# Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels
## 1 Introduction  
Mobile communication has proliferated since its debut due to its flexibility and convenience. Due to the continuous evolution of communication technologies and the exponentially increasing demand for higher mobile data traffic, research has been focused on the frequency regime above 6 GHz to overcome the congestion of the previous bands (below 6 GHz) and to cope with the requirements of the fifth-generation (5G) wireless system and other high-speed multimedia services [1, 2, 3, 4, 5]. Large-scale fading models play a vital role in optimizing base station deployments, estimating radio coverage, and characterizing the radio environment to quantify the performance of wireless communication systems [6]. Furthermore, efficient and reliable determination of crucial factors, such as the signal field strength, carrier-to-interference (C/I) ratio, and signal-to-noise ratio (SNR), can be achieved if in-depth knowledge ofpropagation loss is provided [7]. As a result, for network planning and implementation of wireless communication systems, radio propagation channel characteristics in various environments are required [8]. The performance of wireless communication systems is influenced by radio propagation in physical environments since the radio waves often experience fading. The wirelessly propagated signals from any communication system's transmitting antenna(s) suffer from attenuations over distances and frequencies, which are well-known as large-scale fading and small-scale fading. In addition, the signals also experience losses because of atmospheric conditions and surrounding physical objects, leading to multipath propagation since the receiving antenna(s) receives the signals mainly from reflections, diffractions, and scattering mechanisms [9; 10]. These multipath effects result in signal power fluctuation and increase the uncertainty of received signal power [11]. This work mainly focuses on developing large-scale path loss models that are crucial for estimating radio coverage, allocating frequencies properly, optimizing base stations, and identifying the most suitable antennas [6; 12]. Radio propagation models representing path loss are essential to ensure high-quality services and accurate signal coverage predictions in mobile communication networks. Accordingly, researchers have accelerated their efforts to provide reliable models for various environments and scenarios over a wide range of frequency regimes to assist network engineers in designing reliable future wireless networks and accurate link budget calculations. Moreover, accurate predictions could be beneficial in radio resource management schemes that aim to meet specific Quality-of-Service (QoS) criteria [13; 14; 15; 16; 17; 18; 19; 20]. Fundamentally, traditional approaches to path loss prediction modeling are deterministic, empirical, and stochastic. Deterministic path loss prediction models are site-specific and require sufficient information of the propagation's environments. These models are often related to 3-D map propagations, such as the ray-tracing models. Moreover, these deterministic models repeat their calculations when the environment changes; therefore, they have high computational complexity. The empirical path loss models are based on measurements and observations, such as the Hata model and the COST 231 model. These models are easier to apply; however, they are time-consuming since they require massive measurement campaigns in specific environments and communication scenarios. Further, in terms of prediction accuracy, these models provide less accuracy than deterministic models [6; 12; 21]. Many other well-known empirical path loss prediction models are derived based on the criteria of the minimum mean square error (MMSE) between the models and the measurement data to provide the best fit of these models as a function of the separation distance between the transmitting and receiving antennas in the logarithmic scale.