# 5G Network Deployment Planning Using Metaheuristic Approaches
## 2 Related Works  
The research highlights the strategic use of 5G millimeter-wave frequencies, particularly focusing on indoor mmWave small cell network planning between 26 GHz and 38 GHz, employing Close-In (CI) and CI model with a frequency-weighted path loss exponent (CIF) path loss model along with 3D ray-tracing methods for optimal path loss modeling [7, 8, 9, 10, 11]. These studies emphasize the need for precise path loss models that can predict network coverage effectively across various urban environments, terrains, and generational technology shifts [12, 13].  
Enhancements in 5G coverage have been demonstrated through the use of simulation tools like the Vienna 5G simulator, which address bandwidth limitations and improve network efficiency by optimizing base station placements [14; 15; 16; 17]. The integration of machine learning techniques, particularly Random Forest algorithms, has proven effective in both line-of-sight and non-line-of-sight scenarios, optimizing coverage predictions and network planning [18; 19].  
Robust optimization techniques using multi-layer architectures enhance network resilience and adaptability, incorporating advanced strategies such as game theory for addressing ultra-dense network deployments in both 5G and future 6G scenarios [20; 21]. The rapid advancements facilitated by machine learning algorithms have been speeding up the planning processes, achieving up to 15 times faster results compared to traditional methods [22].  
Furthermore, the paradigm shift towards data-driven network management through network slicing and deep reinforcement learning has opened new avenues for managing network capacities and life-cycle complexities [23; 24; 25]. Despite substantial advancements, there remains a noticeable gap in integrating these sophisticated technologies into a cohesive framework that effectively manages both urban and rural deployment challenges. Our research fills this gap by utilizing a novel integration of metaheuristic algorithms with advanced path loss models to enhance both the accuracy and efficiency of network deployment strategies [26; 27; 28; 29].  
The paper further explores novel optimization techniques like PSO for base station deployment, which have shown to improve coverage significantly and reduce user outages, thereby enhancing the overall network performance [30; 31; 32; 33; 34]. These innovative approaches underscore the necessity for continuous research and adaptation in the face of evolving technological and operational demands, paving the way for more resilient and efficient 5G networks [35; 36; 37; 38; 39].  
Moreover, enhanced strategies such as Smart Simulated Annealing (SSA) and Swarm Intelligence (SI) are optimizing the placement of base stations for better coverage and capacity, while beam-forming technologies in 5G networks are streamlining the number of required base stations, providing increased network capacity and efficiency [40; 41; 42; 43; 44]. These advanced methodologies are crucial for supporting the high demands of modern mobile networks, offering reliable solutions that cater to both current and future connectivity needs [45; 46; 47; 48; 49; 50].  
While refs. [29; 38; 40] evaluate frequencies and offer a broader perspective on the impact, they do not focus heavily on specific recommendation and deployment scenarios. Our study highlights a specific urban area (Thapathali, Kathmandu), providing a case study that showcases the application and benefits of metaheuristic algorithms in a real-world setting. It also provides a more detailed comparison of metaheuristic algorithm within the context of urban deployment.