forested Scenario for Emergency Rescue

Wireless communication has been widely used in emergency rescue, including from command vehicles to command vehicles, command vehicles to rescue teams, command vehicles to wireless sensors, and rescue teams to intelligent platforms such as unmanned vehicles. Compared to those used in cities and suburbs, when the same communication equipment is used in forests, its communication performance, such as transmission distance, is entirely different. The main reason for this phenomenon is the extraordinary complexity of wireless signal propagation in forest scenarios. Therefore, in order to accurately and quantitatively describe the wireless channel characteristics in forest scenarios, a frequency channel measurement activity is conducted in a forest and analysis is performed to acquire the channel characteristics in forest scenarios. The measurements are carried out at 380 MHz, 640 MHz, and 1420 MHz in virgin forest. Based on the measurement data, the average power delay profile (APDP) is obtained, and multipath components (MPCs) are extracted. Root mean square (RMS) delay spread and path loss (PL) are analyzed according to MPCs. Furthermore, a new path loss model is proposed. Finally, a new path loss model and relative analysis are provided.

Indoor WiFi path loss model to estimate indoor network coverage considering residential design

PurposeNetwork coverage is crucial for the adoption of advanced Smart Home applications. The commonly used log-based path loss model is not able to accurately estimate WiFi signal strength in different houses, as it does not fully consider the impact of building morphology. To better describe the propagation of WiFi signals and achieve higher estimation accuracy, this paper studies the basic building morphology characteristics of houses.Design/methodology/approachA new path loss model based on a decision tree was proposed after measuring the WiFi signal strength passing through multiple housing units. Three types of regression models were tested and compared.FindingsThe findings demonstrate that the log-based path loss model fits small houses well, while the newly proposed nonlinear path loss model performs better in large houses (area larger than 125 m2 and area-to-perimeter ratio larger than 2.5). The impact of building design on path loss has been proven and specifically quantified in the model.Originality/valueProposed an improved model to estimate indoor network coverage. Quantify the impacts of building morphology on indoor WiFi signal strength. Improve WiFi signal strength estimation to support Smart Home applications.

EFFECT OF THICK VEGETATION COVER, BUILDING OBSTRUCTIONS, AND EARTH TERRAIN ON THE QUALITY AND PERFORMANCE OF THE GSM SIGNAL AT THE FEDERAL POLYTECHNIC ADO-EKITI

Wireless communication system performance is fundamentally constrained by the mobile radio channels such as the Global System for Mobile Communication (GSM). The transmission path between the base transceiver station and the mobile station can range from line-of-sight to substantially obstructed environmental situations such as buildings, earth undulation valleys, and thick vegetation being the primary cause of poor network performance in and around the school perimeter. Mobile radio channels are incredibly unpredictable and do not provide simple analysis, in contrast to stationary and reliable wired channels. This research work, therefore, investigates the effect of thick vegetation cover, building obstructions, and earth terrain on the quality and performance of the GSM signal at the Federal Polytechnic Ado-Ekiti environs. A model for path loss (attenuation) is developed according to the Longley-Rice propagation model that forecasts the long-term median transmission loss over uneven terrain in comparison to the free-space transmission loss. The model was created for frequencies in the 20 MHz to 40 GHz range and path lengths in the 1 to 2,000 km range. The analysis of the results obtained shows that the path loss (attenuation) produced due to the effect of vegetation, building obstructions, and the earth undulation structures are quite significant. The solution of cell creation as a primary way of improving the quality of the GSM signal reception in the school perimeter without necessarily destroying the natural environment or demolishing any existing building is proposed.

