Automatic Physical Cell Identity Planning using Machine Learning

Background: The growing needs of communications have a higher demand for data and stream-less services for the users. A unique physical cell identity (PCI) is assigned to transfer data between the cellular base station (gNB) and user equipment (UE). It is used to transmit the data to multiple users simultaneously. In this thesis, a heuristic algorithm is generated, aided by an unsupervised machine learning approach to improve the PCI allocation of a cell for better 5G services such as connectivity and speed. Objectives: Firstly, performing a literature review to find the appropriate performance metrics to compare both K-means and density-based spatial clustering of applications with noise (DBSCAN) technique on the PCI allocation data provided by Ericsson. Next, the better-clustering method along with heuristic algorithm was implemented to generate a efficient PCI planning. Later, compare the results of previous planning (existing PCI planning approach), proposed planning (results of using the generated heuristic algorithm) based on the ideal planning derived from the experts. Methods: The literature review is conducted for determining the best metrics for the clustering algorithms mentioned in the objectives. With the use of unsupervised learning the PCI allocation data is clustered based on its distance and neighbors. Subsequently the clusters are used in the heuristic algorithm. The results of proposed planning are compared with previous planning. Results: The literature review indicated that the silhouette coefficient and davies-bouldin index are most suitable metrics for comparing the clustering algorithms mentioned in the objectives. These two metrics are used to determine the best performing clustering algorithm. The clustering results were given as input for heuristic algorithm to generate a PCI planning. Then, the results stated that the proposed planning is better than previous planning and decreased nearly 70% collisions in the areas: Fresno, San Francisco and San Jose compared to the previous planning. Conclusions: The main goal of this study is to achieve a better PCI planning that can accommodate many users and achieve better 5G services. This PCI planning is helpful for the company to utilize its resources efficiently