Research On The Congestion Controlling And Resource Scheduling Strategy Of Massive Machine Type Communication

With the rapid development of cloud computing,big data,and the Internet of Things(Io T),requirements of communication have been continuing to increase.Aiming at the characteristics of massive connection,small data packets and bursts of large-scale access in a short period time of machine type communication,5G added the massive Machine Type Communication(m MTC)application scenario on the basis of the fourth generation of mobile communication.In order to meet the access requirements of massive machine type devices,the control optimization needs to be further performed based on the current congestion control scheme.At the same time,considering the differences in the Quality of Service(Qo S)of different types Machine Type Communication Device(MTCD),the access requirements of MTCD different from each other.Therefore,it is also necessary to solve the preamble division problem when MTCDs of multiple service types accesses the cellular network.For the scenario where multiple service types MTCD perform random access at the same time,the access delay,number of collisions and access fairness of various service types MTCD must be considered while increasing system throughput.To solve the congestion problem in the random access of massive MTCD,a dynamic access class barring(ACB)has been proposed in this thesis to optimize the random access performance of massive MTCD.On the basis of 3GPP protocols,an estimation model based on back-off prediction is established,and in combination with the dynamic ACB mechanism,ACB parameters can be dynamically adjusted to effectively solve the collision problem of the preamble.Because the devices that initiate access at each random access slot include not only newly arrived devices,but also devices that retransmit at this slot.In view of the unknown load number of the current time slot,an estimation model based on backoff prediction can estimate the number of MTCDs that can initiate access at the current slot.In different load intensity scenarios,the access success rate and average access delay performance of the proposed ACB dynamic access mechanism,the static ACB mechanism with optimal parameters,and the existing optimal dynamic ACB mechanism has been compared.Simulation results show that the throughput of the dynamic ACB access mechanism proposed in this thesis can reach the maximum theoretical value when overload occurs.Compared with the static ACB mechanism and the existing dynamic ACB access mechanism,the proposed scheme has a lower average access delay.To solve the accessing priority problem during the random access of machine type device when multiple service types of MTCD coexist,a dynamic preamble allocation mechanism based on the maximum and minimum fairness algorithm is proposed in this thesis.This mechanism ensures the delay requirements of different priority devices.The throughput performance of the system can be maximized while meets the latency requirements.Also,MTCD can be grouped according to machine type of MTCD and delay requirements,and the MTCD that initiates access can be divided into high priority,normal priority,and low priority groups.Then,based on the maximum and minimum fairness algorithm,the preamble is allocated to MTCDs based on the delay requirements of each priority group and the estimated load in time.Finally,the dynamic ACB mechanism is combined to optimize system throughput and access delay.