Path Loss Modeling and Environment Features Powered Prediction for Sub-THz Communication

Path Loss Modeling and Environment Features Powered Prediction for Sub-THz Communication

Automatic Fingerprint Data Labeling Using WiFi Signal and Smartphone Camera for Indoor Positioning

WiFi fingerprinting has been one of the most practical approaches for implementing an indoor positioning system. However, the need to measure location labels for fingerprint data has hindered the deployment of WiFi fingerprint‐based positioning systems. To reduce the cost of location labeling, several methods for automatically labeling locations or bypassing location labeling have been proposed. However, these methods still need to measure location labels for partial fingerprint data, or the estimated location labels are inaccurate. In this paper, we propose Heimdall, an automatic fingerprint data labeling method using a smartphone camera and WiFi signal for an indoor positioning system. Since Heimdall utilizes camera geometry for accurate automatic labeling, it aims to find the location labels of unlabeled fingerprint data composed of WiFi received signal strengths (RSSs) and photos collected using smartphones in multiple reference points (RPs). Using structure from motion (SfM) with all photos in the unlabeled fingerprint dataset, the location and orientation of the cameras can be recovered on an arbitrary world coordinate system. However, the world coordinate system derived by SfM is warped. In other words, it is different from the real‐world coordinate system where we express the location labels since SfM has similarity ambiguity, including scale, rotation, and translation ambiguity. Heimdall finds the location labels by estimating and applying an unwarping transformation that removes those ambiguities. We use two types of geometric constraints for estimating the unwarping transformation. First, several geometric entities in the warped‐world coordinates and real‐world coordinates are extracted from the photos and prior information about the indoor layout. The unwarping transformation transforms the geometric entities in warped coordinates to those in real‐world coordinates. Second, we utilize the log‐distance pathloss model with RSSs to derive the constraint about distances from the location label to the access points. We construct the optimization problem subject to those constraints to estimate the unwarping transformation. To evaluate the performance of Heimdall, we performed several experiments. Heimdall yields a mean location labeling error of 0.12 m and a standard deviation of location labeling error of 0.06 m for the best case in the experiment. These results demonstrate that a fingerprint‐based indoor positioning system can use the location labels estimated by Heimdall without performance degradation.

LoRa Sense: Sensing and Optimization of LoRa Link Behavior Using Path-Loss Models in Open-Cast Mines

LoRa Sense: Sensing and Optimization of LoRa Link Behavior Using Path-Loss Models in Open-Cast Mines

Path Loss and Surface Impedance Models for Surface Wave-Assisted Wireless Communication System

Path Loss and Surface Impedance Models for Surface Wave-Assisted Wireless Communication System

Path Loss Model Estimation at Indoor Environment by Using Deep Neural Network and CatBoost for Wireless Application

Path Loss Model Estimation at Indoor Environment by Using Deep Neural Network and CatBoost for Wireless Application

Advance Path Loss Model for Distance Estimation Using LoRaWAN Network’s Received Signal Strength Indicator (RSSI)

Advance Path Loss Model for Distance Estimation Using LoRaWAN Network’s Received Signal Strength Indicator (RSSI)

Machine Learning-Based Path Loss Modeling with Simplified Features

Machine Learning-Based Path Loss Modeling with Simplified Features

Path Loss Modelling of Wireless Signals in Underground Tunnels

Path Loss Modelling of Wireless Signals in Underground Tunnels

Exploring 60 GHz Millimeter-wave Indoor Propagation Path Loss Models and Capacity Enhancement for Modified Indoor Environments

Exploring 60 GHz Millimeter-wave Indoor Propagation Path Loss Models and Capacity Enhancement for Modified Indoor Environments

Frequency Domain UWB Channel Characterization and Modeling in Indoor Static Environment

In this article, distance and frequency‐dependent indoor wireless channel characterization and modeling are presented in C, X, and Ku bands at the line of sight (LOS) and nonline of sight (nLOS) scenarios. Complex frequency responses of the indoor radio channel are measured using the frequency‐domain approach. The terminal and measurement setup dependencies of the propagation channel are deconvoluted using closed‐form expressions devised from a two‐port network model approach of the antenna transreceiver system. Subsequently, terminal independent frequency‐domain channel transfer functions (CTFs) and time‐domain channel impulse responses (CIRs) are derived for accurate statistical analysis of large‐ and small‐scale fading parameters of the propagation channel. A distance‐dependent statistical pathloss model is proposed. Successively, a frequency domain channel model using the fifth‐order autoregressive process is proposed. Subsequently, the existence of the multiple clusters in the environment is analyzed by the poles of the transfer functions of the model. Models are validated in both time and frequency domains by comparing the computer‐simulated synthetic model data with the empirical channel responses. The measurement data and the model data are seen to be in good agreement.