## 1 Introduction  
In a 5G communication network, mobile operators are forced to use a high-frequency spectrum due to the limited amount of channel bandwidth. The 5G boosts the overall performance and enhances user experience offered by the present generation mobile technologies. Some of the most hyped features include higher data-rates, lower end-to-end delays, and minimal energy consumption [1]. In order to meet a common goal, several technologies need to be implemented in a fashion that allows the smooth operation of the overall network as a singular body. Nevertheless, vast technologies and communication techniques are required to work in unison. An increment in demand of such an efficient system is deemed to attract massive numbers of users utilizing resources simultaneously, which in turn, requires both acquisition and utilization of the spectrum. Some exceptional candidate technologies that enable 5G to better accommodate a large number of users within the limited resource environment are, but not limited to, mmWave, massive multiple-input multiple-output (massive MIMO), multi-radio access technology (RAT), device-to-device (D2D) communication, and cooperative heterogeneous network (HetNet) [2; 3; 4; 5; 6; 7]. The requirement of achieving high reliability and low latency for many Internet of Things (IoT) uses is very critical. In this regard, for a new 5G use case, IoT applications are classified into two classes, i.e., massive machine-type communication (mMTC) and ultra-reliable low-latency communication. There are several practical applications that are possible for a 5G IoT network such as: (i) D2D communication without the need of a cellular network; (ii) real-time delay wireless link for medical robots which can help to do remote surgery; (iii) mMTC such as solar-powered streetlights to help citywide infrastructure, and many more. We know that the 5G networks are bound to hit the market soon worldwide [8], and we realize such technology demands simple, but effective and reliable channel models based on basic physics that also derives from extensive testing and measurement. These channel models aid development of various terms of establishing signaling protocols that ascertain efficient utilization of resources, while improving network performance [9]. Various groups have built their own channel models, such as the statistical spatial channel model NYUSIM by the New York University, COST 2100, 3 rd Generation Partnership Project (3 GPP), International Telecommunication Sector (ITU-R), WINNER II, as well as the Mobile and Wireless Enablers for the Twenty-Twenty Information Society (METIS) [10; 11]. Moreover, it is important to highlight that the entire frequency spectrum would not be used up. An average consumer would require centimeter-sized waves with spectrum licenses ranging from 3 to 30 GHz, as well as between 30 and 40 GHz (up to 300 GHz) as a mmWave spectrum, initially [12; 13]. There is also spectrum sharing that ranges from 60 to 70 GHz for mission-critical services [14], which includes smart city infrastructure, healthcare, self-driving cars, and many other applications. Such services need a continuous high-speed and low-latency connection, and therefore the shared spectrum is key enabler and ensures that these devices are always connected [15]. For example, termination is absent for the connection between a cellular tower and a self-driving car because some other user may place a call on the same spectrum. Consumer usage is a factor that is emphasized when designing shared spectrum. However, enabling mmWave systems in an ultra-dense network may address issues related to the channel impairments and the propagation range. Nonetheless, some issues need to be mitigated prior to practical implementation. For example, mmWave suffers from high penetration losses that cause a lower transmission range [16].