%0 Journal Article %1 noauthororeditor %A Nentawe, Finangwai D. Jacob | Deme C. Abraham | Gurumdimma Y. %D 2020 %J International Journal of Trend in Scientific Research and Development %K 231 COST Field Generalized Hata Model Network Neural Okumura Regression Strength %N 3 %P 467-471 %T A Generalized Regression Neural Network Approach to Wireless Signal Strength Prediction %U https://www.ijtsrd.com/computer-science/artificial-intelligence/30501/a-generalized-regression-neural-network-approach-to-wireless-signal-strength-prediction/finangwai-d-jacob %V 4 %X This study presents a Generalized Regression Neural network GRNN based approach to wireless communication network field strength prediction. As case study, the rural area between the cities of Bauchi and Gombe, Nigeria, was considered. The GRNN based predictor was created, validated and tested with field strength data recorded from multiple Base Transceiver Stations at a frequency of 1800MHz. Results indicate that the GRNN based model with Root Mean Squared Error RMSE value of 5.8dBm offers significant improvements over the empirical Okumura and COST 231 Hata models. While the Okumura model overestimates the field strength, the COST 231 Hata significantly underestimates it. Finangwai D. Jacob | Deme C. Abraham | Gurumdimma Y. Nentawe Ä Generalized Regression Neural Network Approach to Wireless Signal Strength Prediction" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30501.pdf

@article{noauthororeditor, abstract = {This study presents a Generalized Regression Neural network GRNN based approach to wireless communication network field strength prediction. As case study, the rural area between the cities of Bauchi and Gombe, Nigeria, was considered. The GRNN based predictor was created, validated and tested with field strength data recorded from multiple Base Transceiver Stations at a frequency of 1800MHz. Results indicate that the GRNN based model with Root Mean Squared Error RMSE value of 5.8dBm offers significant improvements over the empirical Okumura and COST 231 Hata models. While the Okumura model overestimates the field strength, the COST 231 Hata significantly underestimates it. Finangwai D. Jacob | Deme C. Abraham | Gurumdimma Y. Nentawe "A Generalized Regression Neural Network Approach to Wireless Signal Strength Prediction" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30501.pdf }, added-at = {2020-06-10T08:17:13.000+0200}, author = {Nentawe, Finangwai D. Jacob | Deme C. Abraham | Gurumdimma Y.}, biburl = {https://www.bibsonomy.org/bibtex/281a50d812dd8a126008654d77ed7d717/ijtsrd}, interhash = {6b857e188f8e80ae68ba769f580d9a9f}, intrahash = {81a50d812dd8a126008654d77ed7d717}, issn = {2456-6470}, journal = {International Journal of Trend in Scientific Research and Development}, keywords = {231 COST Field Generalized Hata Model Network Neural Okumura Regression Strength}, language = {English}, month = mar, number = 3, pages = {467-471}, timestamp = {2020-06-10T08:17:13.000+0200}, title = {A Generalized Regression Neural Network Approach to Wireless Signal Strength Prediction }, url = {https://www.ijtsrd.com/computer-science/artificial-intelligence/30501/a-generalized-regression-neural-network-approach-to-wireless-signal-strength-prediction/finangwai-d-jacob}, volume = 4, year = 2020 }