Modelling Ray Tracing Propagation Data Using Different Machine Learning Algorithms. Goudos, S., K., Athanasiadou, G., Tsoulos, G., V., & Rekkas, V. In 14th European Conference on Antennas and Propagation, EuCAP 2020, 2020.
Modelling Ray Tracing Propagation Data Using Different Machine Learning Algorithms [link]Website  doi  abstract   bibtex   
In this paper, we apply different machine learning methods for the prediction of path loss in urban environment for cellular communications with unmanned aerial vehicles (UAVs). We generate the training set using a ray tracing technique assuming a flying base station at different heights within the city of Tripolis, Greece. We produce prediction models for the path loss using three different learners the k-Nearest Neighbors (kNN), the Support Vector Regression (SVR)and the Random Forest (RF). The obtained numerical results are compared with the original data from the test dataset using representative performance indicators and overall they exhibit good precision.
@inproceedings{
 title = {Modelling Ray Tracing Propagation Data Using Different Machine Learning Algorithms},
 type = {inproceedings},
 year = {2020},
 keywords = {Random Forest,Ray tracing,Support Vector Regression,k-Nearest Neighbors,mobile communications},
 websites = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088630520&doi=10.23919%2FEuCAP48036.2020.9135639&partnerID=40&md5=448e25203d908932d9803ee5bea5bb94},
 id = {14451e24-75d9-3db2-a504-a0b3cc6ba392},
 created = {2020-08-31T20:28:48.387Z},
 file_attached = {false},
 profile_id = {c69aa657-d754-373c-91b7-64154b7d5d91},
 last_modified = {2023-02-11T18:54:04.903Z},
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 authored = {true},
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 citation_key = {Goudos2020a},
 source_type = {CONFERENCE},
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 abstract = {In this paper, we apply different machine learning methods for the prediction of path loss in urban environment for cellular communications with unmanned aerial vehicles (UAVs). We generate the training set using a ray tracing technique assuming a flying base station at different heights within the city of Tripolis, Greece. We produce prediction models for the path loss using three different learners the k-Nearest Neighbors (kNN), the Support Vector Regression (SVR)and the Random Forest (RF). The obtained numerical results are compared with the original data from the test dataset using representative performance indicators and overall they exhibit good precision.},
 bibtype = {inproceedings},
 author = {Goudos, Sotirios K and Athanasiadou, Georgia and Tsoulos, George V and Rekkas, Vasileios},
 doi = {10.23919/EuCAP48036.2020.9135639},
 booktitle = {14th European Conference on Antennas and Propagation, EuCAP 2020}
}

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