物联网学报 ›› 2021, Vol. 5 ›› Issue (1): 90-98.doi: 10.11959/j.issn.2096-3750.2021.00164

• 理论与技术 • 上一篇    下一篇

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

朱振宇, 朱晓荣, 蔡艳, 朱洪波   

  1. 南京邮电大学通信与信息工程学院，江苏 南京 210003
  • 修回日期:2020-03-24 出版日期:2021-03-30 发布日期:2021-03-01
  • 作者简介:朱振宇（1995- ），男，南京邮电大学硕士生，主要研究方向为物联网大规模终端接入算法
    朱晓荣（1977- ），女，南京邮电大学教授、博士生导师，主要研究方向为下一代无线网络、物联网等
    蔡艳（1974- ），女，南京邮电大学副教授、硕士生导师，主要研究方向为无线通信与电磁兼容、移动通信与宽带无线通信技术等
    朱洪波（1956- ），男，南京邮电大学教授、博士生导师，南京邮电大学原副校长，物联网研究院院长，江苏省“泛在无线通信与物联网”科技创新团队带头人，主要研究方向为物联网、移动通信网络等
  • 基金资助:
    国家自然科学基金资助项目(61871237);江苏省重点研发计划(BE2019017)

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

Zhenyu ZHU, Xiaorong ZHU, Yan CAI, Hongbo ZHU   

  1. College of Telecommunications ＆Information Engineering, Nanjing University of Posts and Telecommunication, Nanjing 210003, China
  • Revised:2020-03-24 Online:2021-03-30 Published:2021-03-01
  • Supported by:
    The National Natural Science Foundation of China(61871237);The Jiangsu Province Key R＆D Plan(BE2019017)

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摘要：

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 1 000 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1 000 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。

关键词: 物联网, 大规模接入, 时间序列预测, 自适应接入类禁止

Abstract:

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 1 000 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 1 000 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.

Key words: Internet of things, massive access, time series prediction, adaptive access class barring

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