Neural Networks and Random Forests: A Comparison Regarding Prediction of Propagation Path Loss for NB-IoT Networks

2019 ◽  
Author(s):  
Sotirios P. Sotiroudis ◽  
Sotirios K. Goudos ◽  
Katherine Siakavara
Keyword(s):  
Neural Networks ◽  
Random Forests ◽  
Path Loss ◽  
Propagation Path

scholarly journals Joint UAV channel modeling and power control for 5G IoT networks

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Xiuhua Fu ◽  
Tian Ding ◽  
Rongqun Peng ◽  
Cong Liu ◽  
Mohamed Cheriet
Keyword(s):  
Energy Efficiency ◽  
Power Control ◽  
Channel Model ◽  
Path Loss ◽  
Channel Modeling ◽  
Signal Propagation ◽  
Spectrum Efficiency ◽  
Propagation Path ◽  
Control Factor ◽  
The Impact

AbstractThis paper studies the communication problem between UAVs and cellular base stations in a 5G IoT scenario where multiple UAVs work together. We are dedicated to the uplink channel modeling and the performance analysis of the uplink transmission. In the channel model, we consider the impact of 3D distance and multi-UAVs reflection on wireless signal propagation. The 3D distance is used to calculate the path loss, which can better reflect the actual path loss. The power control factor is used to adjust the UAV's uplink transmit power to compensate for different propagation path losses, so as to achieve precise power control. This paper proposes a binary exponential power control algorithm suitable for 5G networked UAV transmitters and presents the entire power control process including the open-loop phase and the closed-loop phase. The effects of power control factors on coverage probability, spectrum efficiency and energy efficiency under different 3D distances are simulated and analyzed. The results show that the optimal power control factor can be found from the point of view of energy efficiency.

scholarly journals Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2332
Author(s):  
Cecilia Martinez-Castillo ◽  
Gonzalo Astray ◽  
Juan Carlos Mejuto
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Random Forests ◽  
Prediction Models ◽  
Meteorological Station ◽  
Ann Model ◽  
Global Irradiation ◽  
Artificial Neural ◽  
Input Variables ◽  
Using Data

Different prediction models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to model the monthly global irradiation (MGI) from different input variables (latitude, longitude and altitude of meteorological station, month, average temperatures, among others) of different areas of Galicia (Spain). The models were trained, validated and queried using data from three stations, and each best model was checked in two independent stations. The results obtained confirmed that the best methodology is the ANN model which presents the lowest RMSE value in the validation and querying phases 1226 kJ/(m2∙day) and 1136 kJ/(m2∙day), respectively, and predict conveniently for independent stations, 2013 kJ/(m2∙day) and 2094 kJ/(m2∙day), respectively. Given the good results obtained, it is convenient to continue with the design of artificial neural networks applied to the analysis of monthly global irradiation.

Sign in / Sign up

Export Citation Format

Share Document