## 1 Introduction  
The wireless communication field is one of the most successful and rapidly expanding industries due to the rising demand for mobile devices, which is followed by network expansion. By the end of 2025, it is estimated that 20 billion devices will be connected to the mobile network, increasing the amount of wireless data traffic [1, 2]. Wireless data transfer has also increased as a result of the development of the Internet of Things (IoT), which includes applications for smart cities, smart health care, smart forming, climate monitoring, intelligent transportation, etc. These applications require very high data rates and large bandwidths to design stable and reliable wireless networks [3, 4, 5]. Therefore, the fifth-generation (5G) wireless networks with millimeter wave (mmWave) frequency (3 GHz-300 GHz) is considered to meet these demands. The mmWave spectrum provides a huge number of underutilized spectrum bands, which will provide high data rates for the expansion of 5G wireless networks. The underused spectrum bands of mmWave offer a great opportunity to extend the coverage capacity and improve the quality of service [6, 7, 8].  
Several studies found that mmWave frequencies have implementation problems, especially with relation to the path loss (PL) and received power (RP) imposed by various parameters such as weather and atmospheric conditions and obstacles in the surroundings [9]. Along with this, the propagating signal is affected by antenna height, location,and type of antenna [10; 11; 12; 13]. Received signal power in a wireless communication system depends on the interference between the signals, spectrum allocation, spectrum efficiency, etc. [14; 15]. Therefore, an accurate estimation of signal power and propagation loss is required to design a modern 5G wireless communication system. Analysis of path-loss models at mmWave frequencies is of the highest priority in order to determine the ideal location of 5G base stations (BS). Many researchers and engineers have developed various mmWave propagation models, which include (i) the 5G channel model (5 GCM), the 3 rd Generation Partnership Project (3 GPP), mobile and wireless communication enablers for the twenty-twenty information society (METIS), and millimeter wave-based mobile radio access networks or 5G integrated communication (mmMAGIC) [16; 17; 18; 19].  
In this paper, the effectiveness of different existing propagation models such as 5 GCM, 3 GPP, METIS, and mmMAGIC at mmWave frequencies between 1 and 100 GHz are considered and compared. The urban micro (UMi), urban macro (UMa), and rural macro (RMa) environments were taken into consideration for line-of-sight (LOS) and non-line-of-sight (NLOS) situations. Because the urban microcellular network provides low latency, high capacity, improved coverage, and improved network reliability, urban and rural macrocellular networks provide extended coverage, improved energy efficiency, and cost effective deployments. Out of these three cellular networks, specific deployment strategies can be selected based on factors such as population density, geographic characteristics, and infrastructure availability. The main goals of this paper are to determine the path loss and received power in urban and rural environments, as well as to estimate the PL and RP at different mmWave frequencies using the various path-loss models that are currently available. An optimized path-loss model can be selected based on the estimated path loss and received power in a given environment. The selected optimized model can be used by the service providers to enhance their network capacity and coverage and energy efficiency. The remainder of the paper is structured as follows: Section 2 explains related work, Section 3 discusses path-loss models, Section 4 contains the results and discussions, and Section 5 contains the conclusion.