PCI Planning Based on Judgmental Nested Genetic Algorithm

The aim of this study is to develop a new optimization method to optimize the PCI value in a communication network and to reduce the signal interference in the area as well as from the surrounding users. Firstly, the MR number is selected as a measure of the degree of PCI planning in this study, and the relevant data of 2067 cells are collected, and two optimization directions, cumulative optimization and priority optimization, are discussed separately. For the cumulative optimization within the region, a genetic algorithm was used to search for the optimal solution by taking the PCI values of the 2067 cells in the sequential order as individuals and changing each PCI value to obtain the optimal solution. For the priority optimization within the region, the feasible optimal solutions in the priority stage are searched first, and then the final solutions satisfying the requirements are filtered in the above solution space, which is searched in the same way as above. Finally, the impact of changing the PCI value in the region on the surrounding users is discussed, and the data form of hash table is introduced to enumerate the impact of changing the PCI value in the region on the cells outside the region, and by optimizing the PCI value of the above 2067 cells, it is found that the optimization effect is better. This study provides a new method for PCI planning, which can reduce the time of running the traditional single genetic algorithm code and improve the user experience.