An ensemble machine learning approach for enhanced path loss predictions for 4G LTE wireless networks

Abstract

Summary Accurate path loss prediction models are indispensable in modern wireless communication systems. In recent times, several path loss prediction models have been proposed to improve network performance. However, most of these models have not addressed the fundamental issues. The problem of deploying a single path loss prediction model that fits well in all wireless propagation environments remains. To address this problem, we present machine learning‐based ensemble methods to path loss predictions. Specifically, ensemble methods have been introduced to improve signal prediction accuracy and performance. Additionally, the radial basis function (RBF) and multilayer perceptron (MLP) neural network models were deployed and improved by adding more network parameters to their respective input layers. Results revealed that the RBF model with an increase in the number of centroids reduces the mean square error (MSE). Also, the Gaussian kernel function gives lower MSE compared to the multiquadric and inverse multiquadric functions. The bagging and blending ensemble path loss prediction models were introduced for optimal performance. An RBF neural network of different clusters was incorporated into the bagging algorithm as base learners. The RBF, MLP, blending, and bagging were examined using standard metrics for the training, testing, and validation dataset for model validation. The developed bagging ensemble path loss prediction model gave the lowest errors (MSE = 0.0011 dB, SSE = 0.6069 dB, MAE = 0.0245 dB, & R = 0.7484 dB) for the datasets. The bagging ensemble method acts as a variance and error reduction mechanism because it predicted path loss closest to measured data and is suitable for near precise path loss predictions.

Assessment and Test-case Study of Wi-Fi Security through the Wardriving Technique

“…It is important to note that the views of [99][100][101][102][103] about various connectivity issues can affect the density and the overall evaluation of a wardriving result based on the variability of several issues highlighted in this paper. e wardriving approach is used in this work to crawl wider regions for examination [104][105][106].…”

“…Because of variable connectivity issues, mobile AP devices (MiFi) may be ON and OFF a wardriving radar, resulting in inconsistent results and datasets at each run. e views expressed in the works [99][100][101][102][103] on various connectivity issues can affect the density and the overall evaluation of a wardriving result based on the variability of the above problems. However, current research endeavour using Artificial Intelligence (AI) and Machine Learning is addressing these issues [110][111][112][113].…”

Path Loss Prediction Based on Machine Learning Techniques: Support Vector Machine, Artificial Neural Network, and Multilinear Regression Model

“…Given the findings of this study, it is suggested that machine learning be used to estimate path loss because it is more effective and requires less data input. Robust ways for better tuning and selection of hyper-parameters resulting in optimal performance of ML-based systems are needed as future improvements to this Having looked at the previous works done by (Ojo et al, 2022), and the results from our work, we can say that using deep learning models is highly recommended for future predictions of path loss in open areas and urban centers, hence…”

“…Model for Path Loss (2.3) A weighted network of latent variables is learned using backpropagation by the ANN, a nonlinear regression system. The ANN model handles more dimensions than the look-up table method and outperforms the regression analysis model in terms of prediction performance (Ojo et al, 2022). When taking into account the intricate propagation due to variable heights and the complex distribution of structures in metropolitan settings, the nonlinear model can match with linear regression better.…”

“…The main reason for the selection of the ML algorithms is according to (Miau, n.d.;Ojo et al, 2022;Wang & Lei, 2022) they have performed well in different terrains.…”