An Artificial Intelligence Approach to Ultra High Frequency Path Loss Modelling of the Suburban Areas of Abuja, Nigeria

were created on the bases of two deep learning networks, namely the Adaptive Neuro Fuzzy Inference System ANFIS and the Generalized Radial Basis Function Neural network RBF NN . These prediction models were created, trained, validated and tested for path loss prediction using path loss data recorded at 1800MHz from multiple Base Transceiver Stations BTSs distributed across the areas under investigation. Results indicate that the ANFIS and RBF NN based models with Root Mean Squared Error RMSE values of 5.30dB and 5.31dB respectively, offer greater prediction accuracy over the widely used empirical COST 231 Hata, which has an RMSE of 8.18dB. Deme C. 

by Abraham ""An Artificial Intelligence Approach to Ultra-High Frequency Path Loss Modelling of the Suburban Areas of Abuja, Nigeria""

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,

URL: https://www.ijtsrd.com/papers/ijtsrd30227.pdf

Paper Url : https://www.ijtsrd.com/computer-science/artificial-intelligence/30227/an-artificial-intelligence-approach-to-ultra-high-frequency-path-loss-modelling-of-the-suburban-areas-of-abuja-nigeria/deme-c-abraham

callforpapertechnology, technologyjournal