Path loss prediction in urban environment using learning machines and dimensionality reduction techniques

Abstract

Path loss prediction is a crucial task for the planning of networks in modern mobile communication systems. Learning machine-based models seem to be a valid alternative to empirical and deterministic methods for predicting the propagation path loss. As learning machine performance depends on the number of input features, a good way to get a more reliable model can be to use techniques for reducing the dimensionality of the data. In this paper we propose a new approach combining learning machines and dimensionality reduction techniques. We report results on a real dataset showing the efficiency of the learning machine-based methodology and the usefulness of dimensionality reduction techniques in improving the prediction accuracy.

Path Loss Prediction Based on Machine Learning Techniques: Principal Component Analysis, Artificial Neural Network, and Gaussian Process

“…The machine learning approach to path loss modeling is expected to provide a better model, which can generalize well the propagation environment since the model is being learned through training with the data collected from the environment. The prediction of propagation path loss is regarded as a regression problem, as stated in the literature [15][16][17]. In this context, path loss models have been developed by various supervised learning techniques such as support vector machine (SVM) [16,18], artificial neural network (ANN) [19][20][21][22], random forest [17], K-nearest neighbors (KNN) [17].…”

“…The prediction of propagation path loss is regarded as a regression problem, as stated in the literature [15][16][17]. In this context, path loss models have been developed by various supervised learning techniques such as support vector machine (SVM) [16,18], artificial neural network (ANN) [19][20][21][22], random forest [17], K-nearest neighbors (KNN) [17]. The authors of [19][20][21][22] provided path loss prediction using ANN models, which provide more precise estimation over the empirical models.…”

The Prediction of Propagation Loss of FM Radio Station Using Artificial Neural Network

“…Piacentini and Rinaldi stated that the neural network is effective and safe in the estimation of propagation path loss [8]. ANN is used successfully in path loss prediction [9].…”

A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

“…As an example, the inter-download times of video segments are predicted in [102], where the output sequences are the interdownload times of the already downloaded segments and the states are the instants of the next download request. ARIMA: [13], [38], [40], [46], [47], [54], [58], [59], [63], [100], [119] Kalman: [32], [ CF: [16], [134], [149] Cluster: [15], [34], [51], [117], [122], [123], [148], [156] Decision trees: [35], [98], [ Functional: [28], [29], [38], [64], [99], [104], [105] SVM: [51], [114], [139] ANN: [14], [48], [106], [ 2) Bayesian inference: This approach allows to make statements about what is unknown, by conditioning on what is known. Bayesian prediction can be summarized in the following steps: 1) define a model that expresses qualitative aspects of our knowledge but has unknown parameters, 2) specify a prior probability distribution for the unknown parameters, 3) compute the posterior probability distribution for the parameters, given the observed data, and 4) make predictions by averaging ove...…”

“…Pathloss prediction in urban environments is investigated in [48]. The authors propose a two-step approach that combines machine learning and dimensional reduction techniques.…”