Path Loss Prediction in Tropical Regions using Machine Learning Techniques: A Case Study

Abstract

In optimization of wireless networks, path loss prediction is of great importance for adequate planning and budgeting in wireless communications. For efficient and reliable communications in the tropics, determination or estimation of channel parameters becomes important. Research for this article employed different machine learning techniques—AdaBoost, support vector regression (SVR), and back propagation neural networks (BPNNs)—to construct path loss models for Akure metropolis, Ondo state, Nigeria. An experimental measurement campaign was conducted for three different broadcasting stations (Ondo State Radiovision Corporation (OSRC), Orange FM, and FUTA FM) all situated within Akure metropolis. Furthermore, we designed machine learning-based models for path loss prediction at various observation points at a particular frequency, and demonstrated how these algorithms agree with the measured data. For instance, for OSRC (operating at 96.5 MHz) measurement, the RMSEs (root mean square errors) of AdaBoost, SVR, BPNN, and the classical model (log-distance model) predictors were 4.15 dB, 6.22 dB, 6.75 dB, and 1.41 dB, respectively. Additionally, path loss prediction at a new frequency according to the available data at specific frequencies was evaluated. In order to resolve the challenge of limited or insufficient samples at a new frequency, a framework hybridizing classical models and machine learning algorithms was developed. The developed framework employs estimated values that are computed by the classical model based on the prior information for the training set expansion. Performance evaluation of the framework was conducted using measured data of Orange FM (94.5 MHz) and FUTA FM (93.1 MHz), and the samples computed from the classical model were used as training datasets for path loss prediction at a new frequency. RMSEs of AdaBoost, SVR, BPNN, and log-distance predictors were 1.77 dB, 1.52 dB, 1.45 dB, and 2.61 dB, respectively. However, adding measured data generated by the classical-based model, the RMSEs of AdaBoost, SVR, BPNN, and log-distance algorithms were 1.81 dB, 1.63 dB, 1.45 dB, and 1.88 dB, respectively. The results demonstrate how the proposed sample expansion framework enhances prediction performance in the scenario of few measured data at a new frequency. Finally, these results are promising enough for the deployment of the proposed technique in practical scenarios.