Reinforcement Learning-Based ACB in LTE-A Networks for Handling Massive M2M and H2H Communications

Tello-Oquendo, Luis; Pacheco-Paramo, Diego; Pla, Vicent; Martínez-Bauset, Jorge

doi:10.1109/icc.2018.8422167

Cited by 54 publications

(29 citation statements)

References 15 publications

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“…Numerous methods to continuously adapt the barring parameters have been proposed in the literature [9,[14][15][16][17][18]; we refer to these methods simply as ACB configuration (ACBC) schemes. In other words, we use the term ACBC scheme to refer to any method or approach to select the barring parameters.…”

Section: Introductionmentioning

confidence: 99%

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Filtering Methods for Efficient Dynamic Access Control in 5G Massive Machine-Type Communication Scenarios

et al. 2018

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One of the three main use cases of the fifth generation of mobile networks (5G) is massive machine-type communications (mMTC). The latter refers to the highly synchronized accesses to the cellular base stations from a great number of wireless devices, as a product of the automated exchange of small amounts of data. Clearly, an efficient mMTC is required to support the Internet-of-Things (IoT). Nevertheless, the method to change from idle to connected mode, known as the random access procedure (RAP), of 4G has been directly inherited by 5G, at least, until the first phase of standardization. Research has demonstrated the RAP is inefficient to support mMTC, hence, access control schemes are needed to obtain an adequate performance. In this paper, we compare the benefits of using different filtering methods to configure an access control scheme included in the 5G standards: the access class barring (ACB), according to the intensity of access requests. These filtering methods are a key component of our proposed ACB configuration scheme, which can lead to more than a three-fold increase in the probability of successfully completing the random access procedure under the most typical network configuration and mMTC scenario.

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Section: Introductionmentioning

confidence: 99%

“…Tello-Oquendo et al presented one of the few ACBC schemes that were specifically designed for 3GPP cellular networks [18]. In particular, a reinforcement learning approach was utilized.…”

Section: Introductionmentioning

confidence: 99%

Filtering Methods for Efficient Dynamic Access Control in 5G Massive Machine-Type Communication Scenarios

et al. 2018

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“…Efficiently supporting mMTC is one of the pillars of 5th generation (5G) [6,49], and 4G will surely be the basis of 5G networks. Therefore, recent research efforts have been focused on ACBC schemes to adapt the barring parameters to the intensity of accesses in real time [34,35,71,94,97]. In theory, such ACBC schemes are the only ones capable of providing an optimal performance.…”

Section: Discussionmentioning

confidence: 99%

“…Results presented by de Andrade et al [34] and by Tello-Oquendo et al [97] showcase the difficulty of designing ACBC schemes and the impact that the delay of notification mechanisms have on performance.…”

Section: Related Workmentioning

confidence: 94%

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On reliable and energy efficient massive wireless communications: the road to 5G

Leyva-Mayorga¹

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To my wife, Mónica, and to my parents, because they have given me the most precious gifts I can think of: their love, confidence, and support. v Lastly, I would like to thank the support provided by the Consejo Nacional de Ciencia y Tecnología, México (CONACYT) and by the Consejo Mexiquense de Ciencia y Tecnología (COMECYT) through grant CONACYT-GEM 383936 for postgraduate studies in a foreign country. This includes the support for my short stays at the Universidad Autónoma del Estado de México (UAEM). These stays were only possible due to the support provided by Prof. Otniel Portillo Rodríguez. Thanks for the invitation.

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A random access channel resources allocation approach to control machine‐to‐machine communication congestion over LTE‐advanced networks

Aragao

Rodrigues

Vieira

et al. 2023

Int J Communication

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Summary The Internet of Things (IoT) is becoming a reality, and one of the core elements to make this reality come true is machine‐to‐machine (M2M) communication. With the fast‐growing rate in which devices are being deployed, communication among them has become crucial to support the development of IoT applications. The Long‐Term Evolution‐Advanced (LTE‐A) is a potential access network for these M2M devices. However, the LTE‐A inherited characteristics from older cellular network standards, which were designed for human‐to‐human (H2H) and human‐to‐machine (H2M) communication. Accordingly, supporting M2M communication poses some challenges to LTE‐A, with a highlight of the congestion and overload problems in the radio access network (RAN) during the random access channel (RACH) procedure. Such a problem arises due to the large number of devices sending access‐request messages to the network and the limited number of resources to satisfy this new demand. In this paper, we introduce a solution to mitigate the impact of M2M communication in the LTE‐A network. Precisely, we model how to divide the random access resources into different types of devices as a bankruptcy problem. Then, we propose two solutions: (i) A game theory approach to formulate the bankruptcy problem as a transferable utility game, and (ii) an axiomatic method where some elements are considered for judging the amount of resources each class should receive. The simulation results show that our approaches present improvements in terms of energy efficiency, impact control of M2M over H2H accesses, and priority respect among the different classes of devices.

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Reinforcement Learning-Based ACB in LTE-A Networks for Handling Massive M2M and H2H Communications

Cited by 54 publications

References 15 publications

Filtering Methods for Efficient Dynamic Access Control in 5G Massive Machine-Type Communication Scenarios

Filtering Methods for Efficient Dynamic Access Control in 5G Massive Machine-Type Communication Scenarios

On reliable and energy efficient massive wireless communications: the road to 5G

A random access channel resources allocation approach to control machine‐to‐machine communication congestion over LTE‐advanced networks

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