Frame based back-off for Q-learning RACH access in LTE networks

Bello, Lawal; Mitchell, Paul; Grace, David

doi:10.1109/atnac.2014.7020894

Cited by 10 publications

(19 citation statements)

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“…This effect has been studied in [16], which introduces the FB-QL-RACH scheme (with fixed frame size) to direct H2H retransmissions to a separate frame, and improves the throughput performance by significantly reducing collisions between H2H and M2M users , which can only occur during each user's first attempt. The throughput analysis of the FB-QL-RACH is developed in this section by extending the throughput analysis of the combined scheme developed above.…”

Section: Application Scenariomentioning

confidence: 99%

“…6 is an examp le of a frame structure representing the FB-QL-RACH scheme, where a steady state of three M2M users acquiring three dedicated slots is assumed. For full details on the FB-QL-RACH scheme, see [16]. On the other hand, Fig.…”

Section: Application Scenariomentioning

confidence: 99%

“…The paper additionally provides an analytical model for thorough investigation of H2H and M2M traffic behaviour in sharing the RACH, which also allows us to predict their throughput performance. In addition, the analysis serves to validate the simulation results obtained in [15], [16]. This is achieved by developing a model that first studies the impact of additional M2M traffic on existing H2H users when the s -ALOHA scheme is used to control RACH access.…”

Section: Introductionmentioning

confidence: 99%

“…The aggregated traffic can be obtained from the fraction of traffic (from the request) being retransmitted for the ith time as T total T total (16) and the throughput is…”

Section: Application Scenariomentioning

confidence: 99%

“…Chu et al [13], [14] use a Qlearning slot selection strategy and show that it is a better scheme than blind transmission in wireless sensor networks (WSNs). Our recent work in [15], [16] developed a simulation model of the QL-RACH and FB-QL-RA CH schemes. The present paper introduces a novel scheme that allows dynamic adaptation of frame size as an effective implementation of the FB-QL-RACH scheme.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent RACH Access Techniques to Support M2M Traffic in Cellular Networks

Bello

Mitchell

Grace

2018

IEEE Trans. Veh. Technol.

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ReuseItems deposited in White Rose Research Online are protected by copyright, with all rights reserved unless indicated otherwise. They may be downloaded and/or printed for private study, or other acts as permitted by national copyright laws. The publisher or other rights holders may allow further reproduction and re-use of the full text version. This is indicated by the licence information on the White Rose Research Online record for the item. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. Index Terms-M achine-to-machine, medium access control, cellular networks, Q-learning, ALOHA, RACH.I. INTRODUCTION ecent technological developments are changing the perception of wireless communication from the traditional human-centric view towards human independent communications. This is due to the increase in the number of devices that require connection to wireless networks. It has been argued in [1] that there are more electrical/electronic or mechanical objects in the world than people. It is estimated in [2], [3] that by 2020, up to 50 billion devices will require access to a communication network for industrial and domestic applications. This number is significant compared to the estimated human population of 8.3 billion. With this large difference in the ratio of the number of machines to humans, it will be difficult for such devices to be directly controlled by humans, and hence there is a need for them to communicate amongst themselves, with or without human intervention. This can be achieved through what is commonly referred to as Copyright (c) M2M communication will enable interaction between various devices with or without human intervention. M2M devices may be sensors, actuators, embedded processors, radio frequency identification (RFID) tags, smart meters, etc., [7]. The devices may be connected using wired or wireless access networks. Although wired networks are considered to be reliable and secure, they are very expensive to roll out and are not very flexible. As a result, many standards are not considering wired networks as an option for M2M communication. On the other hand, a wireless network is capable of providing excellent coverage, flexibility, mobility , and roaming capability. Hence, wireless access networks, which may be short range or long range (e.g. cellular), are considered as the most suitable option to deploy M2M communication [6].To realise cellular M2M communication, different wireless communication standardisation bodies , including 3GPP, are actively involved in research to provide global standards. Initial access to a cellular network is through the random access channel (RACH) which has a limited capacity. One of the majo r challenges identified by 3GPP in supporting M2M communication is the potential for RACH overload, due to the significant increase in traffic load that will arise from large numbers of M2M devices....

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Section: Application Scenariomentioning

confidence: 99%

Section: Application Scenariomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The aggregated traffic can be obtained from the fraction of traffic (from the request) being retransmitted for the ith time as T total T total (16) and the throughput is…”

Section: Application Scenariomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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