Research On Access And Resource Management Of Large Scale Machine Type Communication

With the rise of health care,smart cars,autopilot and smart cities technology,Machine Type Communications(MTC)is treated as one of the most important development directions by many research organizations.The relevant standardization organizations which include The 3rd Generation Partnership Project(3GPP)have begun to promote the research of MTC based on the Long Term Evolution(LTE).Now lots of researchers are devoted to solve the problem of large scale MTC users'load.Random access and radio resource management are important directions of MTC's research.MTC communication has higher requirements than traditional communication services on access performance,connection quantity and reliability.These factors pose great challenges to the limited signaling resources in the traditional networks.Considering both random access and resource management of MTC communication,the main research aspects of this thesis are summarized as follows:Compared with traditional cellular users' traffic,the MTC users'number is large while the amount of data is small.Under the basis of traditional random access procedure,we propose a scheme which combines the hierarchical-access strategy and back-off method to solve the congestion problem.The simulation result shows our proposed scheme gets better performance.Furthermore,wireless resource management is considered under the scenario including both cellular users and MTC users.The Quality of Experience(QoE)models have been respectively formed for both types,and an integer problem has been formulated.Then we propose a two-step iterative algorithm to solve the problem which enhances the average QoE performance of users.A critical issue of MTC is how to support the massive number of connections.Generally,a group-based access scheme is adopted to solve this problem.Concretely,MTC devices transmit messages to their group heads,which aggregate messages in the same group.This method can improve the utilization of signaling resources.Different from traditional group-based access scheme,our scheme can adaptive choose group heads.With the constraint of network resources and the guarantee of transmission date rate,we jointly optimize signaling and data transmission.A two-step iterative algorithm has been designed to solve the problem,which can effectively mitigate the problem of signaling overloading.Additionally,we consider the access problem in multi-slots.Based on Group-based access strategy for MTC users,a Markov Decision Process(MDP)-based user selection scheme has been proposed for MTC devices.On the basis of traditional access procedure in a single slot,reinforcement learning has been introduced into the analysis.By using policy iteration,we gradually get the optimal access strategy.Compared with RSS(Received Signal Strength)method,the proposed MDP-based method provides better performance on MTC users' selection in multi-slots' scenario.In summary,this thesis studies three research aspects about MTC's access and resource management problem.Different schemes are proposed respectively,whose performance are verified by simulations.