Research On Load Control And Energy Efficiency Optimization Of Machine-to-Machine Communication In LTE

With the rapid development of Internet communication technology and the rise of the Internet of Things(IoT),the number of machine equipment with ability of access network is increasing.Machine-to-machine(M2M)communication is a key technology in IoT,with little or without human intervention.In 3GPP,M2M communication,also known as machine type communication(MTC),is mainly used for data transmission over the cellular network.In order to meet the M2M service requirements,3GPP evolves the cellular system,since the traditional cellular system is mainly designed for the human-to-human(H2H)communication.Therefore,this paper,the LTE-M communication technology for the cellular network is researched in depth.The communication load control and energy efficiency optimization of the massive devices enables the network to meet the requirements of the M2M service of small traffic and large number of connections.This is of great significance to the development of the IoT technology based on the cellular network.Firstly,this paper studies the random access protocol of LTE-M.Due to the congestion caused by the access of massive devices,random access in the Random Access Network(RAN)is a key process for generating congestion.Therefore,this paper describes and analyzes the random access process of LTE-M,and then establishes a complete LTE-M random access analysis model.Considering the periodic reporting and abnormal alarm MTC business scenarios,the performance of the two M2M device access traffic models is evaluated through the established analysis model.Secondly,this paper discusses the primary problem that when a large number of MTC devices initiate a random access request,the congestion of the random access network seriously affects the success rate of the M2M device.In order to control the load of the LTE-M random access,the advantages and disadvantages of various current congestion control algorithms are discussed and analyzed.Then,a statistical-waiting random access load control algorithm is proposed.The proposed algorithm and three typical algorithms are compared,based on the random access mathematical model of the established LTE-M system,The results show that the algorithm has a better improvement on the access success rate than the existing algorithms.Finally,this paper also studies another problem that increasing consumption by increasing signaling overhead,while equipment energy is limited.So,the energy efficiency of the cellular network becomes more and more important.In this paper,a system model including radio resource RB in LTE is established to include a service model,a channel link model,and an energy efficiency model,based on the established mathematical model of random access.Then,an adaptive clustering energy efficiency algorithm is proposed,based on the concept of cooperative relaying,by improving the clustering algorithm in machine learning,and a simulation platform is built to perform simulation analysis and comparison of the proposed system performance.The simulation results show that the algorithm can significantly improve the system energy efficiency and throughput.Further research work may take into account the small data features of IoT services and simplifying the process of uploading data from MTC devices,to further increase system capacity,For deeply study the effort of energy efficiency,the technology used in cluster communication need to be considered.