Enhanced Mobility Algorithm Research Based On Estimated Speed In LTE-A Heterogenerous Network

As an evolution of LTE system, LTE-A keeps a good backward compatibility with LTE. It offers higher peak data rates and better system capacity. With ever increasing demand for the mobile radio communication services, it is difficult for the conventional homogeneous networks to provide required system capacity and user throughput as it has approached its fundamental limits. One of the promising and cost-effective solutions to eliminate the coverage hole and improve the system capacity is to increase the node deployment density, i.e. deploy Low Power Nodes (LPNs), such as relay, pico eNodeB (eNB), and Femto eNB in macro area. This networking manner is named as heterogeneous networks (HetNets). The introduction of the heterogeneous network brings the gain of the whole system and also brings the new challenges. The transmit power of LPN is far below the transmit power of the macro base station in HetNet. Moreover, the coverage area of LPN is significantly less than that of the macro base station, especially when users are in the mobile status, many configuration parameters and algorithms in the homogeneous networks are no longer applicable.The type of the LPNs deployed in the system-level platform is pico eNodeB. Both enhanced mobility state estimation and optimization of handover algorithm are two aspects of the paper to be researched. The main contents include:Firstly, low-power nodes in heterogeneous network such as picocells have a lower transmission power and small coverage. Even if UE moves at the uniform speed, during the same time, the handover number between pico-pico and macro-macro is different. When mobility state of UE is estimated based on the handover numbers, it may be biased. So we propose an enhanced mobility state estimation (EMSE) algorithm utilizing self-optimization prcess so it could be applied in different network topologies.Secondly, the handover performance in HetNet deployment is not as good in pure macro deployments. Of the different HO types, handover performance from pico shows the worse performance than from macro. The handover performance is different when the UE is in different mobility state. The quality of handover gets worse with the increase of the UE speed. We propose an enhanced algorithm based on the estimated speed, it could solve these problems and improve the handover performance.