Base Station Selection in M2M Communication Using Q-Learning Algorithm in LTE-A Networks

Mohammed, Abbas; Khwaja, Ahmed Shaharyar; Anpalagan, Alagan; Woungang, Isaac

doi:10.1109/aina.2015.160

Cited by 16 publications

(17 citation statements)

References 8 publications

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“…Moreover, in order to verify the effectiveness of our proposed ASCC scheme, we also run the Q-leaning-based base station-selection scheme as another baseline method for comparisons. 38 The proposed ASCC congestion control scheme would be investigated in two perspectives. First, the instant performances with fixed number of access attempts would be simulated in terms of access delay and collision probability.…”

Section: Simulation and Resultsmentioning

confidence: 99%

Autonomous and adaptive congestion control for machine-type communication in cellular network

Pan

Wen

et al. 2019

International Journal of Distributed Sensor Networks

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Machine-type communications have suffered serious congestion, overload, and poor quality of service problems in cellular networks, since the cellular networks are designed for human-to-human communications. Moreover, current solutions just focus on the congestion for single base station and cannot adaptively work in the dynamic and complex conditions. In this article, we provide an autonomous and adaptive attractor-selection-based congestion control scheme for massive access from machine-type communication devices based on the resource separation scheme. First, we introduce a feasible and self-adaptive extended attractor-selection mechanism to decide which base station to be chosen. Simultaneously, an effective estimation algorithm for the traffic load of base stations is also designed to represent the network traffic load without frequent information exchanges among devices or base stations. With the available access resources and estimated traffic load taken into consideration, massive access attempts can receive the decisions via the broadcast and adaptively choose proper stations for alleviation of the congestion and overload. Finally, simulation results show that the proposed attractor-selection-based congestion control scheme achieves better performance in terms of average access delay, collision probability, and throughput of the whole system, adaptively accommodating to unpredictable environments under cellular networks.

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Section: Simulation and Resultsmentioning

confidence: 99%

Autonomous and adaptive congestion control for machine-type communication in cellular network

Pan

Wen

et al. 2019

International Journal of Distributed Sensor Networks

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“…In [127], an RL scheme was developed to avoid access network congestion and minimize the packet delay by allocating MTC devices to appropriate BSs. In [188], a Qlearning algorithm (one of RL techniques) for the selection of appropriate BS for the MTC devices was proposed. With the algorithm, MTC devices are able to adapt to dynamic network traffic conditions and decide which BS is the best to be 1) High complexity in massive access with a large number of MTC devices; 2) bandwidth expansion required to increase the number of MTC devices that can be supported simultaneously.…”

Section: Machine Learning-assisted Iot Connectivitymentioning

confidence: 99%

IoT Connectivity Technologies and Applications: A Survey

et al. 2020

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The Internet of Things (IoT) is rapidly becoming an integral part of our life and also multiple industries. We expect to see the number of IoT connected devices explosively grows and will reach hundreds of billions during the next few years. To support such a massive connectivity, various wireless technologies are investigated. In this survey, we provide a broad view of the existing wireless IoT connectivity technologies and discuss several new emerging technologies and solutions that can be effectively used to enable massive connectivity for IoT. In particular, we categorize the existing wireless IoT connectivity technologies based on coverage range and review diverse types of connectivity technologies with different specifications. We also point out key technical challenges of the existing connectivity technologies for enabling massive IoT connectivity. To address the challenges, we further review and discuss some examples of promising technologies such as compressive sensing (CS) random access, non-orthogonal multiple access (NOMA), and massive multiple input multiple output (mMIMO) based random access that could be employed in future standards for supporting IoT connectivity. Finally, a classification of IoT applications is considered in terms of various service requirements. For each group of classified applications, we outline its suitable IoT connectivity options.

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“…Upon the selection of an action, the agent should analyse the new state that it has transitioned to. A reward function is defined for each action to check its correctness . A higher reward illustrates that the action had beneficial consequences, while a lower one illustrates that a different action needs to be tried out.…”

Section: Proposed Solutionmentioning

confidence: 99%

“…The agent will learn to take the best actions that maximise its long‐term rewards by using its own experience . In , RL‐based base station selection algorithm is proposed that allows the MTCDs to choose the base station in a self‐organising fashion: while authors in used quality of service performance measure to switch from one base station to another and it is the ratio between the device throughput and its delay. In , a distributed algorithm in which MTCDs share resources with a particular cellular user in a TDMA manner is proposed.…”

Section: Introductionmentioning

confidence: 99%

Resource allocation and congestion control in clustered M2M communication using Q‐learning

Hussain

Anpalagan

Khwaja

et al. 2016

Trans. Emerging Tel. Tech.

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In this paper, we apply a Q‐learning algorithm to carry out slot assignment for machine type communication devices (MTCDs) in machine‐to‐machine communication. We first make use of a K‐means clustering algorithm to overcome the congestion problem in an machine‐to‐machine network where each MTCD is associated with one controller. Subsequently, we formulate the slot selection problem as an optimisation problem. Then, we present a solution using the Q‐learning algorithm to select conflict‐free slot assignment in a random access network with MTCD controllers. The performance of the solution is dependent on parameters such as learning rate and reward. We thoroughly analyse the performance of the proposed algorithm considering different parameters related to its operation. The convergence time, that is, the time required to reach a solution, decreases with increasing value of learning rate, whereas the convergence probability increases. In addition, for smaller values of learning rate, the convergence time decreases with increasing reward values. We also compare with simple ALOHA and channel‐based scheduled allocation and show that the proposed Q‐learning‐based technique has a higher probability of assigning slots compared with these techniques. Copyright © 2016 John Wiley & Sons, Ltd.

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Base Station Selection in M2M Communication Using Q-Learning Algorithm in LTE-A Networks

Cited by 16 publications

References 8 publications

Autonomous and adaptive congestion control for machine-type communication in cellular network

Autonomous and adaptive congestion control for machine-type communication in cellular network

IoT Connectivity Technologies and Applications: A Survey

Resource allocation and congestion control in clustered M2M communication using Q‐learning

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