## 9 A Novel Methodology for 5G-Enabled Broadband Connectivity Coverage of Smart Cities  
Smart city applications are deployed in topologies with many buildings of varying heights and construction materials, with mobile users wishing to connect to the services offered by each municipality [36; 37; 38]. Wireless propagation in such environments is hindered by both large-scale and small-scale fading, whereas channel state information is not known beforehand but requires assumptions within a certain probabilistic range (semideterministic channel prediction) based on measurements and other empirical data. All these mobile users, either in an outdoor environment or in their apartments, require a broadband connection, the minimum value of which is set by the ITU at 2.048 Mbps [5]. This is set as the essential minimum rate for each user connecting to the municipal services. This means that when there is a large accumulation of users (i.e., an increased density of users wishing to connect to the services), congestion occurs due to the increased telecommunication traffic. In the case of wireless networking, the distribution of broadband connectivity to users within range who wish to connect to the smart city's broadband infrastructure is of particular importance. It is therefore imperative to establish a reliable method for the prediction and calculation of the signal-to-noise ratio (SNR) in order to provide a robust metric for the calculation of the total bandwidth connectivity rate offered by any given antenna in the urban area [36]. The maximum reach of any given antenna, that is, the cell radius, is determined by the minimum broadband rate of 2.048 per device. This means that the average (expected) number of devices per cell needs to be estimated. If empirical data exist, then the number of devices becomes the basis for all further calculations. In any case, the local mean SNR (the SNR at each point of interest) needs to be calculated in a reliable, albeit empirical, manner [37]. This requires the calculation of noise, which in our case is already provided from the empirical data derived from extensive measurements that we performed, and the signal power (i.e., local mean value) is the objective of this section. To achieve the reliable calculation of the local mean received power, validated RF models were employed, adjusted for use in the 3.5 GHz channel. The idealistic free space path loss model was employed first, since it serves as a reference path model for all subsequent calculations [38]. The basic radio propagation mechanism is described mathematically by Friis' formula and is based on the physical concept of the propagation of EM radiation as an ideal spherewith an ever-increasing radius in space. As the radius of this ideal sphere increases (i.e., the distance between the transmitter, which is assumed to be located at the center of the ideal sphere, and the receiver, which is located on the surface of the sphere), the radiation density per unit volume (J/m\({}^{3}\)) decreases [39]. If the speed of the propagation of the EM radiation (i.e., the rate of increase of the sphere) equals the speed of light, then the rate of increase of the sphere leads to a decrease in the power density per unit area (W/m\({}^{2}\)). In this way, the inverse square law in [PERSON] formula is explained, which in its logarithmic form is expressed as follows [39]:  
\[PL(d)=32.45+20\log_{10}f(MHz)+20\log_{10}d(km) \tag{1}\]  
The logarithmic formulation of [PERSON] formula, also known as the free space model, calculates (in dB) the mean path loss as a function of the distance between the transmitter and the receiver (in km) and the operating frequency of the system. Knowing the mean path loss and the total radiated power from the transmitter (effective isotropic radiated power (EIRP)), we can calculate (in dBm) the local mean received power. Equation (1) calculates the mean path loss as a function of distance expressed in kilometers.