RAPID: Contention Resolution-based Random Access using Context ID for IoT

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RAPID: Contention Resolution-based Random Access using Context ID for IoT. / Kim, Junseok; Kim, Seongwon; Taleb, Tarik; Choi, Sunghyun.

julkaisussa: IEEE Transactions on Vehicular Technology, Vuosikerta 68, Nro 7, 8730509, 01.07.2019, s. 7121-7135.

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Kim, Junseok ; Kim, Seongwon ; Taleb, Tarik ; Choi, Sunghyun. / RAPID: Contention Resolution-based Random Access using Context ID for IoT. Julkaisussa: IEEE Transactions on Vehicular Technology. 2019 ; Vuosikerta 68, Nro 7. Sivut 7121-7135.

Bibtex - Lataa

@article{ccbce405ee774e7c847af7343218dbb8,
title = "RAPID: Contention Resolution-based Random Access using Context ID for IoT",
abstract = "With the increasing number of Internet of Things (IoT) devices, Machine Type Communication (MTC) has become an important use case of the Fifth Generation (5G) communication systems. Since MTC devices are mostly disconnected from Base Station (BS) for power saving, random access procedure is required for devices to transmit data. If many devices try random access simultaneously, preamble collision problem occurs, thus causing latency increase. In an environment where delay-sensitive and delay-tolerant devices coexist, the contention-based random access procedure cannot satisfy latency requirements of delay-sensitive devices. Therefore, we propose RAPID, a novel random access procedure, which is completed through two message exchanges for the delay-sensitive devices. We also develop Access Pattern Analyzer (APA), which estimates traffic characteristics of MTC devices. When UEs, performing RAPID and contention-based random access, coexist, it is important to determine a value which is the number of preambles for RAPID to reduce random access load. Thus, we analyze random access load using a Markov chain model to obtain the optimal number of preambles for RAPID. Simulation results show RAPID achieves 99.999{\%} reliability with 80.8{\%} shorter uplink latency, and also decreases random access load by 30.5{\%} compared with state-of-the-art techniques.",
keywords = "Uplink, 5G mobile communication, Load modeling, 3GPP, Internet of Things, Markov processes, Delays, 2-step random access, 5G, Internet of things, Markov chain model, and radio resource control state",
author = "Junseok Kim and Seongwon Kim and Tarik Taleb and Sunghyun Choi",
year = "2019",
month = "7",
day = "1",
doi = "10.1109/TVT.2019.2919867",
language = "English",
volume = "68",
pages = "7121--7135",
journal = "IEEE Transactions on Vehicular Technology",
issn = "0018-9545",
number = "7",

}

RIS - Lataa

TY - JOUR

T1 - RAPID: Contention Resolution-based Random Access using Context ID for IoT

AU - Kim, Junseok

AU - Kim, Seongwon

AU - Taleb, Tarik

AU - Choi, Sunghyun

PY - 2019/7/1

Y1 - 2019/7/1

N2 - With the increasing number of Internet of Things (IoT) devices, Machine Type Communication (MTC) has become an important use case of the Fifth Generation (5G) communication systems. Since MTC devices are mostly disconnected from Base Station (BS) for power saving, random access procedure is required for devices to transmit data. If many devices try random access simultaneously, preamble collision problem occurs, thus causing latency increase. In an environment where delay-sensitive and delay-tolerant devices coexist, the contention-based random access procedure cannot satisfy latency requirements of delay-sensitive devices. Therefore, we propose RAPID, a novel random access procedure, which is completed through two message exchanges for the delay-sensitive devices. We also develop Access Pattern Analyzer (APA), which estimates traffic characteristics of MTC devices. When UEs, performing RAPID and contention-based random access, coexist, it is important to determine a value which is the number of preambles for RAPID to reduce random access load. Thus, we analyze random access load using a Markov chain model to obtain the optimal number of preambles for RAPID. Simulation results show RAPID achieves 99.999% reliability with 80.8% shorter uplink latency, and also decreases random access load by 30.5% compared with state-of-the-art techniques.

AB - With the increasing number of Internet of Things (IoT) devices, Machine Type Communication (MTC) has become an important use case of the Fifth Generation (5G) communication systems. Since MTC devices are mostly disconnected from Base Station (BS) for power saving, random access procedure is required for devices to transmit data. If many devices try random access simultaneously, preamble collision problem occurs, thus causing latency increase. In an environment where delay-sensitive and delay-tolerant devices coexist, the contention-based random access procedure cannot satisfy latency requirements of delay-sensitive devices. Therefore, we propose RAPID, a novel random access procedure, which is completed through two message exchanges for the delay-sensitive devices. We also develop Access Pattern Analyzer (APA), which estimates traffic characteristics of MTC devices. When UEs, performing RAPID and contention-based random access, coexist, it is important to determine a value which is the number of preambles for RAPID to reduce random access load. Thus, we analyze random access load using a Markov chain model to obtain the optimal number of preambles for RAPID. Simulation results show RAPID achieves 99.999% reliability with 80.8% shorter uplink latency, and also decreases random access load by 30.5% compared with state-of-the-art techniques.

KW - Uplink

KW - 5G mobile communication

KW - Load modeling

KW - 3GPP

KW - Internet of Things

KW - Markov processes

KW - Delays

KW - 2-step random access

KW - 5G

KW - Internet of things

KW - Markov chain model

KW - and radio resource control state

UR - http://www.scopus.com/inward/record.url?scp=85069459852&partnerID=8YFLogxK

U2 - 10.1109/TVT.2019.2919867

DO - 10.1109/TVT.2019.2919867

M3 - Article

VL - 68

SP - 7121

EP - 7135

JO - IEEE Transactions on Vehicular Technology

JF - IEEE Transactions on Vehicular Technology

SN - 0018-9545

IS - 7

M1 - 8730509

ER -

ID: 34371795