Path Loss Prediction Method Merged Conventional Models Effectively in Machine Learning for Mobile Communications

2021 ◽  
Author(s):  
Hiroaki NAKABAYASHI ◽  
Kiyoaki ITOI
Keyword(s):  
Machine Learning ◽  
Mobile Communications ◽  
Path Loss ◽  
Prediction Method ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
Conventional Models

scholarly journals Measurement and Prediction of Short-Range Path Loss between 27 and 40 GHz in University Campus Scenarios

2020 ◽  
Author(s):  
Glaucio Ramos ◽  
Carlos Vargas ◽  
Luiz Mello ◽  
Paulo Pereira ◽  
Sandro Gonçalves ◽  
...  
Keyword(s):  
Short Range ◽  
Prediction Models ◽  
Path Loss ◽  
Prediction Method ◽  
University Campus ◽  
Millimetre Wave ◽  
Empirical Prediction ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
Path Loss Measurements

Abstract In this paper, we present the results of short-range path loss measurements in the microwave and millimetre wave bands, at frequencies between 27 and 40 GHz, obtained in a campaign inside a university campus in Rio de Janeiro, Brazil. Existing empirical path loss prediction models, including the alpha-beta-gamma (ABG) model and the close-in free space reference distance with frequency dependent path loss exponent (CIF) model are tested against the measured data, and an improved prediction method that includes the path loss dependence on the height di erence between transmitter and receiver is proposed. A fuzzy technique is also applied to predict the path loss and the results are compared with those obtained with the empirical prediction models.

scholarly journals A New SVM-Based Modeling Method of Cabin Path Loss Prediction

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Xiaonan Zhao ◽  
Chunping Hou ◽  
Qing Wang
Keyword(s):  
Support Vector Machine ◽  
Path Loss ◽  
Prediction Method ◽  
Modeling Method ◽  
Support Vector ◽  
Propagation Modeling ◽  
Fitting Method ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
Curve Fitting Method

A new modeling method of cabin path loss prediction based on support vector machine (SVM) is proposed in this paper. The method is trained with the path loss values of measured points inside the cabin and can be used to predict the path loss values of the unmeasured points. The experimental results demonstrate that our modeling method is more accurate than the curve fitting method. This SVM-based path loss prediction method makes the prediction much easier and more accurate, which covers performance traditional methods in the channel propagation modeling.

scholarly journals Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion

2019 ◽  
Vol 9 (9) ◽  
pp. 1908 ◽  
Author(s):  
Yan Zhang ◽  
Jinxiao Wen ◽  
Guanshu Yang ◽  
Zunwen He ◽  
Jing Wang
Keyword(s):  
Machine Learning ◽  
Communication Systems ◽  
Classical Model ◽  
Path Loss ◽  
Measured Data ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
Data Expansion

Path loss prediction is of great significance for the performance optimization of wireless networks. With the development and deployment of the fifth-generation (5G) mobile communication systems, new path loss prediction methods with high accuracy and low complexity should be proposed. In this paper, the principle and procedure of machine-learning-based path loss prediction are presented. Measured data are used to evaluate the performance of different models such as artificial neural network, support vector regression, and random forest. It is shown that these machine-learning-based models outperform the log-distance model. In view of the fact that the volume of measured data sometimes cannot meet the requirements of machine learning algorithms, we propose two mechanisms to expand the training dataset. On one hand, old measured data can be reused in new scenarios or at different frequencies. On the other hand, the classical model can also be utilized to generate a number of training samples based on the prior information obtained from measured results. Measured data are employed to verify the feasibility of these data expansion mechanisms. Finally, some issues for future research are discussed.

Enhancing Machine Learning Models for Path Loss Prediction Using Image Texture Techniques

2021 ◽  
pp. 1-1
Author(s):  
Sotirios Sotiroudis ◽  
Katherine Siakavara ◽  
George Koudouridis ◽  
Panagiotis Sarigiannidis ◽  
Sotirios Goudos
Keyword(s):  
Machine Learning ◽  
Path Loss ◽  
Image Texture ◽  
Learning Models ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
Machine Learning Models

scholarly journals Measurement and Prediction of Short-Range Path Loss between 27 and 40 GHz in University Campus Scenarios

2021 ◽  
Author(s):  
Glaucio Ramos ◽  
Carlos Vargas ◽  
Luiz Mello ◽  
Paulo Pereira ◽  
Robson Vieira ◽  
...  
Keyword(s):  
Short Range ◽  
Prediction Models ◽  
Path Loss ◽  
Prediction Method ◽  
Additional Parameter ◽  
University Campus ◽  
Millimetre Wave ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
The Difference

Abstract In this paper, we present the results of short-range path loss measurement in the microwave and millimetre wave bands, at frequencies between 27 and 40 GHz, obtained in a campaign inside a university campus in Rio de Janeiro, Brazil. Existing empirical path loss prediction models, including the alpha-beta-gamma (ABG) model and the close-in free space reference distance with frequency-dependent path loss exponent (CIF) model, are tested against the measured data, and an improved prediction method that includes the path loss dependence on the height difference between transmitter and receiver is proposed. The main contribution of this paper is the use of the Fuzzy technique to perform path loss predictions for short links in the millimetre wave range, from 27 to 40 GHz, providing lower errors when compared to the traditional ABG and CIF models. However, it should be noted that the Fuzzy technique uses a set of equations to perform the prediction and the attenuation coefficient is not explicit as in the classical models. Also, a non-negligible correlation between the difference in height between transmitter and receiver positions and the path loss in such short links (i.e., the path inclination) has been observed and requires further investigation. If confirmed, it could provide an additional parameter to improve the accuracy of the traditional ABG model.

scholarly journals Path Loss Prediction Based on Machine Learning Methods for Aircraft Cabin Environments

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 159251-159261 ◽  
Author(s):  
Jinxiao Wen ◽  
Yan Zhang ◽  
Guanshu Yang ◽  
Zunwen He ◽  
Wancheng Zhang
Keyword(s):  
Machine Learning ◽  
Path Loss ◽  
Learning Methods ◽  
Aircraft Cabin ◽  
Machine Learning Methods ◽  
Loss Prediction ◽  
Path Loss Prediction
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