Research On Key Technologies In LTE Self-optimizing Network

The3rd Generation Partnership Project (3GPP) has specified Long Term Evolution (LTE)as a new radio access technique. Compared to the3G network systems, LTE can offer thebetter network performances, such as the greater capacity, higher throughput and so on.However, with the ever-growing wireless mobile traffic, deployment and maintenance ofcellular mobile networks by human operation are becoming more and more complex,capital-costing and time-consuming. To simplify the management of the network and lowerthe maintenance cost, Self-organizing network (SON) is introduced into LTE, which isconsidered as a promising technique with automation mechanisms instead of manualoperation.The functionalities of SON are commonly divided into three major sub-functional groups,including self-configuration, self-optimization and self-healing. Mobility RobustnessOptimization (MRO) and Mobility Load Balancing (MLB) are two crucial functions of SON.MRO aims to decrease the Radio Link Failure (RLF) and the unnecessary handovers.Too-early handover or too-late handover can cause the RLF; here the unnecessary handoverrefers to the ping-pong handover. Meanwhile, in order to improve the network performancesin hot spots, MLB tries to transfer traffic from the edge of overloaded cells to theirunder-loaded neighboring cells. Therefore, the researches on these two functions are of greatimportantce for network performances such as the network resource utilization imporrementsand network operating costs reduction.This dissertation researches on the MRO and MLB in the wireless self-optimizingnetworks and the main contributions of this paper are summarized as follows:1) Based on optimization theory, we propose a scheme which can effectively prevent theconflicts between Mobility Robustness Optimization (MRO) and Mobility Load Balancing(MLB). By considering the variations of user’s RSRP (Received Signal Reference Power)during one period of TTT, we can set the handover range of MLB dynamically. Accordingly,we design a problem aiming to optimize handover procdures subject to the allowed range ofCIO. The simulation results verify that the proposed scheme can effectively reduce theconflicts between the MRO and MLB; Moreover, our methods can be shown to have betterperformance in Call Blocking Ratio (CBR), Radio Link Failure (RLF) and networkthroughput.2) Load balancing among multi-cell has great impact on the performance network. Theexisting methods were difficult to ensure the best performance of the network’s Key Performance indicators (e.g. the call blocking rate). In order to overcome this issue, weformulate the Load balancing as a multi-objective optimization problem subject to physicalresource limits and users’ Quality-of-Service (QoS) demands. The objectives are to optimizeload distribution among neighboring cells, average load of the network for QoS requirementsservices and network utility of other services. In addition, we propose a practical algorithmwith lower complexity. With the help of the proposed method, good load balancing index canbe obtained. Simulation results show that our proposed method can achieve betterperformances in new call blocking rate and network resources utilization comparing with theexisting methods.3) Most of the prervious lieturaters fouce on optimizing the balbance load among cells.However, the effects of KPI were neglected in these methods leading to poor KPIperformance. In practice, the KPIs are the most imporatant. In this paper, the load balancingproblem in LTE is studied. A distributed load balancing algorithm considering users’ QoSrequirements is proposed. The Load balancing problem is formulated as a KPI optimizationproblem. The objective is to minimize the CDBR (Call Drop and Block Ratio) subject tophysical resource limits and users’quality-of-service (QoS) demands. Accordingly, weproposed a practical and distributed algorithm with lower complexity. Extensive simulationshows that the proposed algorithm achieves better performance in the load balancing index,CDBR and network resource utilization.