Accelerated genetic algorithm based on real number coding for solving mobile communication network site planning
Jiawei Luo · Academic Journal of Computing & Information Science · 2023
The advancement of 5G networks has sparked widespread interest in effectively strategizing the placement of communication base stations within areas of weak network coverage. Addressing this pertinent topic, this paper aims to enhance service coverage and reduce the construction costs associated with base stations in such areas. Specifically, we conduct a comprehensive analysis by selecting base station sites within a given area comprising 2500×2500 points, taking into account both ideal and real-world conditions. To ensure practical applicability, we strive to develop an optimal base station site selection scheme for 5G construction enterprises. This is accomplished through the construction of a multi-objective planning model that considers multiple constraints. By building upon this foundation, we further refine and optimize the conditions of the multi-objective planning model. This step enables us to obtain a new multi-objective planning model that aligns with the real-life requirement of fan-shaped base station communication coverage. To effectively solve the listed multi-objective planning model and obtain an optimized station site plan, we employ an accelerated genetic algorithm based on real number coding. Furthermore, to provide visual representation and analysis of the base station solution, this paper utilizes Python and ArcGIS, facilitating the visualization of the results. Through this approach, we can effectively explore and demonstrate the efficacy of the proposed base station placement strategy.