A New Deep-Q-Learning-Based Transmission Scheduling Mechanism for the Cognitive Internet of Things

Zhu, Jiang; Song, Yonghui; Jiang, Dingde; Song, Houbing

doi:10.1109/jiot.2017.2759728

Cited by 265 publications

(138 citation statements)

References 42 publications

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“…The optimal state action information is stored in a look-up table. 12 The channel capacity of each MVNO for the proposed approach is higher than that of the existing approach in the literature. We have used MATLAB simulator to simulate the algorithm.…”

Section: Numerical Results and Discussionmentioning

confidence: 85%

“…Then, we train the QL model with obtained information and use the trained model to map the state and action in the next phase. 12 Furthermore, we observed in Figure 5 that the channel capacity of MVNO 4 is higher than that of other MVNOs for the proposed approach because beam scheduling for MVNO 4 is done more quickly than others. We have used tic-toc and run the simulation from MATLAB to determine the simulation results for the QL to achieve the best policy for the given best action.…”

Section: Numerical Results and Discussionmentioning

confidence: 86%

“…[8][9][10][11][12] Brighente and Tomasin 8 have studied the power allocation and beam scheduling for the downlink in high speed large frequency communications such as 5G cellular millimeter wave communications to serve fixed rate users as well as variable rate users for device-to-device communications and for high data rate services, respectively. [8][9][10][11][12] Brighente and Tomasin 8 have studied the power allocation and beam scheduling for the downlink in high speed large frequency communications such as 5G cellular millimeter wave communications to serve fixed rate users as well as variable rate users for device-to-device communications and for high data rate services, respectively.…”

Section: Introductionmentioning

confidence: 99%

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On the machine learning–based smart beamforming for wireless virtualization with large‐scale MIMO system

Sapavath

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In this paper, we study a machine learning enabled smart beam scheduling approach for wireless virtualization in large‐scale multiple‐input–multiple‐output (MIMO) system. Large‐scale MIMO is regarded as an emerging technology to enhance data rate of future wireless networks and the wireless virtualization is regarded as an efficient paradigm to enhance the radio frequency (RF) spectrum utilization by subleasing RF slices of wireless infrastructure providers to mobile virtual network operators (MVNOs). We leverage machine learning approach for scheduling the beams in large‐scale MIMO where RF slices with the help of subsets of antennas are subleased for MVNOs. Performance of the proposed approach is evaluated using simulation results. The results show that the proposed approach outperforms the state of the art approach.

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Section: Numerical Results and Discussionmentioning

confidence: 85%

Section: Numerical Results and Discussionmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On the machine learning–based smart beamforming for wireless virtualization with large‐scale MIMO system

Sapavath

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2019

Trans Emerging Tel Tech

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“…Internet of Things (IoT) is a promising technology that allows communications among sensor nodes, a continuous exchange of context between sender and receiver, and the ability to join and leave the network spontaneously [3][4][5][6]. IoT has two essential properties: self-adaptation and self-organization.…”

mentioning

confidence: 99%

“…IoT has two essential properties: self-adaptation and self-organization. However, the great challenges for future IoT based multimedia applications are the spectrum scarcity problem, high implementation cost, high energy consumption, and low sum rate as compared with more general radio platforms due to the rapid increase in the number of wireless devices present in future IoT [6] systems. In order to support the applicability of CR for future IoT, the cluster based cognitive radio relay network (CCRRN) which utilises reporting frameworks is a promising approach.…”

mentioning

confidence: 99%

An enhanced sum rate in the cluster based cognitive radio relay network using the sequential approach for the future Internet of Things

Miah

Schukat

Barrett

2018

Hum. Cent. Comput. Inf. Sci.

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The cognitive radio relay plays a vital role in cognitive radio networking (CRN), as it can improve the cognitive sum rate, extend the coverage, and improve the spectral efficiency. However, cognitive relay aided CRNs cannot obtain a maximal sum rate, when the existing sensing approach is applied to a CRN. In this paper, we present an enhanced sum rate in the cluster based cognitive radio relay network utilizing a reporting framework in the sequential approach. In this approach a secondary user (SU) extends its sensing time until right before the beginning of its reporting time slot by utilizing the reporting framework. Secondly all the individual measurement results from each relay aided SU are passed on to the corresponding cluster head (CH) through a noisy reporting channel, while the CH with a soft-fusion report is forwarded to the fusion center that provides the final decision using the n-out-of-k-rule. With such extended sensing intervals and amplified reporting, a better sensing performance can be obtained than with a conventional non-sequential approach, therefore making it applicable for the future Internet of Things. In addition, the sum rate of the primary network and CCRRN are also investigated for the utilization reporting framework in the sequential approach with a relay using the n-out-of-k rule. By simulation, we show that the proposed sequential approach with a relay (Lemma 2) provides a significant sum rate gain compared to the conventional non-sequential approach with no relay (Lemma 1) under any condition.

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Realization of Business Intelligence using Machine Learning

Rath¹

2021

Internet of Things in Business Transformation

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A New Deep-Q-Learning-Based Transmission Scheduling Mechanism for the Cognitive Internet of Things

Cited by 265 publications

References 42 publications

On the machine learning–based smart beamforming for wireless virtualization with large‐scale MIMO system

On the machine learning–based smart beamforming for wireless virtualization with large‐scale MIMO system

An enhanced sum rate in the cluster based cognitive radio relay network using the sequential approach for the future Internet of Things

Realization of Business Intelligence using Machine Learning

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