Intelligent path loss prediction engine design using machine learning in the urban outdoor environment

2018 ◽  
Author(s):  
Genshe Chen ◽  
Ruichen Wang ◽  
Jingyang Lu ◽  
Yiran Xu ◽  
Dan Shen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Path Loss ◽  
Outdoor Environment ◽  
Engine Design ◽  
Loss Prediction ◽  
Path Loss Prediction

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 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

scholarly journals Comparison of Path Loss Prediction Models for UAV and IoT Air-to-Ground Communication System in Rural Precision Farming Environment

2021 ◽  
pp. 60-66
Author(s):  
Sarun Duangsuwan ◽  
◽  
Myo Myint Maw
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Communication System ◽  
Prediction Models ◽  
Path Loss ◽  
Precision Farming ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Loss Prediction ◽  
Path Loss Prediction

The comparison of path loss model for the unmanned aerial vehicle (UAV) and Internet of Things (IoT) air-to-ground communication system was proposed for rural precision farming. Due to the uncertainty of propagation channel in rural precision farming environment, the comparison of path loss prediction was investigated by the conventional particle swarm optimization (PSO) algorithms: PSO (exponential or Exp), PSO (polynomial or Poly) and the machine learning algorithms: k-nearest neighbor (k-NN), and random forest, are exploited to accurate the path loss models on the basic of the measured dataset. Meanwhile, the empirical model in the rural precision farming was considered. By using the machine learning-based algorithms, the coefficient of determination (R-squared: R2) and root mean squared error (RMSE) were evaluated as highly accuracy and precision more than the conventional PSO algorithms. According to the results, the random forest method was able to perform more than other methods. It has the smallest prediction errors.

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.

scholarly journals Investigating Automated Hyper-Parameter Optimization for a Generalized Path Loss Model

2021 ◽  
Author(s):  
Usman Sammani Sani ◽  
Daphne Teck Ching Lai ◽  
Owais Ahmed Malik
Keyword(s):  
Machine Learning ◽  
Parameter Optimization ◽  
Path Loss ◽  
Machine Learning Algorithms ◽  
Bayesian Optimization ◽  
Loss Model ◽  
Path Loss Model ◽  
International Telecommunication ◽  
Loss Prediction ◽  
Path Loss Prediction

This work aims at developing a generalized and optimized path loss model that considers rural, suburban, urban, and urban high rise environments over different frequencies, for use in the Heterogenous Ultra Dense Networks in 5G. Five different machine learning algorithms were tested on four combined datasets, with a sum of 12369 samples in which their hyper-parameters were automatically optimized using Bayesian optimization, HyperBand and Asynchronous Successive Halving (ASHA). For the Bayesian optimization, three surrogate models (the Gaussian Process, Tree Structured Parzen Estimator and Random Forest) were considered. To the best of our knowledge, few works have been found on automatic hyper-parameter optimization for path loss prediction and none of the works used the aforementioned optimization techniques. Differentiation among the various environments was achieved by the assignment of the clutter height values based on International Telecommunication Union Recommendation (ITU-R) P.452-16. We also included the elevation of the transmitting antenna position as a feature so as to capture its effect on path loss. The best machine learning model observed is K Nearest Neighbor (KNN), achieving mean Coefficient of Determination (R2), average Mean Absolute Error (MAE) and mean Root Mean Squared Error (RMSE) values of 0.7713, 4.8860dB, and 6.8944dB, respectively, obtained from 100 different samplings of train set and test set. Results show that machine learning can also be used to develop path loss models that are valid for a certain range of distances, frequencies, antenna heights, and environment types. HyperBand produced hyper-parameter configurations with the highest accuracy in most of the algorithms.

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