# Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels
## 1 Introduction
They include the single-frequency close-in (CI) free space reference distance model and the single-frequency floating-intercept (FI) model [22; 23; 24; 25; 26; 27; 28; 29; 30]. Stochastic path loss models have probability distributions and assumptions to be considered in the models' equations. These models suffer from limited precision because of some mathematical expressions since the communications environments are considered random variables [6]. Multi-frequency path loss prediction models are receiving more attention from some researchers in recent times to develop accurate and stable path loss models for future wireless systems over a wide range of frequency regimes [6; 31]. However, these models face the same problems mentioned above as the single-frequency models. There are two main problems in using the previous empirical models: the first problem is that these models are accomplished by a large amount of measurement in a certain environment to obtain a specific model that works for a particular environment at a specific frequency band, which is obviously time-consuming. The second problem is the limited prediction accuracy (the models do not fit the measurement data with a deficient prediction error) provided by these log-distance path loss models in some specific regions. Moreover, the use of the traditional linear models for predicting path loss is not sufficient to capture the path loss behavior accurately in higher frequency bands that are adopted to cope with the emerging demands of new wireless technologies. Accordingly, innovative methods that provide reliable modeling and prediction of the wireless propagation channels are highly needed, especially for complex environments of radio wave propagation that have a severe influence on the quality of wireless communication systems. Machine learning (ML) is a set of approaches for making predictions based on datasets and modeling algorithms. ML-based methods are now used in various disciplines, including speech recognition, image identification, natural language processing, and computer vision. In many telecommunication fields, the research based on ML of various topics such as propagation loss prediction, channel decoding, signal detection, and channel estimation has already made significant progress [32; 33; 34]. All ML methods rely on the type of information (input features) that is used for the training. ML methods can be classified as supervised learning and unsupervised learning. For classification or regression issues, supervised learning is used to learn a function or relationship between inputs and outputs. Unsupervised learning, on the other hand, is the process of extracting hidden rules or connections from unlabeled data. Path loss prediction can be viewed as a supervised regression problem that ML methods can handle [11; 35; 36; 37; 38]. Path loss prediction models based on machine learning algorithms are promising to overcome the time consumption in traditional linear path loss models that depend mainly on measurement campaigns at new frequency bands in specific outdoor and indoor environments and communication scenarios and/or simulation-based methods, such as ray-tracing techniques [6]. ML-based algorithms have been successfully used to assist to predict the path loss in several operating environments [6; 7; 11; 13; 21; 36; 37; 38; 36; 35; 37; 36; 38]. Furthermore, unlike traditional models, ML-based path loss prediction models can provide reliable generalizations on the propagation environment [42]. There are many distinct types of machine learning algorithms, each with its own structure. Our objective is to see if these models can offer reliable prediction results at mmWave frequencies for a specific environment, e.g., a typical indoor corridor that can be viewed as an air-filled rectangular waveguide with huge dimensions compared to the wireless signals' wavelength [57].