电子学报 ›› 2019, Vol. 47 ›› Issue (12): 2544-2549.DOI: 10.3969/j.issn.0372-2112.2019.12.012

• 学术论文 • 上一篇    下一篇

基于退避预测的ACB动态接入新方案

孙君, 万聪, 闵宝成, 杨赛赛   

  1. 南京邮电大学江苏省无线通信重点实验室, 江苏南京 210003
  • 收稿日期:2018-11-28 修回日期:2019-07-10 出版日期:2019-12-25
    • 通讯作者:
    • 孙君
    • 作者简介:
    • 万聪 男,1993年8月生于湖北鄂州,现为南京邮电大学通信与信息工程学院硕士研究生.主要研究方向为海量机器类通信设备的随机接入.E-mail:wc_xcb@163.com;闵宝成 男,1993年7月生于安徽天长,现为南京邮电大学通信与信息工程学院硕士研究生,主要研究方向为SCMA接收端解码算法.E-mail:673240842@qq.com;杨赛赛 男,1993年7月生于江苏宿迁,现为南京邮电大学通信与信息工程学院硕士研究生,主要研究方向为海量机器类通信过程中的资源分配.E-mail:stephon510@163.com
    • 基金资助:
    • 国家自然科学基金 (No.61771255,No.61427801)

A Novel Dynamic ACB Accessing Scheme Based on Back-Off Prediction

SUN Jun, WAN Cong, MIN Bao-cheng, YANG Sai-sai   

  1. Jiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210003, China
  • Received:2018-11-28 Revised:2019-07-10 Online:2019-12-25 Published:2019-12-25
    • Corresponding author:
    • SUN Jun
    • Supported by:
    • National Natural Science Foundation of China (No.61771255, No.61427801)

摘要: 在LTE-A网络的过载场景中,机器类通信(Machine Type Communication,MTC)设备的突发性接入会使得网络发生严重的拥塞,甚至死锁,造成网络的接入效率低下.在可用前导资源有限的前提下,根据实时负载数控制发起接入的设备数可以有效降低前导的碰撞概率,但是控制方法尚不明确.为此,本文提出了一种接入类别限制(Access Class Barring,ACB)的动态接入机制来优化海量MTC的随机接入性能.建立了一种基于退避预测的估计模型,该模型根据重传的设备数和状态转移过程估计出了实时活跃的设备数.结合估计模型和ACB参数调整可以最优化实时成功接入的设备数,能够有效地提高设备的接入成功率.本文在不同负载强度场景下,将提出的ACB动态接入机制和现有的动态ACB机制的接入性能进行了比较.仿真结果证明,本文提出的ACB动态接入机制的接入成功率为100%.而且,与现有的ACB动态接入机制相比,所提的新方案的平均接入时延更低.

 

关键词: 海量机器类通信, LTE-A, 接入类别限制, 负载估计, 接入成功率, 平均接入时延

Abstract: In the overload scenario of the LTE-A network, the bursty access of the machine type communication (MTC) device may cause serious congestion or even deadlock in the network, resulting in low network access efficiency. Under the premise that available preambles are limited, controlling the number of devices that initiate the access according to the real-time load can effectively reduce the collision probability of the preamble, but the control method is not clear. To do this, this paper proposes a dynamic access class barring (ACB) mechanism to optimize the random access performance of the massive MTC. An estimation model based on back-off prediction is established.The model estimates the number of real-time active devices based on the number of retransmitted devices and the state transition process. Moreover, combined with the adjustment of the ACB parameter, the number of successfully accessed devices in real time can be optimized, and the access success rate of the device is effectively improved. The performance of the proposed dynamic ACB scheme is compared with that of the existing dynamic ACB schemes under different traffic degrees. Simulation results prove that the access success probability of the proposed dynamic ACB scheme is 100%. Simultaneously, the proposed novel scheme can get lower average access delay comparing with the existing dynamic ACB schemes.

 

Key words: massive machine type communication (mMTC), LTE-A, access class barring (ACB), load estimation, access success probability, average access delay

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