A look at random access for machine‐type communications in 5th generation cellular networks

Grassi, Alessandro; Piro, Giuseppe; Boggia, Gennaro

doi:10.1002/itl2.3

Cited by 10 publications

(2 citation statements)

References 8 publications

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“…Other studies in [ 27 , 28 ] considered an information redundancy approach in which RA response (RAR) messages are redundantly sent to users to reduce the collision rate and hence support an envisioned a large number of users. In the legacy system, if more than one user gets the same preamble, they collide or one of them obtains a RAR response.…”

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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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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“…The conclusions of most of these studies point out that for Machine-Type Communications (MTC) applications, the Random Access procedure can drastically limit network performance. Possible approaches to modify the PRACH access procedure have been proposed in [20,21,22].…”

Section: Related Workmentioning

confidence: 99%

Limitations and sidelink-based extensions of 3GPP cellular access protocols for very crowded environments

et al. 2020

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An experience common to smartphone users is the difficulty in accessing services in crowded scenarios, such as a rock concert or a football match. In these cases, to (partially) mitigate frustration, users generically claim that network congestion is occurring, and try again and again to access the network with their smartphones: the result is that user frustration and network congestion reinforce each other! This paper investigates the root causes of poor performance of cellular networks in crowded environments and shows that the commonly adopted random access procedure can prevent full utilization of wireless resources. We develop a simple yet accurate analytical model to analyze why attempting random access to wireless resources can become a problem even when access congestion avoidance is enforced, e.g., with the Access Class Barring technique. The model we propose suggests that cluster-based network access, leveraging device-to-device communications, significantly alleviates access problems. Moreover, it sheds light on scalability laws that govern network utilization and quality of experience, in terms of cell capacity, number of access channels, and cluster size.

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