On the Resource Consumption of M2M Random Access: Efficiency and Pareto Optimality

Vilgelm, Mikhail; Linares, Sergio Rueda; Kellerer, Wolfgang

doi:10.1109/lwc.2018.2886892

Cited by 8 publications

(17 citation statements)

References 8 publications

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“…For IIoT applications, reliability and delay play an important role, therefore, choosing reliability as an objective can increase the application performance at the cost of extra resources. The trade-off between performance and resource consumption should also be studied as a multi-objective optimization [19].…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Grant-Free Access with Multipacket Reception: Analysis and Reinforcement Learning Optimization

Jacquelin

Vilgelm

Kellerer

2019

2019 15th Annual Conference on Wireless on-Demand Network Systems and Services (WONS)

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Grant-free access has been identified by 3GPP as a potential solution for Industrial Internet-of-Things applications in 5G networks. It allows to decrease overhead and delay, but it is also prone to collisions in the high-load regime. To reduce the effects of collisions, Non-Orthogonal Multiple Access or other Successive Interference Cancellation (SIC) protocols can be applied, allowing to partially recover collisions. In this paper, we abstract the grant-free access protocols with SIC with a K-Multipacket Reception (K-MPR) model. Based on this abstraction, we analyze its one-frame and steady-state throughput, delay and failure probability under different backoff schemes. Furthermore, we propose a reinforcement learning approach to allocate grant-free resources dynamically in order to maximize the normalized throughput of the protocol. Monte-Carlo simulations are employed to confirm the accuracy of analytical results and to evaluate the throughput, delay, and reliability of the proposed resource allocation approach.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Grant-Free Access with Multipacket Reception: Analysis and Reinforcement Learning Optimization

Jacquelin

Vilgelm

Kellerer

2019

2019 15th Annual Conference on Wireless on-Demand Network Systems and Services (WONS)

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“…In [8]- [18], the authors consider the random access procedure and study the effect of preamble selection under certain assumptions on the knowledge of device activities. Specifically, [8]- [10], [13], [16]- [18] assume that the number of active devices is known; [14], [15] assume that the distribution of the number of active devices is known; [11], [12] assume that the statistics of the data queue of each device are known. In [8]- [18], preambles are selected according to a uniform distribution, the average throughput [8]- [18], average access efficiency [16] and resource consumption [10] are analyzed, and the number of allocated preambles is optimized to maximize the average throughput [10], [17] or access efficiency [16].…”

Section: Introductionmentioning

confidence: 99%

Joint Optimization of Preamble Selection and Access Barring for Random Access in MTC with General Device Activities

Liu¹,

Cui²,

Feng³

et al. 2021

Preprint

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Most existing random access schemes for MTC simply adopt a uniform preamble selection distribution, irrespective of the underlying device activity distributions. Hence, they may yield unsatisfactory access efficiency, especially for correlated device activities. In this paper, we model device activities for MTC with a general MVB distribution and optimize preamble selection and access barring for random access in MTC according to the underlying device activity distribution. We investigate three cases of the general joint device activity distribution, i.e., the cases of perfect, imperfect, and unknown joint device activity distributions, and formulate the average, worst-case average, and sample average throughput maximization problems, respectively. The problems in the three cases are challenging nonconvex problems. In the case of perfect joint device activity distribution, we develop an iterative algorithm and a low-complexity iterative algorithm to obtain stationary points of the original problem and an approximate problem, respectively. In the case of imperfect joint device activity distribution, we develop an iterative algorithm and a low-complexity iterative algorithm to obtain a KKT point of an equivalent problem and a stationary point of an approximate problem, respectively. In the case of unknown joint device activity distribution, we develop an iterative algorithm to obtain a stationary point.

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“…In [2]- [9], the authors consider the random access procedure and study the effect of preamble selection under certain assumptions on the knowledge of device activities. Specifically, [2]- [4], [6], [8], [9] assume that the number of active devices is known; [7] assume that the distribution of the number of active devices is known; [5] assume that the statistics of the data queue of each device are known. In [2]- [9], preambles are selected according to a uniform distribution, and the average throughput [2]- [9], average access delay [8] and resource consumption [4] are analyzed.…”

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confidence: 99%

“…Specifically, [2]- [4], [6], [8], [9] assume that the number of active devices is known; [7] assume that the distribution of the number of active devices is known; [5] assume that the statistics of the data queue of each device are known. In [2]- [9], preambles are selected according to a uniform distribution, and the average throughput [2]- [9], average access delay [8] and resource consumption [4] are analyzed. Assuming that all devices have the same preamble selection distribution, the authors in [4], [8], [9] optimize the number of allocated preambles to maximize the average throughput [4], [9] or access efficiency [8].…”

mentioning

confidence: 99%

“…In [2]- [9], preambles are selected according to a uniform distribution, and the average throughput [2]- [9], average access delay [8] and resource consumption [4] are analyzed. Assuming that all devices have the same preamble selection distribution, the authors in [4], [8], [9] optimize the number of allocated preambles to maximize the average throughput [4], [9] or access efficiency [8].…”

mentioning

confidence: 99%

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Joint Optimization of Preamble Selection and Access Barring for MTC with Correlated Device Activities

Liu

Cui

Ding

et al. 2021

Preprint

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Most existing works on random access for machinetype communication (MTC) assume independent device activities. However, in several Internet-of-Things (IoT) applications, device activities are driven by events and hence may be correlated. This paper investigates the joint optimization of preamble selection and access barring for correlated device activities. We adopt a random access scheme with general random preamble selection parameterized by the preamble selection distributions of all devices and an access barring scheme parameterized by the access barring factor, to maximally exploit correlated device activities for improving the average throughput. First, we formulate the average throughput maximization problem with respect to the preamble selection distributions and the access barring factor. It is a challenging nonconvex problem. We characterize an optimality property of the problem. Then, we develop two iterative algorithms to obtain a stationary point and a low-complexity solution respectively by using the block coordinate descend (BCD) method. Numerical results show that the two proposed solutions achieve significant gains over existing schemes, demonstrating the significance of exploiting correlation of device activities in improving the average throughput. Numerical results also show that compared to the stationary point, the low-complexity solution achieves a similar average throughput with much lower computational complexity, demonstrating the effectiveness of the low-complexity solution. I. INTRODUCTIONInternet-of-Things (IoT) has had broad applications in several areas, such as home automation, smart grids, healthcare systems, and industrial monitoring, and has received increasing attention in recent years. The number of IoT devices is expected to grow up to 30 billion by 2030, and more and more new IoT applications are emerging. There is a need to design a robust, scalable, and efficient sixth-generation (6G) wireless network that can effectively realize machine-type communications (MTC) to support future IoT applications. This paper aims to provide a promising solution for random access for MTC in 6G.In random access for MTC, devices compete in a random access channel (RACH) to access a base station (BS) through the random access procedure [1]. Specifically, each active device randomly selects a preamble from a pool of available preambles according to a preamble selection distribution and transmits it during the RACH. The BS acknowledges the

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On the Resource Consumption of M2M Random Access: Efficiency and Pareto Optimality

Cited by 8 publications

References 8 publications

Grant-Free Access with Multipacket Reception: Analysis and Reinforcement Learning Optimization

Grant-Free Access with Multipacket Reception: Analysis and Reinforcement Learning Optimization

Joint Optimization of Preamble Selection and Access Barring for Random Access in MTC with General Device Activities

Joint Optimization of Preamble Selection and Access Barring for MTC with Correlated Device Activities

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