Prediction Method for Propagation Path Loss Obstructed by Dielectric Material in the 920-MHz Band

Path Loss Propagation Model Prediction for GSM Network Planning

This paper deals with the outdoor path loss behavior. The study has been conducted in Mosul city Iraq to fit a suitable propagation model to measured data at 900 MHz and 1800 MHz in urban and suburban environment. The empirical models dealt which are Hata, Cost-231 Hata, international Telecommunication Union (ITU-R), Ericsson and Stanford University Interm (SUI). Path loss accuracy of the fitti...

Propagation Path-Loss Prediction Model for 4G Mobile Communication Systems

A Statistical-Sakagami model is proposed by using multiple regression formula for path loss prediction in urban and sub urban area. It is assumed that the three main parameters: hB, W and α are not deterministic variables like those in theory propagation models, but statistical variables, and all the three parameters are varying at the same time in the simulation. The performance of the formula...

study of cohesive devices in the textbook of english for the students of apsychology by rastegarpour

this study investigates the cohesive devices used in the textbook of english for the students of psychology. the research questions and hypotheses in the present study are based on what frequency and distribution of grammatical and lexical cohesive devices are. then, to answer the questions all grammatical and lexical cohesive devices in reading comprehension passages from 6 units of 21units th...

Dielectric Constant and Loss Factor of Dielectric Material Using Finite Element Method in a Cavity

A numerical technique to estimate the dielectric constant and loss factor of a homogeneous dielectric material placed in an arbitrary shaped cavity has been developed. The values of S-parameters are measured experimentally by placing the sample in the cavity. Starting with a trial set of permittivity values, the computation is carried out using the finite element technique (FEM) to match the Sp...

Path Loss Prediction by Robust Regression Methods