Path loss predictions for fringe areas using adaptive neuro-fuzzy inference system
Author:
Publisher
Springer Science and Business Media LLC
Subject
Strategy and Management,Safety, Risk, Reliability and Quality
Link
https://link.springer.com/content/pdf/10.1007/s13198-021-01196-7.pdf
Reference20 articles.
1. Abdulrasheed IY (2017) Kriging based model for path loss prediction in the VHF band. 2017 IEEE 3rd International Conference on Electro-Technology for National Development (NIGERCON), Owerri, Nigeria, https://doi.org/10.1109/NIGERCON.2017.8281892
2. Angeles JCD, Dadios EP (2015) Neural network-based path loss prediction for digital TV macrocells, Humanoid, nanotechnology, information technology, communication and control, environment and management (HNICEM), International Conference IEEE, Cebu, Philippines, https://doi.org/10.1109/HNICEM.2015.7393223
3. Benmus TA, Abboud R, Shatter MK (2015) Neural network approach to model the propagation path loss for great Tripoli area at 900, 1800, and 2100 MHz bands, sciences and techniques of automatic control and computer engineering (STA), 2015, 16th International Conference on, IEEE, Monastir, Tunisia https://doi.org/10.1109/STA.2015.7505236
4. Cruz JCD, Caluyo FS (2013) Heuristic modeling of outdoor path loss for 9m, 3m and 1.5 m antenna at 677 MHz. cybernetics and intelligent systems (CIS), IEEE Conference on IEEE, Manila, Philippines https://doi.org/10.1109/ICCIS.2013.6751589
5. Da Costa FM, Ramirez LAR, Dias MHC (2018) Analysis of ITU-R VHF/UHF propagation prediction methods performance on irregular terrains covered by forest. Antennas Propagat IET Microwaves 12(8):1450–1455. https://doi.org/10.1049/iet-map.2017.0992
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1. Large scale survey for radio propagation in developing machine learning model for path losses in communication systems;Scientific African;2023-03
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