Path Loss Prediction Formula for Urban and Suburban Areas for 4G Systems

2006 ◽  
Author(s):  
K. Kitao ◽  
S. Ichitsubo
Keyword(s):  
Path Loss ◽  
Prediction Formula ◽  
Suburban Areas ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
4G Systems

scholarly journals Selection and Optimization of Most Suitable Path Loss Prediction Model for Suburban City in Nigeria Using Spline Interpolation

2018 ◽  
pp. 1-8
Author(s):  
A. Danladi ◽  
N. W. Silikwa ◽  
K. G. Gaya ◽  
A. H. Augustine
Keyword(s):  
Spline Interpolation ◽  
Mobile Station ◽  
Path Loss ◽  
Telecommunication Networks ◽  
Network Resources ◽  
Suburban Areas ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
Base Transceiver Station ◽  
Optimized Model

Path loss is an attenuation of Global System for Mobile Communication (GSM) signal between Base Transceiver Station (BTS) and Mobile Station (MS). Path loss helps network engineers in planning, designing and implementing telecommunication networks. This work examines the applicability of Hata, COST 231, ECC – 33, Ericsson and SUI models in a medium city (Mubi) in Adamawa State, Nigeria. Root mean square error (RMSE) between the measured and the predicted losses (output of the models) is obtained as 7.632dB, 14.736dB, 10.593dB, 0.639dB and 23.491dB respectively. Ericsson model is found to have the least RMSE. Therefore, it is selected as the best fit model and modified. Furthermore, the modified model is optimized to recover missing or to eliminate unwanted information in the radio path using spline interpolation. The optimized model can be employed for the deployment of network resources in suburban areas of Nigeria, especially in Mubi town in Adamawa State, in order to significantly enhance GSM signal QoS. It is recommended that fuzzy logic and spline interpolation techniques may be integrated and employed to further minimize the error obtained in this work.

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
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