Distributed queuing with preamble grouping for massive IoT devices in LTE random access

Yoon, Chang-Bae

doi:10.1109/ictc.2016.7763445

Cited by 10 publications

(6 citation statements)

References 5 publications

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“…However, the newly arrived devices were only allowed to contend in the groups without collisions. The method proposed showed a reduction in the overall access delay and a significant optimization in the use of preamble resources compared to the baseline EAB and the DQ variants proposed separately by Laya and Yoon in [3] and [11] respectively.…”

Section: Dq In Lte Networkmentioning

confidence: 94%

“…The conventional (sequential) DQ algorithm applied to LTE networks for M2M communications have been found to cause significant access delay [11]- [13]. In [11], instead of resolving each contention in each group sequentially, the author proposed to resolve those contention groups in parallel. Compared to the sequential method, the proposed scheme was shown to reduce the average number of random access slots with the average number of preambles slightly increasing.…”

Section: Dq In Lte Networkmentioning

confidence: 99%

“…Most of the studies reviewed have been found to put an emphasis on the evaluation of these four performance metrics: the access delay [3]- [5], [9], [10], [12]- [16], the throughput [2]- [6], [15], [16], the energy consumption [2], [3], [5], [8], [14], and the number of attempts per node [3], [4], [10], [11]. Besides, it is a fact that the algorithm outperforms the Aloha-based schemes in terms of those performance metrics.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Statistical Analysis of a Distributed Queuing Random Access Protocol in a Massive Communication Environment

Nibitanga¹,

Mwangi²,

Ndungu³

2020

EAI Endorsed Trans IoT

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Most of the networks deployed for massive IoT communications use Aloha-based algorithms for channel access. However, those algorithms are known to be unstable and inefficient when the network size is high. Since recently, a Distributed Queuing (DQ) algorithm is being proposed as a solution to mitigate several of the Aloha issues in IoT networks. In this paper, a statistical performance analysis of the DQ algorithm without any prior consideration of any physical layer is presented. We evaluate the DQ algorithm in a massive communication environment and give the average values for these performance metrics: collision resolution time, access delay per sensor, channel throughput, number of attempts required by a sensor to complete the contention process, number of nodes contending per frame and the distribution of contention slots into idle, successful, and collided. The goal of this paper is to provide a statistical baseline performance evaluation of the DQ algorithm in general.

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Section: Dq In Lte Networkmentioning

confidence: 94%

Section: Dq In Lte Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Statistical Analysis of a Distributed Queuing Random Access Protocol in a Massive Communication Environment

Nibitanga¹,

Mwangi²,

Ndungu³

2020

EAI Endorsed Trans IoT

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“…Here again, the algorithm was found superior to the Extended Baring Access (EAB) conventionally used in LTE networks in terms of delay. In [30], a DQ algorithm that resolves the contention groups from an initial collision in parallel was found to decrease the average number of access compared to the DQ sequential method.…”

Section: Related Workmentioning

confidence: 99%

Analysis of the Distributed Queueing Algorithm in a Multichannel Network with Mixed-Type Traffic

Nibitanga¹,

Mwangi²,

Ndungu³

2021

EAI Endorsed Trans IoT

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Networks such as LTE-M, NB-IoT, LoRa and SigFox are being deployed for massive IoT communications. However, the Aloha protocols used for the media access are inefficient when the network size is high. As a result, a Distributed Queuing (DQ) algorithm is being proposed to replace the conventional access schemes because of its superior network performance. In this paper, we study the algorithm with no prior consideration of any underlying technology at the physical layer. We perform a steady-state analysis of the algorithm in a network where the traffic is composed of periodic and urgent packets. It was found that for high traffic from the network all the nodes were taking part in each contention. Nevertheless, for better performance in terms of access delay and packet drop rate, it was preferable to operate the algorithm in the small traffic interval. Moreover, the downlink traffic has been observed to have a significant impact on the stability of the algorithm.

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“…This process allows the colliding devices to compete separately within several groups of preambles which reduces the collision rate and in turn improves the access success probability for the massive access scenarios. The same concept of preamble grouping is used in the distributed queueing algorithm [20]. In this algorithm, the devices that are colliding in a given preamble are directed to the same queue for the next access attempt.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Backoff Collision Resolution for Massive M2M Random Access in Cellular IoT Networks

et al. 2020

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The deployment of machine-to-machine (M2M) communications on cellular networks provides ubiquitous services to Internet-of-Things (IoT) systems. Cellular networks have been chosen as the best infrastructure for M2M communications due to the wide coverage and spectral efficiency. However, with the increased number of devices connecting to the network, massive number of devices are expected to simultaneously access the network resources. This massive access results in excessive congestion and collisions in the random access channel (RACH) which causes major degradation in systems performance. This paper focuses on resolving the RACH collisions during the massive access scenarios for cellular M2M communications. We propose a collision resolution scheme using the backoff procedure which dynamically adjusts the backoff indicator (BI) based on the number of backlog devices and the available resources. The proposed scheme is integrated with three well-known random access schemes; standard random access (SRA), static access class barring (ACB) and dynamic access class barring (DAB). Furthermore, the paper presents an analysis for access success probability based on the dynamic backoff procedure. The optimal value of BI that achieves the highest access success probability is derived for the three different schemes. The analysis and simulation results indicate that the dynamic value of BI achieves approximately 99.9% access success rate with a slight increase in access delay of around 10%, which is considered a reasonable increment for delay-tolerant applications during the massive arrivals scenarios.

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Distributed queuing with preamble grouping for massive IoT devices in LTE random access

Cited by 10 publications

References 5 publications

Statistical Analysis of a Distributed Queuing Random Access Protocol in a Massive Communication Environment

Statistical Analysis of a Distributed Queuing Random Access Protocol in a Massive Communication Environment

Analysis of the Distributed Queueing Algorithm in a Multichannel Network with Mixed-Type Traffic

Dynamic Backoff Collision Resolution for Massive M2M Random Access in Cellular IoT Networks

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