Improvement of QoS of outdoor prediction models using tuning and machine learning approach in fringe areas. (3rd September 2021)

• Record Type: Journal Article
• Authors: Gupta, Akansha; Ghanshala, Kamal; Joshi, R. C.
• Is Part Of: International journal of modelling & simulation. Volume 41:Number 5(2021)
• Page Start: 359
• Page End: 375
• Publication Date: 2021-09-03
• Subjects: Machine learning -- propagation loss -- RSSI -- radio propagation -- tuning -- KNN -- SVM; Mathematical models -- Periodicals; Simulation methods -- Periodicals; Mathematical models; Simulation methods; Periodicals; 003.3
• DOI: 10.1080/02286203.2021.1983078
• Languages: English
• ISSNs: 0228-6203

Abstract

ABSTRACT: Artificial Intelligence is used to increase the prediction accuracy of the empirical propagation models by considering multivariate parameters in radio path. The mean absolute error between the empirical path loss models and path loss from the measured data at 1800 MHz has been compared. Tuning of the best fit model is done by incorporating correction factors due to local terrain conditions. Lee's model shows the minimum RMSE and is tuned further. Measurement is conducted at the fringe area of Uttarakhand, India, but due to terrain conditions, it is not possible to collect signal strength at mountains, deep valleys, and dense forests. In this paper, tuning and machine learning is used to predict the signal strength at the missed point and thus improves the predicting accuracy and QoS of propagation models. A multivariate machine learning classifier approach is applied, and KNN shows a maximum area under the curve (AUC) of 0.99.