Recursive Pseudo-Bayesian Access Class Barring for M2M Communications in LTE Systems
- Authors
- Jin, Hu; Toor, Waqas Tariq; Jung, Bang Chul; Seo, Jun-Bae
- Issue Date
- Sep-2017
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Access class barring (ACB); Bayesian estimation; internet-of-things (IoTs); machine-type communication (MTC); massive random access
- Citation
- IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, v.66, no.9, pp.8595 - 8599
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
- Volume
- 66
- Number
- 9
- Start Page
- 8595
- End Page
- 8599
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/9017
- DOI
- 10.1109/TVT.2017.2681206
- ISSN
- 0018-9545
- Abstract
- Commercial long-term evolution (LTE) systems adopt an access class barring (ACB) mechanism in the initial random access procedure with multiple preambles in order to accommodate bursty traffic arrivals of machine-type communications. In this paper, we propose two Bayesian ACB algorithms that estimate the number of active machine devices based only on the number of idle preambles in each slot. In the commercial LTE systems, eNodeB cannot instantaneously distinguish if a particular preamble is sent from a single device (i.e., success) or multiple devices (i.e., collision). However, the idle preambles can be instantaneously detected at the base station (BS) in each slot. Numerical results show that the proposed algorithms yield quite similar performance with the ideal ACB algorithm, assuming that the exact number of active devices is known to the eNodeB.
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