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 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 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.

Research on Prediction Method of Soft Soil Foundation Settlement

2011 ◽  
Vol 97-98 ◽  
pp. 36-39
Author(s):  
Xiao Ma Dong
Keyword(s):  
Support Vector Machine ◽  
Soft Soil ◽  
Prediction Method ◽  
Least Square ◽  
Support Vector ◽  
Prediction Methods ◽  
Foundation Settlement ◽  
Soil Foundation ◽  
Soft Soil Foundation ◽  
Curve Fitting Method

The current prediction methods of foundation settlement have biggish error under the condition of lesser foundation settlement observational datum. Aim at the localization of present prediction methods and the virtues of Support Vector Machine arithmetic, the method of predicting soft soil foundation settlement based on Least Square Support Vector Machine (LS-SVM) was proposed in this paper and compared with the neural network method and curve fitting method. The research results show that this proposed method is feasible and effective for predicting soft soil foundation settlement. Least Square Support Vector Machine provides a more advanced method than these conventional methods for predicting foundation settlement.

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 Characterization Using Machine Learning Models for GS-to-UAV-Enabled Communication in Smart Farming Scenarios

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Sarun Duangsuwan ◽  
Phakamon Juengkittikul ◽  
Myo Myint Maw
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Path Loss ◽  
Statistical Error ◽  
Support Vector ◽  
Ann Model ◽  
Smart Farming ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
Error Indicators

The purpose of this paper was to predict the path loss characterization of the ground-to-air (G2A) communication channel between the ground sensor (GS) and unmanned aerial vehicle (UAV) using machine learning (ML) models in smart farming (SF) scenarios. Two ML algorithms such as support vector regression (SVR) and artificial neural network (ANN) were studied to analyze the measured data in different scenarios with Napier and Ruzi grass farms as the measurement locations. The proposed empirical GS-to-UAV two-ray (GUT-R) model and the ML models were compared to characterize path loss prediction models. The performances of the path loss prediction models were evaluated using the statistical error indicators in different measurement locations and UAV trajectories. To obtain the statistical error indicators, the accuracy path loss results of UAV trajectory at 2 m altitudes showed the SVR model (MAE = 1.252 dB, RMSE = 3.067 dB, and R2 = 0.972) and the ANN model (MAE = 1.150 dB, RMSE = 2.502 dB, and R2 = 0.981) for the Napier scenario. In the Ruzi scenario, the SVR model (MAE = 1.202 dB, RMSE = 2.962 dB, and R2 = 0.965) and the ANN model (MAE = 1.146 dB, RMSE = 2.507 dB, and R2 = 0.983) were presented. For UAV trajectory at 5 m altitudes, the SVR model (MAE = 2.125 dB, RMSE = 4.782 dB, and R2 = 0.933) and the ANN model (MAE = 2.025 dB, RMSE = 4.439 dB, and R2 = 0.950) were resulted in the Napier scenario. In the Ruzi scenario, the SVR model (MAE = 2.112 dB, RMSE = 4.682 dB, and R2 = 0.935) and the ANN model (MAE = 2.016 dB, RMSE = 4.407 dB, and R2 = 0.954) were displayed. The proposed ML models using SVR and ANN can optimally predict the path loss characterization in SF scenarios, where the accuracy was 95% for the SVR and 97% for the ANN.

Application of the Least Squares Support Vector Machine for Life Prediction of Vital Parts

2014 ◽  
Vol 584-586 ◽  
pp. 2129-2132
Author(s):  
Hong Kai Wang ◽  
Ji Sheng Ma ◽  
Li Qing Fang ◽  
Da Lin Wu ◽  
Yan Feng Yang
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Curve Fitting ◽  
Life Prediction ◽  
Large Scale ◽  
Development Trend ◽  
Small Sample ◽  
Support Vector ◽  
Fitting Method ◽  
Curve Fitting Method

In order to better study the wear state of vital parts of the large scale equipment, and overcoming the disadvantage of small sample of vital parts, we use the least squares support vector machine (LS_SVM) algorithm to predict the wear state of vital parts. Using of quantum particle swarm optimization (QPSO) to optimize parameters least squares support vector machine, and achieved good results. Compared those with the method that use of curve fitting to predict the data development trend, show that this method is superior to the curve fitting method, and has good application value.

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