Path Loss Propagation Evaluation and Modelling based ECC-Model in Lowland Area on 1800 MHz Frequency

Abstract

Propagation modeling is the most important part of mobile wireless network planning. Wireless network planning requires an accurate calculation of the path, which depends on different environmental conditions. It requires accurate path loss modeling of the characteristics of a specific region. The study aimed to obtain a path loss propagation model by modifying the ECC model and using linear, logarithmic regression in lowland areas. The measurement used drive test method, located in the Jakabaring area that represented the lowland area. This research used four existing path loss models, namely Okumura-Hatta, COST-Hatta, Ericsson Model, and ECC Model. It was found that the Okumura-Hatta model had the largest RMSE value, 34.90, followed by the Ericsson model, 27.07, while the ECC model had the smallest RMSE value, 8.43. The ECC model required to be modified using logarithmic, linear regression to obtain the proposed model. The results of the evaluation showed that the proposed model improved with RMSE 4.93, MAPE 2.71, and MAD 3.91, whereas the values of the existing ECC Model before modification were 8.43 for RMSE, 4.72 for MAPE and 7.09 for MAD. The proposed model provided an accurate prediction of the path loss propagation in a lowland environment. The results of the study can be used for planning engineers to plan, design, and implement the wireless communication networks in lowland area conditions.

A Multiwall Path-Loss Prediction Model Using 433 MHz LoRa-WAN Frequency to Characterize Foliage’s Influence in a Malaysian Palm Oil Plantation Environment

“…In this research, each measurement showed varying signal strength (RSS) data for each LoRa radio configuration. This is because of the natural propagation phenomena known as diffraction, refraction, and reflection of the transmitted signal, which are caused by the surroundings of the palm oil plantation environment [ 39 ].…”

“…To observe the deviation of the multiwall prediction model from the measured and empirical models, RMSE (root-mean-square error) was calculated, since other researchers have also adopted RMSE as a validation method for path-loss propagation evaluation [ 39 ]. The RMSE (root-mean-square error) values present the variation between the empirical measurements and predicted/related empirical models for the palm oil plantation environment.…”

Development of Path Loss Prediction Model using Feature Selection-Machine Learning Approach

“…The crossover % values were changed between 0.5, 0.7, and 0.9, and the mutation % values were varied between 0.3, 0.5, and 0.7. Table IV shows that the best composition of variables achieved was using the variable numbers [2,3,4,5,9,10,11,15,18]. These variables are frequency, TX height, RX height, RX vertical angle from TX main beam, distance between buildings, barometric pressure, temperature, slope contour, and border to user distance (water).…”

“…The number of particles was varied with the values of 40, 70, and 100, while the W and C1/C2 values were varied with the values of 0.2, 0.5, and 0.8. Table V shows that the best composition of variables was the composition of variables [2,3,4,6,7,9,10,11,12,13, www.ijacsa.thesai.org 14,15,16,17,18]. The variables eliminated from the selected variables were TX-RX distance, RX vertical angle to mainbeam, and building height.…”

“…Path loss is used to predict the strength of the signal received by the user. The accuracy of path loss prediction plays an important role in determining the quality of a network design [2].The complexity of environmental characteristics influences the level of complexity in the prediction of received signal strength.In the propagation process, electromagnetic waves undergo a number of treatments caused by various environmental and weather factors that are present around the propagation media. Some of the nearby objects can affect the treatment of electromagnetic wave propagation [3].…”