## 1 Introduction
In contrast, millimeter bands can deliver higher data rates of the order of multi-Gbps but have limited coverage [22] and are suitable for highly dense urban areas. Due to the small coverage, there is a need for more RRUs to be installed, which will be costlier and require higher CAPEX, IMPEX, and OPEX. A trade-off between capacity and coverage can be achieved using both millimeter-band and mid-band frequencies, such as the sub-6 GHz band. Proper planning, preliminary preparations, and choice of suitable frequency bands based on the population density are required to meet data rate requirements, comprehensive coverage, and QoS requirements. In Nepal, 5G has not yet been deployed. There is already considerable investment in deploying 4G infrastructure. 5G greenfield deployment will incur a large amount of CAPEX, and there is a significant amount of savings if we optimize the 5G BTS, keeping the existing 4G BTS in the same positions and ensuring the required QoS. In this paper, we study 5G greenfield deployment optimization and analyze the impact of 5G SA deployment leveraging the existing 4G infrastructure. We propose a novel mixed cell approach to deploy both macro-RRU (RRU\({}_{\text{M}}\)) and micro-RRU (RRU\({}_{\text{m}}\)) appropriately based on the demand of data requirement and population distribution for the 5G network. The main objective is to find the optimum placement of RRU\({}_{\text{M}}\) and RRU\({}_{\text{m}}\) to meet data rate requirements and ensure all users' coverage. Due to the smaller area coverage of RRU\({}_{\text{m}}\) using millimeter wave, there is a need for a massive number of RRU\({}_{\text{m}}\), resulting in excessive investment then strategically positioning RRUs in areas with high demand while minimizing overlap, can reduce cost. We first calculate the link budget for 3.5 GHz and 28 GHz simultaneously and throughput for both frequency bands. We initially implemented a random distribution of RRU\({}_{\text{m}}\) and RRU\({}_{\text{M}}\) based on the calculated link budget. We implemented PSO [23], GWO [24], WOA [25], MPA [26], SSA [27], and ALO [28] and carried out a comparative study for optimum placement of RRU\({}_{\text{m}}\) and RRU\({}_{\text{M}}\). We optimized 5G RRUs placement without relocating the existing 4G RRUs using metaheuristic algorithms for 5G SA deployment. Then, we analyzed the impact of using the existing 4G infrastructure with 5G greenfield deployment. The proposed approach ensures 98%coverage and an average data rate of 50 Mbps. To achieve the capacity and coverage requirement, we summarize our contributions as follows:  
* We performed appropriate system parameter definition and user service requirement for frequency bands FR1 (3.5 GHz) for macro-RRU (RRU\({}_{\text{M}}\)), and FR2 (28 GHz) for micro-RRU (RRU\({}_{\text{m}}\)). * Next, we performed dimensioning for cell capacity and cell coverage requirements for the required QoS, determining the preliminary required number of RRU\({}_{\text{M}}\) and RRU\({}_{\text{m}}\). * We simulated two different use-case scenarios for 5G SA greenfield deployment (Case I) and 5G deployment using existing 4G infrastructure (Case II), and carried out a comparative analysis. The rest of this paper is organized as follows. Section 2 presents the background and related work. Section 3 describes the system model formulation. Section 4 discusses the experimental setup and evaluation. Section 5 discusses the outcome of the experimental results, while Section 6 concludes the paper.