scholarly journals Model-Aided Deep Learning Method for Path Loss Prediction in Mobile Communication Systems at 2.6 GHz

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 7925-7936 ◽  
Author(s):  
Jakob Thrane ◽  
Darko Zibar ◽  
Henrik Lehrmann Christiansen
Keyword(s):  
Deep Learning ◽  
Mobile Communication ◽  
Communication Systems ◽  
Path Loss ◽  
Learning Method ◽  
Mobile Communication Systems ◽  
Loss Prediction ◽  
Path Loss Prediction

scholarly journals Fusing Diverse Input Modalities for Path Loss Prediction: A Deep Learning Approach

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 30441-30451
Author(s):  
Sotirios P. Sotiroudis ◽  
Panagiotis Sarigiannidis ◽  
Sotirios K. Goudos ◽  
Katherine Siakavara
Keyword(s):  
Deep Learning ◽  
Path Loss ◽  
Learning Approach ◽  
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.

scholarly journals ANFIS Model for Path Loss Prediction in the GSM and WCDMA Bands in Urban Area

2019 ◽  
Vol 18 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Nasir Faruk ◽  
N. T. Surajudeen-Bakinde ◽  
Abubakar Abdulkarim ◽  
Segun I. Popoola ◽  
A. Abdulkarim ◽  
...  
Keyword(s):  
Urban Areas ◽  
Communication Systems ◽  
Research Work ◽  
Path Loss ◽  
Theoretical Models ◽  
Anfis Model ◽  
Propagation Models ◽  
Reliable Model ◽  
Loss Prediction ◽  
Path Loss Prediction

Path loss propagation is a vital concern when designing and planning networks in mobile communication systems. Propagation models such as the empirical, deterministic and theoretical models, which possess complex, inconsistent, time-consuming and non-adaptable features, have proven to be inefficient in designing of wireless systems, thereby resulting in the need for a more reliable model. Artificial Intelligence methods seem to overcome the drawbacks of the propagation models for predicting path loss. In this paper, the ANFIS approach to path loss prediction in the GSM and WCDMA bands is presented for selected urban areas in Nigeria. Furthermore, the effects of the number of Membership Functions (MFs) are investigated. The prediction results indicated that the ANFIS model outperformed the Hata, Cost-231, Egli and ECC-33 models in both Kano and Abuja urban areas. In addition, an increase in the number of MFs conceded an improved RMSE result for the generalized bell-shaped MF. The general performance and outcome of this research work show the efficiency and usefulness of the ANFIS model in improving prediction accuracy over propagation models

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