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

Vilgelm, Mikhail; Schiessl, Sebastian; Al-Zubaidy, Hussein; Kellerer, Wolfgang; Gross, James

doi:10.1109/icc.2018.8422323

Cited by 14 publications

(8 citation statements)

References 21 publications

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“…IV. USER BARRING ALGORITHM For mMTC scenarios, traffic overloading on the RA channel is a challenging issue, which can be especially critical when a large number of devices simultaneously activate, e.g., sensors reconnecting after a power outage [42]. This simultaneous triggering causes bursty arrivals, which can significantly degrade the performance of RA protocols.…”

Section: Probability Of Idle Channel Analysismentioning

confidence: 99%

Throughput Analysis and User Barring Design for Uplink NOMA-Enabled Random Access

Foh

Quddus

et al. 2021

IEEE Trans. Wireless Commun.

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Being able to accommodate multiple simultaneous transmissions on a single channel, non-orthogonal multiple access (NOMA) appears as an attractive solution to support massive machine type communication (mMTC) that faces a massive number of devices competing to access the limited number of shared radio resources. In this paper, we first analytically study the throughput performance of NOMA-based random access (RA), namely NOMA-RA. We show that while increasing the number of power levels in NOMA-RA leads to a further gain in maximum throughput, the growth of throughput gain is slower than linear. This is due to the higher-power dominance characteristic in power-domain NOMA known in the literature. We explicitly quantify the throughput gain for the very first time in this paper. With our analytical model, we verify the performance advantage of NOMA-RA scheme by comparing with the baseline multi-channel slotted ALOHA (MS-ALOHA), with and without capture effect. Despite the higher-power dominance effect, the maximum throughput of NOMA-RA with four power levels achieves over three times that of the MS-ALOHA. However, our analytical results also reveal the sensitivity of load on the throughput of NOMA-RA. To cope with the potential bursty traffic in mMTC scenarios, we propose adaptive load regulation through a practical user barring algorithm. By estimating the current load based on the observable channel feedback, the algorithm adaptively controls user access to maintain the optimal loading of channels to achieve maximum throughput. When the proposed user barring algorithm is applied, simulations demonstrate that the instantaneous throughput of NOMA-RA always remains close to the maximum throughput confirming the effectiveness of our load regulation.Index Terms-Massive machine type communication (mMTC), NOMA, random access, user barring.

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Section: Probability Of Idle Channel Analysismentioning

confidence: 99%

Throughput Analysis and User Barring Design for Uplink NOMA-Enabled Random Access

Foh

Quddus

et al. 2021

IEEE Trans. Wireless Commun.

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“…The random variables are the control parameters discussed in this paper. Assuming the RA channel (RACH) as a queueing system, probabilistic upper bounds for performance metrics have also been suggested in [ 34 ]. However, in this analysis process, some questionable assumptions were made.…”

Section: Ai and Recent Ra Enhancements In Literaturementioning

confidence: 99%

Random Access Using Deep Reinforcement Learning in Dense Mobile Networks

Bekele

Choi

2021

Sensors

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5G and Beyond 5G mobile networks use several high-frequency spectrum bands such as the millimeter-wave (mmWave) bands to alleviate the problem of bandwidth scarcity. However high-frequency bands do not cover larger distances. The coverage problem is addressed by using a heterogeneous network which comprises numerous small and macrocells, defined by transmission and reception points (TRxPs). For such a network, random access is considered a challenging function in which users attempt to select an efficient TRxP by random access within a given time. Ideally, an efficient TRxP is less congested, minimizing delays in users’ random access. However, owing to the nature of random access, it is not feasible to deploy a centralized controller estimating the congestion level of each cell and deliver this information back to users during random access. To solve this problem, we establish an optimization problem and employ a reinforcement-learning-based scheme. The proposed scheme estimates congestion of TRxPs in service and selects the optimal access point. Mathematically, this approach is beneficial in approximating and minimizing a random access delay function. Through simulation, we demonstrate that our proposed deep learning-based algorithm improves performance on random access. Notably, the average access delay is improved by 58.89% from the original 3GPP algorithm, and the probability of successful access also improved.

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“…IV-C, BCCR asymptotically allows to achieve arbitrary low collision probability. This property could be utilized in for ultra-reliable low latency (URLLC) applications, to design RA procedure with reliability guarantees [22]. .…”

Section: B Future Workmentioning

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

Cited by 14 publications

References 21 publications

Throughput Analysis and User Barring Design for Uplink NOMA-Enabled Random Access

Throughput Analysis and User Barring Design for Uplink NOMA-Enabled Random Access

Random Access Using Deep Reinforcement Learning in Dense Mobile Networks

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

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