Research And Design Of The Mechanism Of LTE Handover Parameters Self-Optimization

The third generation mobile communication cooperation program (3GPP) started Long Term Evolution (Long Term Evolution, LTE) project, LTE project based on OFDM/FDMA (Orthogonal Frequency Division Multiplexing/Frequency Division Multiple Access) as its core. Through the wireless interface and wireless network architecture for a number of improvements to achieve lower latency and improve the user's data rate, increasing the system capacity and coverage and reduce operating costs. It bring the evolution of the network structure, wireless devices and the core network of a major change logical interface from the 3G network to the LTE network. LTE network discarded RNC-NodeB structure, together provided by the eNodeB and core network function.3GPP LTE introduces the concept of Self-Organized Network because of The complexity of the LTE network, and also need the support from autonomic network management.3GPP and SOCRATES have done a lot of self-organized network management research. SOCRATES's use cases in the self-configuration, self-optimization, self-healing, gradually become a research hotspot. Traditional research of handover parameter self-optimization focus on too-early handover, too-late handover and ping-pong handover, the research of these cases have no much difference with 2G/3G network. The target of handover parameter self-optimization is to achieve load balancing and desired Key Performance Index.In this context, this paper introduces two methods. The first introduce a novel distributed mobility load balancing algorithm through adjusting Radio Resource Management (RRM) parameters dynamically based on the source cell load and its neighboring cell condition. By setting different RRM parameters for different neighboring cell, the load can be directed to the target neighboring cells without causing degradation of user's satisfaction. The other method is to analyze the relationship between the key performance indicators and radio resource management parameters through linear regression. With other parameters unchanged, the KPI such as call blocking rate, call drop rate, load, and the average bit rate have linear relationship with handover margin. Applying this to the network monitoring can make guidance to the adjustment of key performance indicators. So we can achieve the desired KPI with handover margin self-optimization.