Recursive Pseudo-Bayesian Access Class Barring for M2M Communications in LTE Systems

The advent of Machine-to-Machine communication has sparked a new wave of interest to random access protocols, especially in application to LTE Random Access (RA). By analogy with classical slotted ALOHA, state-of-the-art models LTE RA as a multi-channel slotted ALOHA. In this letter, we direct the attention to the resource consumption of RA. We show that the consumption is a random variable dependent on the contention parameters. We consider two approaches to include the consumption into RA optimization: by defining resource efficiency and by the means of a bi-objective optimization, where resource consumption and throughput are the competing objectives. We then develop the algorithm to obtain Paretooptimal RA configuration under resource constraint. We show that the algorithm achieves lower burst resolution delay and higher throughput than the state-of-the-art.

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“…In [8], the authors propose a dynamic adaptation of access probability maximizing S p = min(1, M/n).…”

Section: A Related Workmentioning

confidence: 99%

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

Vilgelm

Linares

Kellerer

2019

IEEE Wireless Commun. Lett.

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“…In the state-of-the-art, RACH problem is typically approached as a maximization of the expected throughput S. With access probability p i being the only design parameter (no BCCR), there is a single optimal point [6], [25]…”

Section: Single Contention Round: Bi-objective Optimizationmentioning

confidence: 99%

“…It calculates the a posteriori estimationn i as a function of the a priori estimaten − i (which depends on the previous RA round estimation, hence its recursiveness) and the number of idle preambles M I i . It assumes the backlog size in the i th contention round is a Poisson random variable whose mean is the a priori estimatê n − i and calculates the correction [25]:…”

Section: B Estimating the Backlogn Imentioning

confidence: 99%

Dynamic Binary Countdown for Massive IoT Random Access in Dense 5G Networks

Vilgelm

Linares

Kellerer

2019

IEEE Internet Things J.

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Massive connectivity for Internet of Things applications is expected to challenge the way access reservation protocols are designed in 5G networks. Since the number of devices and their density are envisioned to be orders of magnitude larger, state-of-the-art access reservation, Random Access (RA) procedure, might be a bottleneck for end-to-end delay. This would be especially challenging for burst arrival scenarios: Semisynchronous triggering of a large number of devices due to a common event (blackout, emergency alarm, etc.). In this article, to improve RA procedure scalability, we propose to combine Binary Countdown Contention Resolution (BCCR) with the state-of-theart Access Class Barring (ACB). We present a joint analysis of ACB and BCCR and apply a framework for treating RA as a biobjective optimization, minimizing the resource consumption and maximizing the throughput of the procedure in every contention round. We use this framework to devise dynamic load-adaptive algorithm and simulatively illustrate that the proposed algorithm reduces the burst resolution delay while consuming less resources compared to the state-of-the-art techniques.

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“…If the access probability is too large, the burst resolution might take even longer due to high preamble collision rates. To optimize the burst resolution times, several works have proposed a dynamic adaptation of the access probability [7], [8], based on the pseudo-Bayesian broadcast [6]. Consider expected number of successful UEs in a single slot, as a function of b i , M [9]:…”

Section: Dynamic Access Barringmentioning

confidence: 99%

“…In general, the number of UEs contending in a given PRACH slot B(i) is unknown. However, there exist a number of backlog estimation techniques, producing accurate results [5], [7], [8], [17], [18]. We study the impact of estimation numerically in Sec.…”

Section: Dynamic Access Barringmentioning

confidence: 99%

On the Reliability of LTE Random Access: Performance Bounds for Machine-to-Machine Burst Resolution Time

Vilgelm

Schiessl

Al-Zubaidy

et al. 2018

2018 IEEE International Conference on Communications (ICC)

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Random Access Channel (RACH) has been identified as one of the major bottlenecks for accommodating massive number of Machine-to-Machine (M2M) devices in LTE networks, especially in the case of bursty arrivals of connection requests. As a consequence, the burst resolution problem has sparked a large number of works analyzing and optimizing the expected performance of RACH. In this paper, we go beyond the study of performance in expectation by investigating the probabilistic performance limits of RACH with access class barring. We model RACH as a queuing system, and apply stochastic network calculus to derive probabilistic performance bounds for burst resolution time, i.e., the time it takes to connect a burst of M2M devices to the base station. We illustrate the accuracy of the proposed methodology and its potential applications in performance assessment and system dimensioning.

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Recursive Pseudo-Bayesian Access Class Barring for M2M Communications in LTE Systems

Cited by 104 publications

References 8 publications

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

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

Dynamic Binary Countdown for Massive IoT Random Access in Dense 5G Networks

On the Reliability of LTE Random Access: Performance Bounds for Machine-to-Machine Burst Resolution Time

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