Abstract As mobile communication systems continue to evolve to support higher data rates, ultra-low latency, and greater reliability, the effective management of Physical Cell Identities (PCI) becomes increasingly important. The pool of available identifiers is limited—504 in fourth-generation networks and 1008 in fifth-generation networks—so reuse of these identities is unavoidable. Improper reuse can lead to collisions and confusion, resulting in interference, failed handovers, and reduced network throughput. This work addresses these challenges by exploring advanced automated techniques for optimizing PCI. We model the network as a graph, where each cell is represented as a vertex and interference relationships between cells are represented as edges. Using this approach, we implement graph coloring algorithms, including a maximum degree first coloring method, to efficiently assign identifiers and minimize conflicts. We also introduce clustering-based methods, such as Fuzzy hierarchical clustering, to dynamically group cells based on real-time traffic patterns, enabling adaptive management in dense networks. Beyond these deterministic methods, we evaluate metaheuristic approaches, including Biased Random-Key Genetic Algorithms (BRKG), Integer Linear Programming (ILP), and constructive heuristics based on DSATUR. Where they are particularly effective in navigating complex solution spaces to generate near-optimal PCI plans. Our simulation results demonstrate that these modern techniques significantly outperform traditional strategies, reducing identity conflicts by up to 80% and improving key performance metrics such as signal quality, handover success rates, and overall network throughput.
Farghaly et al. (Mon,) studied this question.