scholarly journals Path loss models with distance‐dependent weighted fitting and estimation of censored path loss data

2016 ◽  
Vol 10 (14) ◽  
pp. 1467-1474 ◽  
Author(s):  
Aki Karttunen ◽  
Carl Gustafson ◽  
Andreas F. Molisch ◽  
Rui Wang ◽  
Sooyoung Hur ◽  
...  
Keyword(s):  
Path Loss ◽  
Loss Data ◽  
Loss Models

scholarly journals Path loss models based on stochastic rays

2007 ◽  
Vol 1 (3) ◽  
pp. 602 ◽  
Author(s):  
L.Q. Hu ◽  
H. Yu ◽  
Y. Chen
Keyword(s):  
Path Loss ◽  
Loss Models

scholarly journals Bounding the Practical Error of Path Loss Models

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
Caleb Phillips ◽  
Douglas Sicker ◽  
Dirk Grunwald
Keyword(s):  
A Priori ◽  
Path Loss ◽  
Urban Environments ◽  
Production Networks ◽  
Modeling Error ◽  
Propagation Models ◽  
Performance Accuracy ◽  
Rural And Urban ◽  
Standard Models ◽  
Loss Models

We seek to provide practical lower bounds on the prediction accuracy of path loss models. We describe and implement 30 propagation models of varying popularity that have been proposed over the last 70 years. Our analysis is performed using a large corpus of measurements collected on production networks operating in the 2.4 GHz ISM, 5.8 GHz UNII, and 900 MHz ISM bands in a diverse set of rural and urban environments. We find that the landscape of path loss models is precarious: typical best-case performance accuracy of these models is on the order of 12–15 dB root mean square error (RMSE) and in practice it can be much worse. Models that can be tuned with measurements and explicit data fitting approaches enable a reduction in RMSE to 8-9 dB. These bounds on modeling error appear to be relatively constant, even in differing environments and at differing frequencies. Based on our findings, we recommend the use of a few well-accepted and well-performing standard models in scenarios wherea prioripredictions are needed and argue for the use of well-validated, measurement-driven methods whenever possible.

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