Research On Downlink Transmission Of Massive MTC Devices

With the intelligent enhancement of mobile terminals,Machine Type Communi-cation(MTC)has become the most important application form of the Internet of Things(IoT),and has a very wide application space.The access of massive machine terminals and the production of massive machine communication data will bring many challenges to the future wireless networks.How to improve the efficiency of the limited wireless resources is a problem to be solved urgently.The current research on MTC resource allocation is focused on the upstream infor-mation collection process.With the popularization of remote control and information publishing,the downlink flow will increase gradually.It is possible for MTC users in the same cluster to request the same or similar information almost at the same time,and these MTC users have certain delay tolerance.In order to improve the utilization of downlink resources,based on the above char-acteristics,this thesis studies the downlink transmission strategy of massive machine communication users.The main contributions and innovations are as follows:(1)Based on the relevance of user request content,the transmission time sequence is adjusted under the delay requirement.We proposed the Aggregation Transmission Scheme(ATS)and designed the dynamic time slot allocation model.A system model is established for a machine communication user cluster in the TDMA(Time Division Multiple Access)system,including the mathematical model of information sending and delay requirement,and the standard of correlation measurement.In order to maximize the information correlation among users,the optimal timing adjustment problem of the information transmission matrix is established under the delay constraint,which makes as many users as possible to request the same data blocks at the same time.Further-more,a dynamic slot allocation model is designed to improve the utilization efficiency of slot resources.Subsequent simulation experiments show that the ATS performance is superior to the traditional MBMS(Multimedia Broadcast Multicast Service)mode,which proves the validity of ATS.(2)Three timing adjustment algorithm are designed,namely Conditional Random Search(CRS),Standard Row Algorithm(SRA)and genetic algorithm(GA)based tim-ing adjustment algorithm and performance simulation experiments are carried out.CRS reduces the search space by filtering the illegal solution that does not meet the delay re-quirements,but it is still time-consuming,and the stability of the search results is poor.so it is generally used as a comparison standard for other algorithms.SRA indirectly improves the correlation among all users by maximizing the similarity between other users and standard row users.The algorithm is simple,fast and suitable for large-scale user scenarios.GA has the best comprehensive performance,but with the increasing number of users,the computing efficiency is reduced,so it is more suitable for small and medium-sized users.The ATS strategy proposed in this thesis makes use of information relevance to adjust the time series under time delay constraints.It can improve the utilization ef-ficiency of time slot resources,and is of great significance to the resource allocation problem in MTC.