A crow search algorithm integrated with dynamic awareness probability for cellular network cost management

This research presents a crow search algorithm (CSA) for reducing the cellular network cost when reporting the cell planning (RCP) scheme. In cellular systems, the RCP scheme is used to maintain location. We employ CSA as an optimization tool because it is a bio-inspired optimization technique. In this study, CSA uses the cellular network's diversity to optimize the cost of reporting the RCP scheme. The cost of location management is calculated using dynamic awareness probability (DAP) and a CSA for various cellular network sizes. With each iteration of the CSA, the dynamic properties of the DAP are used to change the decision threshold. This provides additional freedom and enhances decision-making abilities. As a result, the set awareness probability allows for a cheaper cost per call arrival. Extensive simulations are used to test and evaluate the suggested method's performance. The experiments are carried out with 4 x 4, 6 x 6, and 8 x 8 cells in current cellular systems. The recommended CSA is used to measure performance in groups of 50, 100, 150, and 200 people. Multiple graphs displaying statistical measurements, convergence rates, and other data are used to present the conclusions. It was determined that scaling up from a smaller to a larger network lowers the cost per call arrival by about 12%. This shows a possible vision of the proposed CSA's vast range of uses and needs more research to improve existing cellular services.