Physical Cell Identity Research Articles

Self-organization in disaster-resilient heterogeneous small cell networks

Heterogeneous small cell networks with overlay femtocells and macrocell is a promising solution for future heterogeneous wireless cellular communications. However, great resilience is needed in heterogeneous small cells in case of accidents, attacks and natural disasters. In this article, we first describe the network architecture of disaster resilient heterogeneous small cell networks (DRHSCNs), where several self-organization inspired approaches are applied. Based on the proposed resilient heterogeneous small cell network architecture, self-configuring (power, physical cell ID and neighbor cell list self-configuration) and self-optimizing (coverage and capacity optimization and mobility robustness optimization) techniques are investigated in the DRHSCN. Simulation results show that self-configuration and self-optimization can effectively improve the performance of the deployment and operation of the small cell networks in disaster scenarios.

Interference management via access control and mobility prediction in two-tier heterogeneous networks

Two-tier heterogeneous networks (HetNets), where the current cellular networks, i.e., macrocells, are overlapped with a large number of randomly distributed femtocells, can potentially bring significant benefits to spectral utilization and system capacity. The interference management and access control for open and closed femtocells in two-tier HetNets were focused. The contributions consist of two parts. Firstly, in order to reduce the uplink interference caused by MUEs (macrocell user equipments) at closed femtocells, an incentive mechanism to implement interference mitigation was proposed. It encourages femtocells that work with closed-subscriber-group (CSG) to allow the interfering MUEs access in but only via uplink, which can reduce the interference significantly and also benefit the marco-tier. The interference issue was then studied in open-subscriber-group (OSG) femtocells from the perspective of handover and mobility prediction. Inbound handover provides an alternative solution for open femtocells when interference turns up, while this accompanies with PCI (physical cell identity) confusion during inbound handover. To reduce the PCI confusion, a dynamic PCI allocation scheme was proposed, by which the high handin femtocells have the dedicated PCI while the others share the reuse PCIs. A Markov chain based mobility prediction algorithm was designed to decide whether the femtocell status is with high handover requests. Numerical analysis reveals that the UL interference is managed well for the CSG femtocell and the PCI confusion issue is mitigated greatly in OSG femtocell compared to the conventional approaches.

Auto-configuration of Physical Cell ID in LTE femtocellular systems using Self Organizing Networks

The commercial success of cellular networks, combined with advances in digital electronics, signal processing, and telecommunications research have lead to the design of next generation 4G-based long term evolution (LTE) wireless systems. The key essence of these emerging, LTE cellular systems lie in deployment of multiple femtocells for improved coverage and higher data rates. However, the arbitrary deployment of a wide number of femtocells makes the configuration, management and planning of LTE systems quite complex and challenging. In order to support dynamic and efficient network configuration, every cell needs to be assigned a particular Physical Cell ID (PCID). In this paper we show that the dynamic, optimal PCID allocation problem in LTE systems is NP-complete. Subsequently we provide a near-optimal solution using Self-Organizing Networks which models the problem using new merge operations and explores the search space using a suitable randomized algorithmic approach. We also discuss two feasible options for dynamic auto-configuration of the system and analyze the algorithm to prove its convergence. Simulation results point out that our proposed near-optimal solution dynamically achieves ~85−90 % of global optimal auto-configuration in computationally feasible time.

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.

Interferer Identification in HetNets using Compressive Sensing Framework

We consider heterogeneous cellular networks (HetNet) where each base station (BS) sends unique training signal based on its physical layer cell identity. Received signal at mobile terminal (MT) is superposition of training signals from different BS. Neither BS identities nor their channel response is known apriori at MT. For this scenario, we consider the problem of finding constituent BS identities from superimposed components in received signal. Though number of BS with unique identities can be quite large in a HetNet, in any given scenario, actual number of BS interfering at MT is relatively few. By exploiting this sparseness, we show that our problem can be solved using block sparse signal reconstruction algorithms under compressive sensing framework where sensing matrix is block-matrix with circulant-blocks (BCB). We apply convex programming based \ell_2 / \ell_1 mixed norm minimization and greed based subspace matching pursuit approaches to recover interfering BS identities. We characterize block restricted isometry property, mutual subspace incoherence of BCB matrices with i.i.d. Rademacher distributed entries and establish certain recovery guarantees. Our proposed approaches give significant improvements over conventional successive interference cancellation approach over both randomly generated and 3GPP-LTE training signals.

