# Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion
## 1 Introduction
These tasks can be classified as supervised learning and unsupervised learning. With labeled data, the goal of supervised learning is to learn a general and accurate function between inputs and outputs, which makes it suitable for solving classification and regression problems. On the other hand, unsupervised learning algorithms have to describe the hidden structure from unlabeled data. In essence, path loss prediction is a supervised regression problem, so it can also be solved by supervised machine learning algorithms, such as artificial neural network (ANN), support vector regression (SVR), and decision tree. It has been reported that the machine-learning-based models are more accurate than empirical models and more computational efficient than deterministic ones [3; 6]. Among these machine-learning-based models, ANN, especially back propagation neural network (BPNN), has been widely used for path loss prediction. In [3], ANNs with different hidden neurons and different training algorithms were analyzed based on measured data collected in a rural environment. It was indicated that more complex ANNs do not considerably increase the prediction accuracy. In [7], a path loss prediction model based on BPNN was proposed for railway environment and it had good prediction accuracy and generalization ability for similar scenarios. In [8], a pure ANN system and a hybrid prediction system were designed for urban and suburban environments. It was illustrated that the ANN modeling approach provided more accurate prediction of field strength loss than that of COST231-Walfisch-Ikegami model. In [5], an ANN-based model was designed to predict the path loss values for heterogeneous networks, in which several frequencies and different environments including urban, suburban, and rural scenarios are considered. Compared with empirical models, the results showed that ANN performed well in terms of precision but with a slight increase of processing time and memory consumption. For indoor environments, a multilayer perceptron and a generalized regression neural network were proposed in [9] at the frequency of 1890 MHz, which showed a good agreement with the measurements. A propagation model using BPNN was developed within multi-wall and multi-frequency scenarios in [10]. In [11], parameters related to body shadowing and furniture effects were added to inputs and the proposed ANN model demonstrated high performance compared to empirical model and measurements. Besides BPNN, radial basis function neural network (RBF-NN) [12], dynamic neural networks (DNN) [13], and wavelet neural network [14], were also employed for path loss prediction. Recently, Neuro-Fuzzy draw great interests in the path loss prediction because of its transparency [15; 16]. SVR was used for the prediction of radio-wave path loss values in suburban environments in [17; 18]. Some algorithms, including genetic algorithms (GA) and tabu search (TS), were applied to select important parameters for SVR-based predictors. In [19], a SVR-based modeling method was presented to predict in-cabin path loss values at 3520 MHz, outperforming the curve fitting model. In [20], a propagation loss prediction model was built on the basis of SVR and it was able to achieve a good accuracy at the price of an acceptable computational cost. Other machine learning algorithms such as decision tree and K-Nearest-Neighbors (KNN) were also employed for path loss prediction. In [21], random forest (RF) and KNN are exploited to predict the path loss in an urban environment for UAV communications. Results have shown that machine-learning-based models have high prediction accuracy and acceptable computational efficiency. Besides, feature importance is assessed by using RF algorithm. In [22], a hybrid scheme based on the ray tracing method and RF was presented for the field strength prediction. In contrast with the results of the finite integral method, the proposed model achieved higher prediction accuracy with less computation time.