## 4 FWA Design Considerations, Challenges, and Solutions
### Envisaged 5G Deployment Spectrum
#### 4.1.1 High Path Loss  
Effective system designs, power budget calculations, interference/coverage predictions, as well as capacity estimation of evolving ultrawideband wireless networks demand depth perception of the associated propagation impairments that are likely to affect the free-space links [252]. One such notable impairment is path loss. The path loss defines the manner in which the received signal power decreases in accordance with an increase in the distance between the transmitting and receiving nodes. Furthermore, it is highly contingent on the nature of the environment where the network is being deployed [273; 274; 275]. It should be noted that, as the free-space path loss is inversely proportional to the square of the wavelength, \(\lambda\) (i.e., \(FSPL=[(4\pi d)/\lambda]^{2}\)[252; 276]), a signal at higher band experiences more propagation loss compared with lower band counterpart [267]. For instance, as illustrated in Figure 13, the path loss in a dense urban setting over a 10 km distance at 28 GHz is about 24 dB higher than that at 3.5 GHz for the same distance. Furthermore, for the same setting at 28 GHz, the path loss over a 100 km distance is about 32 dB higher than at a 10 km distance. Figure 13: Free-space path loss for different settings. Moreover, the path loss for 3G and beyond networks that are functioning at a frequency above 2 GHz can be evaluated using the Stanford Univesity Interim (SUI) model for IEEE 802.16e systems [252; 275]. In this context, the SUI-based path loss model, \(\alpha_{\text{SUI}}\), in dB, can be defined as [252; 275; 276]  
\[\alpha_{\text{SUI}}(d)[\text{dB}]= \varrho[f,d_{0}\ (\text{m})](\text{dB})+10\mathcal{P}\log_{10} \biggl{(}\frac{d}{d_{0}\ (\text{m})}\biggr{)}\] \[+ X_{f_{c}}+X_{\text{CPE}}+X_{\sigma},\quad d>d_{0} \tag{2}\]  
where \(\varrho[f,d_{0}\ (\text{m})]\) is the free-space path-loss in dB at a close-in reference range \(d_{0}\); \(X_{\sigma}\) is a typical log-normal random shadowing variable with mean 0 dB and standard deviation \(\sigma\) denoted by \(8.2<\sigma<10.6\) dB; \(\mathcal{P}\) represents the path loss exponent; \(X_{\text{CPE}}\) and \(X_{f_{c}}\) represent the adjustment factors for the CPE antennas heights and the corresponding operating frequency at above 2 GHz, respectively; and \(f_{c}\) denotes the carrier frequency. Furthermore, \(\varrho[f,d_{0}\ (\text{m})]\), \(\mathcal{P}\), \(X_{f_{c}}\), and \(X_{\text{CPE}}\) can be defined, respectively, as  
\[\varrho[f,d_{0}\ (\text{m})](\text{dB})= 20\log_{10}\biggl{(}\frac{4\pi d_{0}f}{3\times 10^{8}}\biggr{)} \tag{3a}\] \[= 32.4+20\log_{10}\biggl{(}f_{\text{GHz}}\biggr{)}\] (3b) \[\mathcal{P}= a-bh_{\text{TX}}(\text{m})+\frac{e}{h_{\text{TX}}(\text{m})}\] (3c) \[X_{f_{c}}= 6\log_{10}\biggl{(}\frac{f_{\text{MHz}}}{2000}\biggr{)},\ f\ >2\ \text{GHz}\] (3d) \[X_{CPE}= \left\{ \begin{array}{ll}-10.8\log_{10}\biggl{(}\frac{h_{\text{ CPE}}(\text{m})}{2}\biggr{)},&\text{for}\ \mathcal{Y}\\ -20\log_{10}\biggl{(}\frac{h_{\text{CPE}}(\text{m})}{2}\biggr{)},&\text{for} \ \mathcal{Z}\end{array} \right. \tag{3e}\]  
where \(h_{\text{CPE}}\) and\(h_{\text{TX}}\) denote the CPE and TX antenna heights in meters, respectively; \(f_{\text{GHz}}\) and \(f_{\text{MHz}}\) are the frequencies in MHz and GHz, respectively; \(\mathcal{Y}\) denotes terrain types A and B while \(\mathcal{Z}\) represents terrain type C; and parameters \(a\), \(b\), and \(e\) represent constants used in modeling typical physical terrain scenarios that could be experienced in the service area. The related values of the constants are listed in Table 9[252; 275; 276]. Furthermore, note that terrain A represents scenarios with the maximum path loss and can be used for modeling a hilly environment with very dense vegetation. Consequently, it is appropriate for urban area modeling. Furthermore, Terrain B denotes a scenario with moderate (intermediate) path loss and it is suitable for modeling flat terrains with hilly and rare vegetation. Based on this, it can be employed for suburban area modeling.