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.

Design and Implementation of Cell Search Time Synchronization in LTE System Based on FPGA

LTE is accepted worldwide as the Long Term Evolution Perspective for todays 3G and 4G networks. Cell search time synchronization is an important physical layer procedure by which a user equipment (UE) acquires synchronization with a cell and detects the physical layer cell ID of that cell. Timing synchronization is consisted of the symbol timing synchronization and frame synchronization. This paper supplies the introduction of synchronization signals, presents the detection method, and analyzes the design based on FPGA platform and finally simulated on the Modelsim 6.5 and implemented in Xilinx Virtex-6 kit.

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.

Automatic Distributing Schemes of Physical Cell Identity for Self-Organizing Networks

This paper presents and puts forward an optimal automatic distributing of physical cell identity (ADPCI) scheme for the self-organizing network (SON). Considering the high number and the layered structure of the evolved node B (eNodeB, eNB) in the initial rollout phase, the assigning of PCI for cells would be quite complex. The PCI self-distributing 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. The correlation property of PCI is analyzed and taken into consideration in the assigning phase. Moreover, a suboptimal algorithm (SADPCI) is presented as it performs approximately to ADPCI but the computational complexity is lower. To demonstrate the proposal validity, performances of ADPCI and SADPCI are evaluated. Simulation results illustrate that these schemes can achieve significantly higher performance even under the condition of severe PCI deficiency.

Self-optimization of LTE networks utilizing Celnet Xplorer

In order to meet demanding performance objectives in Long Term Evolution (LTE) networks, it is mandatory to implement highly efficient, autonomic self-optimization and configuration processes. Self-optimization processes have already been studied in second generation (2G) and third generation (3G) networks, typically with the objective of improving radio coverage and channel capacity. The 3rd Generation Partnership Project (3GPP) standard for LTE self-organization of networks (SON) provides guidelines on self-configuration of physical cell ID and neighbor relation function and self-optimization for mobility robustness, load balancing, and inter-cell interference reduction. While these are very important from an optimization perspective of local phenomenon (i.e., the eNodeB's interaction with its neighbors), it is also essential to architect control algorithms to optimize the network as a whole. In this paper, we propose a Celnet Xplorer-based SON architecture that allows detailed analysis of network performance combined with a SON control engine to optimize the LTE network. The network performance data is obtained in two stages. In the first stage, data is acquired through intelligent non-intrusive monitoring of the standard interfaces of the Evolved UMTS Terrestrial Radio Access Network (E-UTRAN) and Evolved Packet Core (EPC), coupled with reports from a software client running in the eNodeBs. In the second stage, powerful data analysis is performed on this data, which is then utilized as input for the SON engine. Use cases involving tracking area optimization, dynamic bearer profile reconfiguration, and tuning of network-wide coverage and capacity parameters are presented.

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.

Femtocell access control strategy in UMTS and LTE

Femtocells have become an attractive device for operators to offer extended services on their licensed UMTS/LTE spectrum. In this article we focus on the access control strategy - a crucial aspect for operators to give preferential access to femtocells for their subscribers. The general issue of supporting femtocells in pre-Release 8 UMTS networks with pre-Release 8 UE is first outlined in order to help the reader understand the motivation behind some concepts introduced in 3GPP Release 8 to support femtocells. A description of the femtocell access control strategy for UMTS and LTE is then provided based on 3GPP Release 8 specifications. Finally, the issue of LTE physical cell identity confusion associated with inbound handover to femtocells (which is expected to be resolved within the 3GPP Release 9 timeline) is discussed.