Load balancing based on clustering analysis and deep learning for multi-frequency and multi-mode network - Yaxing QIU; Xidong WANG; Sen BIAN; Lei YUE (Dianxin kexue)
Load balancing is a huge challenge for LTE multi-frequency and multi-mode network.Hundreds of parameters are involved in load balancing for the complex network structure.Therefore,it is difficult to perform precise and meticulous configuration only relying on human experience.In order to cope with the challenge,a load balancing scheme based on clustering analysis and deep learning was proposed.Firstly,the key indicators were selected to identify the network scenes,and then big data and deep learning technologies were used to mine the relationship between data.Finally,the optimum system parameters for different network scenes were found.It has been proved that machine learning technology can greatly improve the accuracy and the efficiency of parameter configuration.