# 5G Network Deployment Planning Using Metaheuristic Approaches
###### Abstract  
The present research focuses on optimizing 5G base station deployment and visualization, addressing the escalating demands for high data rates and low latency. The study compares the effectiveness of Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Grey Wolf Optimizer (GWO) in both Urban Macro (UMa) and Remote Macro (RMa) deployment scenarios that overcome the limitations of the current method of 5G deployment, which involves adopting Non-Standalone (NSA) architecture. Emphasizing population density, the optimization process eliminates redundant base stations for enhanced efficiency. Results indicate that PSO and GA strike the optimal balance between coverage and capacity, offering valuable insights for efficient network planning. The study includes a comparison of 28 GHz and 3.6 GHz carrier frequencies for UMa, highlighting their respective efficiencies. Additionally, the research proposes a 2.6 GHz carrier frequency for Remote Macro Antenna (RMa) deployment, enhancing 5G Multi-Tier Radio Access Network (RAN) planning and providing practical solutions for achieving infrastructure reduction and improved network performance in a specific geographical context. capcaption = 0.95 2024 2  
tenuation, and improve overall network connection, thereby enabling seamless, high-speed connectivity in the 5G era. Furthermore, the deployment of 5G networks, utilizing millimeter wave frequencies, introduces novel topologies and ambitious goals for download speeds. With a focus on supporting up to 1 million devices per square kilometer, 5G emphasizes the enhancement of mobile broadband services, particularly for indoor users who constitute a significant portion of network users. Innovations like massive Multiple-Input Multiple-Output (MIMO), distributed antenna systems, spatial modulation, mm-Wave tech, and small cells with heterogeneous network deployments are explored to meet these objectives [4]. The deployment of 5G networks at mm-Wave frequencies faces unique challenges, primarily due to the physical characteristics of mmWave signals. These frequencies, while capable of delivering high data rates, have shorter ranges and are more susceptible to attenuation from obstacles like buildings and trees. This necessitates a denser network of base stations to ensure comprehensive coverage, especially in urban environments. The deployment strategy must, therefore, consider the urban landscape, identifying optimal locations for base stations that maximize coverage and signal quality while minimizing interference and cost. In Nepal, the optimal method for 5G deployment currently involves adopting a Non-Standalone (NSA) architecture, which overlays a 5G radio access network (RAN) on existing 4G infrastructure. This approach minimizes upfront costs by utilizing already deployed infrastructure while enabling a faster rollout of 5G services [5, 6]. However, NSA relies on the existing 4G core network, which may limit the full realization of 5G's standalone capabilities until a later phase. To address this challenge, our study emphasizes the significance of metaheuristic algorithms in optimizing the placement of new 5G base stations, while also optimizing the use of spectrum resources, thereby enhancing the efficiency and effectiveness of 5G deployments in Nepal's diverse geographic and economic landscape. The focus of this study is on the optimization of 5G base station deployment at mmWave frequencies. The aim is to determine an optimal placement strategy that maximizes coverage, minimizes interference, and ensures efficient utilization of available spectrum resources. The primary emphasis of this research lies in maximizing data rates and coverage while minimizing the deployment cost of multi-tier remote radio units. The main contributions of this paper are summarized as follows:  
* Conduct a detailed comparative study of various optimization algorithms, including GA, PSO, Simulated Annealing (SA), and Grey Wolf Optimizer (GWO), in the context of 5G Radio Access Network (RAN) planning.