Deep learning for radio propagation: Using image-driven regression to estimate path loss in urban areas
Authors:
- Sotirios P. SotiroudisSotirios P. Sotiroudis
- Sotirios K. GoudosSGSotirios K. Goudos
- Katherine SiakavaraKSKatherine Siakavara
Open Access
- 1 September 2020
- journal article
- research article
- Published by Elsevier in ICT Express
- Vol. 6 (3) , 160-165
Abstract
No abstract availableKeywords
Funding Information
- European Union
- Greek national funds (T1EDK-05274)
This publication has 6 references indexed in Scilit:
- Neural Networks and Random Forests: A Comparison Regarding Prediction of Propagation Path Loss for NB-IoT Networks2019
- ANFIS Model for Path Loss Prediction in the GSM and WCDMA Bands in Urban AreaELEKTRIKA- Journal of Electrical Engineering, 2019
- Path loss predictions for multi-transmitter radio propagation in VHF bands using Adaptive Neuro-Fuzzy Inference SystemEngineering Science and Technology, an International Journal, 2018
- Face recognition based on convolutional neural network2017
- Mobile radio propagation path loss prediction using Artificial Neural Networks with optimal input information for urban environmentsAEU - International Journal of Electronics and Communications, 2015
- Deep learningNature, 2015