# Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion
## 1 Introduction  
Radio wave propagation plays an important role in the research and development of wireless communication systems. The wireless signal strength decreases as the distance between the transmitter and receiver increases. Moreover, the mechanisms of the electromagnetic wave propagation are diverse and can be generally classified as reflection, diffraction, and scattering [1]. The complex propagation environment makes the prediction of the received signal strength a very hard problem. Path loss is used to describe the attenuation of an electromagnetic wave as it propagates through space [2]. An accurate, simple, and general model for the path loss is essential for link budget, coverage prediction, system performance optimization, and selection of base station (BS) locations. Consequently, researchers and engineers have made great efforts to find out reasonable models for the path loss prediction in different scenarios and at different frequencies. Many measurement campaigns have been conducted worldwide to collect data, which have been used to build, adjust, and evaluate these models. The upcoming fifth-generation (5G) networks are designed to support increased throughput, wide coverage, improved connection density, reduced radio latency, and enhanced spectral efficiency. Supporting Internet of Things (IoT) applications will involve vast coverage areas and various terrains. Besides, new frequency bands like the sub-6 GHz and millimeter wave bands, will be exploited to provide wide bandwidths. Numerous measurement campaigns and drive tests have to be carried outto collect attenuation data at the new frequencies. The network planning of 5G mobile communication systems will confront severe challenges, and preferable models are required for the path loss prediction. Traditionally, path loss prediction models have been built based on empirical or deterministic methods [3]. Empirical models mainly rely on measurements in a given frequency range and a specific scenario. They provide statistical descriptions of the relationship between path loss and propagation parameters such as frequency, antenna-separation distance, antenna heights, and so on. For example, the log-distance model [4] uses the path loss exponent, which is determined empirically, to characterize how the receiver power falls off with the antenna-separation distance. A Gaussian random variable with zero mean is used to depict the attenuation (in decibel) caused by the shadow fading. Other typical empirical models include the Bullington, Egli, Longley-Rice, Okumura, and Hata models [2]. Empirical models are quite simple because few parameters are required and model equations are concise. However, parameters of empirical models are extracted from measured data in a specific scenario. Thus, their accuracy may be unsatisfactory when these models are applied to more general environments [5]. At the same time, empirical models can only represent the statistics of the path loss at a given distance, but they cannot give the actual received power at a specific location. Deterministic models, such as models based on ray tracing and finite-difference time-domain (FDTD), apply radio-wave propagation mechanisms and numerical analysis techniques for modeling computational electromagnetics. In general, they can achieve high accuracy and provide the path loss value at any specific position. However, their disadvantages include the lack of computational efficiency and therefore prohibitive computation time in real environments. Site-specific geometry information and dielectric properties of materials are also required. Moreover, we have to run the time-consuming computation procedure again once the propagation environment has changed. Machine learning is a method based on an extensive dataset and a flexible model architecture to make predictions. Recently, machine-learning-based methods have been used in self-driving cars, data mining, computer vision, speech recognition, and many other fields.