ALPACA: A PCI Assignment Algorithm Taking Advantage of Weighted ANR

Abstract

The growth of mobile data traffic has led to the use of dense and heterogeneous networks with small cells in 4G and 5G. To manage such networks, dynamic and automated solutions for operation and maintenance tasks are needed to reduce human errors, save on Operating expense (OPEX) and optimize network resources. Self Organizing Networks (SON) are a promising tool in achieving this goal, and one of the essential use cases is Physical Cell Identity (PCI) assignment. There are only 504 unique PCIs, which inevitably leads to PCI reuse in dense and heterogeneous networks. This can create PCI collisions and confusions, but also a range of modulo PCI issues. These PCI issues can lead to use cases where User Equipments (UEs) cannot properly identify a cell or cells cannot properly identify UEs, especially during handovers, which all leads to radio communication failure. Therefore, a proper PCI assignment is crucial for network performance. In this paper, we first conduct a study on the impact of different PCI issues on the performance of the network by doing experiments on real-life hardware. Based on the finding from the experiments we create two SON algorithms: a Weighted Automatic Neighbor Relations (ANR) and a PCI assignment algorithm ALPACA. The Weighted ANR creates neighbor relations based on measurements from the network and calculates weights for cells and neighbor relations. ALPACA uses these weights to assign PCI values to cells in a way that avoids PCI issues or at least minimizes their effects on the network. ALPACA works in phases to allow it to adapt to dynamic network topology changes and continuously optimize the network. We validate and evaluate our approach using a simulator package that we have developed. The results show that ALPACA can resolve all collisions and confusion for up to 1000 cells in a highly dense topology, as well as minimize the effects of inevitable modulo PCI issues.