## 4 Experimental Setup and Evaluation
### Experimental Setup  
We used MATLAB 2024a and the MATLAB toolbox to implement the defined algorithms running on a PC having 12 th Gen Intel(R) Core(TM) i7-1270P 2.20 GHz, 16.0 GB RAM, 64-bit operating system, x64-based processor, and Windows 11 Pro. We propose to conduct the experiments on two scenarios.  
Scenario I : We select an urban geographic area of 2 km \(\times\) 2 km (4 km\({}^{2}\)) to serve 10,000 UEs for simulation. In an urban area, we optimize the micro-RRU and macro-RRU locations for both the cases of 5G greenfield and 5G SA using existing 4G infrastructure. Figures 3 and 4 show both use cases for Scenario I.  
Scenario II : We choose an urban area of 4 km \(\times\) 4 km (16 km\({}^{2}\)) with a population density of about 5000 per km\({}^{2}\). The optimization is carried out for both 5G greenfield and 5G with existing 4G infrastructure. The performance evaluation is carried out for both cases in this scenario. Figures 5 and 6 show both cases of the initial deployment scenario with population distribution for Scenario II.  
Scenario I (Urban initial deployment scenario)  
Figure 4: Case II: Urban 5G with existing 4G.  
Figure 3: Case I: Urban 5G greenfield.  
After coverage dimensioning, we calculated the total throughput of \(\rm RRU_{m}\) and \(\rm RRU_{M}\) using Equation (16) and using parameter coding rate (948/1024), a bandwidth of 100 MHz for \(\rm RRU_{M}\), and using the parameter \(J=1\), \(v_{Layers}^{(j)}=1\), \(Q_{m}^{(j)}=6\) (64 QAM), \(f^{(j)}=1\), \(\mu=2\) (60 KHz SCS), \(OH^{j}=0.14\) and \(R_{max}=948/1024\) and with spectral efficiency of 12.63 bits/s/Hz, the total throughput per sector is 1246.73 Mbps. Similarly, for \(\rm RRU_{m}\), taking bandwidth of 200 MHz and SCS = 120 KHz and with a spectral efficiency of 12.467 bits/s/Hz, the total throughput per sector is 2.493 Gbps.  
After coverage dimensioning, we chose the appropriate values for the radius of RRUs and the number of RRUs for simulation. Table 4 shows the link budget outcomes and the number of RRUs required after coverage and capacity dimensioning. The minimum number of \(\rm RRU_{m}\) for the urban area after coverage dimensioning is maximum (uplink, downlink), i.e., maximum (30, 59) is 59. A maximum of the two values is chosen since 59 \(\rm RRU_{m}\) meet the requirement of coverage for both uplink and download and 30 \(\rm RRU_{m}\) does so for uplink only. Similarly, for the minimum number of \(\rm RRU_{M}\) after coverage dimensioning is 6 i.e., maximum (3, 6). The number of RRUs after coverage dimensioning satisfies the coverage constraints but does not satisfy capacity constraints. So the minimum number of \(\rm RRU_{m}\) and \(\rm RRU_{M}\) for the urban area after capacity dimensioning is 139 and 70, respectively. Using 139 \(\rm RRU_{M}\) is a waste of resources, and we chose 70 initial numbers of \(\rm RRU_{m}\) in an urban area (Scenario I). For Scenario II, i.e., suburban area, the minimum number of \(\rm RRU_{m}\) and \(\rm RRU_{M}\) required to meet coverage constraints are 236 and 22, respectively. To meet capacity constraints, the requirements of \(\rm RRU_{m}\) and \(\rm RRU_{M}\) are 18 and 35, respectively. To meet both constraints, we chose the number of \(\rm RRU_{M}\), which is 35 because choosing \(\rm RRU_{m}\) in the suburban area requires 236, which is a waste of resources in such a population distribution.  
Figure 5: Case I: Suburban 5G greenfield.  
Figure 6: Case II: Suburban 5G with existing 4G.