Signal-to-Noise Ratio Optimization in 5G Network Architectures using the Pelican Optimization Algorithm

Abstract

Modern fifth-generation mobile networks face significant challenges due to increasingly densely populated locations, which result in limited coverage and lower performance due to congestion, interference bottlenecks, and service quality variations. The introduction and operation of 5G technology presents issues as a viable option for connecting highly populated areas. A unique 5G network structure must be devised to serve highly populated locations, as it will optimize coverage while expanding capacity, as well as improve interference control and service delivery. POA is well-suited for solving complicated optimization issues with more computational efficiency, lowering processing time or resource utilization, and thereby improving complex data management. The performance study revealed that implementing the pelican optimization algorithm enhanced the signal-to-noise ratio over current approaches such as genetic algorithm, PSO, TLBO, and GWO. Modified network architecture for high-density subscribers would be developed using the pelican optimization algorithm, which efficiently explores complex solution spaces, handles multiple objectives, and prevents premature convergence, thereby indicating significant improvement of the signal-to-noise ratio of the modified model, which shows an improvement in the quality of service for high-density subscribers, which is limited to concurrent users of mobile networks. This model's small cell equipment operates at a frequency of 28 GHz with a bandwidth of 700 MHz, supporting 3.5 million concurrent users within the allocated area. The main criterion for evaluating network performance will be user-received power levels.