5G Network Deployment Planning Using Metaheuristic Approaches
Binod Sapkota,
Rijan Ghimire,
Paras Pujara,
Shashank Ghimire,
Ujjwal Shrestha,
Roshani Ghimire,
Babu R. Dawadi,
Shashidhar R. Joshi
Affiliations
Binod Sapkota
Department of Electronics and Computer Engineering, Pulchowk Campus, Institute of Engineering, Tribhuvan University, Kathmandu 19758, Nepal
Rijan Ghimire
Department of Electronics and Computer Engineering, Thapathali Campus, Institute of Engineering, Tribhuvan University, Kathmandu 19758, Nepal
Paras Pujara
Department of Electronics and Computer Engineering, Thapathali Campus, Institute of Engineering, Tribhuvan University, Kathmandu 19758, Nepal
Shashank Ghimire
Department of Electronics and Computer Engineering, Thapathali Campus, Institute of Engineering, Tribhuvan University, Kathmandu 19758, Nepal
Ujjwal Shrestha
Department of Electronics and Computer Engineering, Thapathali Campus, Institute of Engineering, Tribhuvan University, Kathmandu 19758, Nepal
Roshani Ghimire
Department of Electronics and Computer Engineering, Advanced College of Engineering and Management, Institute of Engineering, Tribhuvan University, Kathmandu 19758, Nepal
Babu R. Dawadi
Department of Electronics and Computer Engineering, Pulchowk Campus, Institute of Engineering, Tribhuvan University, Kathmandu 19758, Nepal
Shashidhar R. Joshi
Department of Electronics and Computer Engineering, Pulchowk Campus, Institute of Engineering, Tribhuvan University, Kathmandu 19758, Nepal
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.
