Two-Level Estimation Enabled Online Congestion Control for Massive IoT Networks

IF 4.4 3区 计算机科学 Q2 TELECOMMUNICATIONS
Shilun Song;Jie Liu;Han Seung Jang;Hu Jin
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引用次数: 0

Abstract

In the massive Internet of Things (mIoT) scenario, characterized by a burst of access requests, the random access (RA) mechanism faces significant challenges in establishing radio resource control (RRC) connections. Access class barring (ACB) and Backoff are two typical control schemes. Devices first undergo the ACB check, and upon passing, transmit preambles and payloads in a contention-based manner. Failed attempts then enter the Backoff process for retransmission. Maximizing RA efficiency by collaborating these two control schemes is a critical challenge. This letter presents a performance analysis of the coexistence of ACB and Backoff and proposes an optimal control scheme. To enhance practical applicability, a Bayesian estimation-based approach is introduced. Simulation results validate the proposed algorithm’s substantial improvement in RA efficiency.
大规模物联网网络的两级估计在线拥塞控制
在以突发访问请求为特征的大规模物联网场景中,随机访问(RA)机制在建立无线电资源控制(RRC)连接方面面临重大挑战。Access class blocking (ACB)和Backoff是两种典型的控制方案。设备首先进行ACB检查,通过检查后,以基于争用的方式发送序言和有效载荷。失败的尝试然后进入回退过程进行重传。通过协作这两种控制方案来最大化RA效率是一个关键的挑战。本文对ACB和Backoff共存的性能进行了分析,并提出了一种最优控制方案。为了提高实际应用,提出了一种基于贝叶斯估计的方法。仿真结果验证了该算法在RA效率上的显著提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Communications Letters
IEEE Communications Letters 工程技术-电信学
CiteScore
8.10
自引率
7.30%
发文量
590
审稿时长
2.8 months
期刊介绍: The IEEE Communications Letters publishes short papers in a rapid publication cycle on advances in the state-of-the-art of communication over different media and channels including wire, underground, waveguide, optical fiber, and storage channels. Both theoretical contributions (including new techniques, concepts, and analyses) and practical contributions (including system experiments and prototypes, and new applications) are encouraged. This journal focuses on the physical layer and the link layer of communication systems.
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