Research On Access Control Strategy For 5G Massive Machine Communication

In different application scenarios supported by future 5G cellular networks,massive Machine-Type Communication(mMTC)has become a research focus for scholars at home and abroad because it faces the technical difficulty of supporting a large number of MTC devices to access simultaneously in cellular networks.Therefore,this article proposes an access control strategy for 5G mMTC,which is of practical significance.The Access Class Barring(ACB)scheme proposed by the 3rd Generation Partnership Project(3GPP)is an effective way that can directly control machine-tomachine(M2M)trafffic.Based on the ACB scheme,this paper establishes a random access model for a large number of MTC devices and studies the access control strategies.Firstly,the discrete Generalized Pursuit algorithm(DGPA)is introduced into the access control strategy,and an access control strategy with the goal of minimizing the total service time is proposed.Then,we study the problem of simultaneous access of devices with different priorities,and proposed an access control strategy that comprehensively considers Qo S requirements and access resource allocation.The main research content of this article can be summarized as follows:(1)In order to tackle radio access network(RAN)overload problem in the random access process of machine-to-machine communication(M2M)in 5G scenarios,we propose an access class barring(ACB)adaptive adjustment strategy based on discrete Generalized Pursuit algorithm(DGPA)in learning automaton.We consider the use of the early preamble collision detection(E-PCD)technique in the detection of the preambles to avoid the problem of conflicting messages in the access process.Then,according to the collided preambles and idle preambles,the number of accessing devices is estimated by the DGPA to adaptively adjust the ACB factor to reduce the access delay.Simulation results show that the proposed algorithm can achieve similar performance with the optimal ACB algorithm,assuming that the exact number of access devices is known to the base station.In addition,when the preambles are insufficient or the number of devices is huge,the algorithm proposed in this paper can reduce the total access delay by nearly20% compared with the previously proposed access strategy based on Bayesian estimation.(2)For the problem of simultaneous access of two types of devices with different Qo S requirements,an access control strategy that takes into account the dynamic allocation of access resources and adaptive adjustment of ACB factors is proposed.Different strategies are proposed in the two cases of considering only the preamble resource allocation and the combined preamble and uplink shared resource allocation.These strategies set different priorities for the two types of devices.In each access time slot,resources are dynamically allocated according to the access load.Delay-sensitive devices can use access resources first,and delay-tolerant devices are allowed to access after them.Then,according to the idle preambles of each time slot,the number of access devices in the next time slot is estimated,so as to adaptively change the ACB factor to realize the access control of the devices.The simulation results show that the strategy proposed in this paper has performance similar to the optimality that use the optimal ACB factor in every time slot,which can stabilize the access delay of delay-sensitive devices while improving resource utilization,and meet the throughput requirements of delaytolerant devices at the same time.Besides,when the allocatable Physical Uplink Shared Channel(PUSCH)resources are insufficient,the integrated resource allocation algorithm proposed in this paper can further ensure no significant changes in system performance and obtain performance similar to the optimal algorithm.