Improved Pseudo-Bayesian Access Class Barring for Bursty M2M Communications

Hu, Yangqian; Liu, Jie; Jin, Hu

doi:10.1109/ICTC49870.2020.9289237

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Improved Pseudo-Bayesian Access Class Barring for Bursty M2M Communications

Hu, Yangqian;
Liu, Jie ;
Jin, Hu

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초록

Random access procedure of long-term evolution (LTE) cellular systems can cause excessive communication delay for bursty machine-type communications. In this paper, we propose an improved pseudo-Bayesian access class barring (ACB) algorithm by introducing a novel tuning for burst factor to support bursty traffic arrivals. As a result, the proposed algorithm estimates the number of active machine-type devices (MTDs) more accurately compared to the conventional ACB algorithms and significantly reduces access delay for bursty MTD activations. Simulation results show that the proposed algorithm shows closed performance to the ideal ACB algorithm which is assumed to know the actual number of active MTDs. © 2020 IEEE.

키워드

ACB; Bayesian estimation; M2M; massive random access; Long Term Evolution (LTE); Access class; Access delay; Burst factors; Bursty traffic; Cellular system; Communication delays; Machine type communications; Random access; Machine-to-machine communication

제목: Improved Pseudo-Bayesian Access Class Barring for Bursty M2M Communications

저자: Hu, Yangqian; Liu, Jie ; Jin, Hu

DOI: 10.1109/ICTC49870.2020.9289237

발행일: 2020-10

유형: Conference Paper

저널명: 11TH INTERNATIONAL CONFERENCE ON ICT CONVERGENCE: DATA, NETWORK, AND AI IN THE AGE OF UNTACT (ICTC 2020)

권: 2020-October

페이지: 336 ~ 338

언어: ENG

출판사: IEEE

발행국가: 미국

분량: 3 페이지

ISSN: P 2162-1233