Research And Implementation Of Multi-source Acquisition Technology Based On Dedicated Small LTE Base Station

As an evolution project of the third generation communication technology,LTE(Long Term Evolution)improved its air access technology and the performance of cell-edge users.In addition,the project also reduced the system delay and increased the user capacity of the cell.As the fourth-generation mobile communication standard,LTE is widely used in various fields.In some specific scenarios,we need to design special processes based on standard protocols to achieve their functions.Based on the LTE network system,this paper implements the functions of mobile user identification and location information collection under the special scenario requirements of the public security and netcom field.In order to achieve its functions,this article first analyzes the basic network architecture and key technologies of the dedicated small base station.Then,based on the standard LTE protocol,the feasibility and timeliness of different access processes to realize their identification and collection functions are discussed.Finally,its core functional modules are designed,implemented and tested.The main tasks of this paper are as the following points:1.Based on the special scenario in the field of public security and netcom,a demand analysis was performed.And discussed the current mainstream technology of dedicated small base stations and the technology stack used in this article.2.Analyzed the network architecture of LTE and the key technologies in the access process.Introduced software stack,terminal identity information,RRC layer protocol,non-access layer signaling and other related knowledge.3.The main functional modules are divided and designed based on the LTE standard protocol and network architecture.Design the message signaling in the TAU process to complete the function of reporting identity information.After discussing the feasibility and timeliness of different schemes for obtaining measurement reports in special scenarios.Then the TAU Reject message was designed to allow UE to return to the originally connected network.4.Use the RSSI obtained from measurement report to train offline models and predict users location information online.The method based on support vector machine is used to establish the offline model.The data is preprocessed and filtered before themodel is trained.Analyzed the influence of different kernel functions on model accuracy.5.Based on the hardware platform and software platform,test the designed modules and processes.Then validates the feasibility of their implementation.In the end,this paper summarized the content of the paper.The imperfections in the design and testing of the scheme are analyzed.The directions for improvement and the points for further research are proposed.