Physical Cell Identity Assignment Research Articles

The Physical Cell Identity Assignment Problem: A Practical Optimization Approach

The Physical Cell Identity (PCI) is a critical parameter to radio access networks used by the user equipment, like a smartphone, to identify the cell to connect. The PCI misassignment leads to call drops, no voice/data connections, and other sorts of impairments. Many works in the literature only treat the PCI assignment problem from the technological constraints viewpoint. In this work, we take a realistic approach and incorporate several operational constraints found daily in carrier-grade networks. For that, we model the problem with multiple objectives with a strict lexicographical dominance relation. We solve it using a hybrid memetic algorithm with warm start solutions obtained by a constraint programming solver and intensification with path-relinking and custom local search procedures. The experimental results suggest that our approach is superior to commercial solver packages in several scenarios.

Evaluation of Random Physical Cell Id Assignment to Femtocells Under Dense Cell Deployment

The paper discusses femtocell identification aspects in mobile networks such as long-term evolution (LTE) and LTE-advanced. Femtocells, or femto access points (FAPs), are new-class of base stations in addition to existing macro, microcells. Unlike these cells, femtocells implement automatic configuration principles. One of the main parameters in the automatic configuration process is among others an physical cell identifier. By considering two FAP deployment models, we discuss collision and confusion issues, which affect the proper physical cell id allocation. The first model represents a random deployment of FAPs within a (rural) macrocell area, whereas the second one, called dense urban model, constitutes placement of FAPs in a built-up area. The given study serves for the better understanding of interrelations between differently placed FAPs in term of collision and confusion events, and the obtained results can be used as a basis when designing an algorithm for automated assignment of physical cell identifier.

Dynamic PCI assignment in two‐tier networks based on cell activity prediction

Physical cell identity (PCI) is used to identify each cell. However, with the introduction of femtocells, 8 bytes allocation for PCI cannot accommodate a large number of femtocells. The current solution in 3GPP Release 9 is to use cell global identity associated with PCI to resolve this problem. However, this solution may incur higher inbound handover failure. A dynamic PCI assignment based on cell activity level prediction is proposed. The system level simulation showed that the approach outperforms the existing approaches.

Neural Networks Based Physical Cell Identity Assignment for Self Organized 3GPP Long Term Evolution

This paper proposes neural networks based graph coloring technique to assign Physical Cell Identities throughout the self-organized 3GPP Long Term Evolution Networks. PCIs are allocated such that no two cells in the vicinity of each other or with a common neighbor get the same identity. Efficiency of proposed methodology resides in the fact that minimum number of identities is utilized in the network wise assignment. Simulations are performed on a very large scale network, where initially all the cells are without any PCIs assigned. Results of simulations are demonstrated to analyze the performance of the proposed technique. Discussions about the presence of femto cells and PCI assignment in them are also presented at the end.

Graph theory based physical cell identity self-configuration for LTE-A network

Self-configuration of physical cell identity (PCI) is a key feature for the long term evolution advanced (LTE-A) network. The PCI is used to identify the different cells in the system, becoming an essential cell configuration parameter. Considering the uncoordinated deployments of the evolved Node B (eNodeB) and the limited number of PCI, the PCI assignment for cells would be quite complex. This paper presents and puts forward a graph theory based centralized PCI self-configuration scheme (GT-PCIS). The PCI self-configuration problem is mapped to the well-known minimum spanning tree (MST) problem in order to optimize the PCI reuse distance and decrease the multiplexing interference throughout the entire network. The proposal provides a greedy search to make the locally optimal selection of PCI at each stage, and to achieve a global optimum. To demonstrate the algorithm validity, performances of GT-PCIS and manual configuration are evaluated. Simulation results show that the proposed GT-PCIS outperforms other configuration algorithms even under the condition of severe PCI deficiency.

Distributed Graph Coloring for Self‐Organization in LTE Networks

Primary Component Carrier Selection and Physical Cell ID Assignment are two important self‐configuration problems pertinent to LTE‐Advanced. In this work, we investigate the possibility to solve these problems in a distributive manner using a graph coloring approach. Algorithms based on real‐valued interference pricing of conflicts converge rapidly to a local optimum, whereas algorithms with binary interference pricing have a chance to find a global optimum. We apply both local search algorithms and complete algorithms such as Asynchronous Weak‐Commitment Search. For system level performance evaluation, a picocellular scenario is considered, with indoor base stations in office houses placed in a Manhattan grid. We investigate a growing network, where neighbor cell lists are generated using practical measurement and reporting models. Distributed selection of conflict‐free primary component carriers is shown to converge with 5 or more component carriers, while distributed assignment of confusion‐free physical cell IDs is shown to converge with less than 15 IDs. The results reveal that the use of binary pricing of interference with an attempt to find a global optimum outperforms real‐valued pricing.