Research On Mobility Management Method In Heterogeneous Cellular Networks

A new era of mobile Internet applications has been opened by the development of high performance of mobile terminals and corresponding abundant online resources. Meantime, mobile data traffic accounted for the proportion of global mobile traffic is get-ting much higher, and most of the traffic is requested in densely inhabited district such as indoor, stadium, and downtown, namely hot-spot. However, the synthetical environ-ment in hot-spot can lead a big penetration loss of signal, and the operated network cannot provide a peak transmission rate for massive traffic requests or even accepted experience for realtime interactive traffic. To address this shortfall, based on the Long Term Evolu-tion (LTE), Heterogeneous Cellular Networks (HCN) is proposed to improve the coverage depth in hot-spot. HCN is composed of vast Small Cells (SC), which has limited trans-mitting power and backhaul bandwidth, and the same frequency as the macrocell in LTE. Therefore, more flexible and efficient configuration of radio resources is necessary for HCN. As one of the key technology of radio resource management, Mobility Manage-ment (MM) is to guarantee the terminals to be served continuously. So this dissertation is mainly focused on the MM for HCN to provide the technical support for improving the network capacity in hot-spot.MM is devised for user motion features in HCN, which affects the performance of MM. The complex environment with dense buildings and road topology in hot-spot has restricted the mobilities, which are worth to be analyzed to assist MM design. So the user mobility features are analyzed based on Hidden Markov Model (HMM). The user mobility features of travel frequency, self-similarity, and long-tail effect in hot-spot are concluded. To evaluate the MM method more precisely, the dynamic system level simulation and cor-responding parameters are discussed. All the above is a support of the following research. MM is related to three procedures, which are searching the network, network paging, and roaming among cells. The procedures are corresponding to the main research aspects, which are Physical Cell Identification (PCI) assignment, Tracking Area (TA) planning, and handover management.In the cell search process, limited PCIs are difficult for users to distinguish the co-existed cells completely due to the massive deployment of SC randomly. So a fuzzy lay-ered PCI assignment method is proposed to reuse PCIs efficiently. The method assumes the PCI assignment for the graph coloring problem. According to the level of user activi-ties, the cells are fuzzy layered based on fuzzy clustering method, so that the cell with high user activity will be allocated PCI preferentially. PCI conflicts and confusions may still exist during the system operating. So a PCI adjustment method of neighbor cells is also proposed, via coordinating PCIs preferentially based on hot-spot activities. Simulation results show that the two proposed methods have better performance on the rate of PCI conflicts and confusions, handover failure, and system utilization.The signaling overheads of location updating have extremely increased with a given maximum load of paging signaling due to the massive SC deployments with limited cov-erage. A location management method is proposed based on the community detection for TA replanning. The method assumes the TA planning for the graph partition prob-lem, and then changes the the traditional model to the complex network model in order to improve the efficiency of graph partition. So the community is detected based on coop-erative game, and partition the cells belonging to the same community into a single TA. Simulation results show that the proposed methods have better performance on the rate of location updating, SC utilization, new call blocking, and system utilization.The users roaming in HCN, may encounter reduced signal quality resulting of service interruption or request handovers between the two cells frequently. So a handover man-agement method towards user experience and perceptual behavior is proposed to improve the seamless handover and to reduce the ping-pong effect. The method models from the user activity with HMM. Firstly, the Baum-Welch algorithm is adopted to learn the user traces in the real scene and obtain several HMM parameters. Secondly, the Forward algo-rithm is adopted to find the specific HMM parameter for the user, and generate the event sequences to obtain the next event. Finally, according to the preferred route between the two events, the Time to Trigger (TTT) is set for handover decision-making. In addition, a group handover method towards user experience is also proposed based on the matched-degree between user traffic requests and network status. Simulation results show that the two proposed methods reduce the rate of handover failure and ping-pong, while increasing the utilization rate of the SC and whole system.