Methodologies for path loss prediction in LTE-1.8 GHz networks using neuro-fuzzy and ANN

2017 ◽  
Author(s):  
H. A. O. Cruz ◽  
R. N. A. Nascimento ◽  
J. P. L. Araujo ◽  
E. G. Pelaes ◽  
G. P. S. Cavalcante
Keyword(s):  
Path Loss ◽  
Neuro Fuzzy ◽  
Loss Prediction ◽  
Path Loss Prediction

Adaptive Neuro-Fuzzy model for path loss prediction in the VHF band

2017 ◽  
Author(s):  
Muhammed A. Salman ◽  
Segun I. Popoola ◽  
Nasir Faruk ◽  
N. T. Surajudeen-Bakinde ◽  
Abdulkarim A. Oloyede ◽  
...  
Keyword(s):  
Fuzzy Model ◽  
Path Loss ◽  
Neuro Fuzzy ◽  
Loss Prediction ◽  
Path Loss Prediction

scholarly journals Path Loss Modeling for Next Generation Wireless Network Using Fuzzy Logic - Spline Interpolation Technique

2018 ◽  
pp. 1-10
Author(s):  
A. Danladi ◽  
P. G. Vasira
Keyword(s):  
Fuzzy Logic ◽  
Wireless Networks ◽  
Spline Interpolation ◽  
Path Loss ◽  
Next Generation ◽  
Neuro Fuzzy ◽  
Interpolation Technique ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
Spline Interpolation Technique

Appropriate path loss mathematical models help in reducing the complexity of the co-channel interference of Global System for Mobile Communication and Wireless Networks. This work proposes, to adopt and modify Hata model using fuzzy logic and spline interpolation techniques. Mean absolute errors of 1.55 dB and 0.4 dB were obtained using the techniques respectively. It was found that, using fuzzy logic - spline interpolation model, path loss is minimized by 1.94 dB in the study area. Therefore, fuzzy logic - spline interpolation is recommended for path loss prediction for next generation wireless networks in medium cities in Nigeria especially Yola. It is suggested that, neuro – fuzzy spline interpolation may be used to further minimize path loss error for medium cities.

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

Calibration of Empirical Models for Path Loss Prediction in Urban Environment

2020 ◽  
pp. 301-310
Author(s):  
Robert O. Abolade ◽  
Dare J. Akintade ◽  
Segun I. Popoola ◽  
Folasade A. Semire ◽  
Aderemi A. Atayero ◽  
...  
Keyword(s):  
Urban Environment ◽  
Path Loss ◽  
Empirical Models ◽  
Loss Prediction ◽  
Path Loss Prediction

scholarly journals Path loss prediction model development in a mountainous forest environment

2021 ◽  
pp. 1-1
Author(s):  
Bilguunmaa Myagmardulam ◽  
Tadachika Nakayama ◽  
Kazuyoshi Takahashi ◽  
Ryu Miura ◽  
Fumie Ono ◽  
...  
Keyword(s):  
Prediction Model ◽  
Model Development ◽  
Path Loss ◽  
Forest Environment ◽  
Loss Prediction ◽  
Path Loss Prediction

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 Application of Computational Intelligence Algorithms in Radio Propagation: A Systematic Review and Metadata Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Quadri Ramon Adebowale ◽  
Nasir Faruk ◽  
Kayode S. Adewole ◽  
Abubakar Abdulkarim ◽  
Lukman A. Olawoyin ◽  
...  
Keyword(s):  
Computational Intelligence ◽  
Path Loss ◽  
Channel Modeling ◽  
Radio Propagation ◽  
Future Research ◽  
Computational Techniques ◽  
Research Issues ◽  
Main Research ◽  
Loss Prediction ◽  
Path Loss Prediction

The importance of wireless path loss prediction and interference minimization studies in various environments cannot be over-emphasized. In fact, numerous researchers have done massive work on scrutinizing the effectiveness of existing path loss models for channel modeling. The difficulties experienced by the researchers determining or having the detailed information about the propagating environment prompted for the use of computational intelligence (CI) methods in the prediction of path loss. This paper presents a comprehensive and systematic literature review on the application of nature-inspired computational approaches in radio propagation analysis. In particular, we cover artificial neural networks (ANNs), fuzzy inference systems (FISs), swarm intelligence (SI), and other computational techniques. The main research trends and a general overview of the different research areas, open research issues, and future research directions are also presented in this paper. This review paper will serve as reference material for researchers in the field of channel modeling or radio propagation and in particular for research in path loss prediction.

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