基于时隙ALOHA与自适应接入类禁止混合的大规模终端接入算法

Large-scale terminal access algorithm based on slot ALOHA and adaptive access class barring

为了解决物联网环境中大规模终端接入碰撞率高、时效性低的问题,提出了一种基于时隙ALOHA与自适应接入类禁止(ACB,access class barring)混合的大规模终端接入算法。该算法首先以各终端所处理业务的数据量及对时延的要求对业务进行分类,对于时延不敏感且有效数据部分小于1000 bit的业务,采用基于时隙ALOHA的竞争接入方式;对于时延敏感型业务或者有效数据部分大于1000 bit的业务,采用基于ACB的随机接入方式。在此基础上,提出了一种基于定量估计的接入申请量预测方法,并依据此预测值动态调整ACB控制参数。仿真结果表明,与现有的其他接入算法相比,在保证高优先级业务时延要求的前提下,所提出的算法有效降低了碰撞率,提高了系统接入成功率。

In order to solve the problem of high collision rate and low timeliness of large-scale terminals access in the Internet of things,a large-scale terminal access algorithm based on slot ALOHA and adaptive access class barring(ACB)was proposed.Firstly,the services were classified based on the data from each terminal by the volume of the services processed and the requirements for delay.For the services that were not time-sensitive and whose effective data portion was less than 1000 bit,a slot-based ALOHA-based competitive access method was used.ACB-based random access was used for the services that were time-sensitive or whose data portion was greater than 1000 bit.On this basis,a method was proposed for predicting the access application volume based on the quantitative estimation,and dynamically adjusting the ACB control parameters based on this predicted value.Simulation results show that compared with other existing access algorithms,the proposed algorithm reduces the collision rate and improves the system access success rate under the premise of ensuring the high priority service delay requirements.