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Intelligent and Converged Networks 2021, 2(4): 295-305 https://doi.org/10.23919/ICN.2021.0021
Open Access | Issue | Published: 30 December 2021

QSPCA: A two-stage efficient power control approach in D2D communication for 5G networks

Show Author's Information Saurabh Chandra1,  Prateek1,  Rohit Sharma1,  Rajeev Arya1( ),  Korhan Cengiz2
Wireless Sensor Network Lab, the Department of Electronics and Communication Engineering, National Institute of Technology Patna, Ashok Rajpath, Patna 800005, India
Department of Electrical-Electronics Engineering, Trakya University, Edirne 22030, Turkey
Keywords:
machine learning, Internet of Things (IoT), power control, 5G, support vector machine (SVM), interference, device-to-device (D2D), Q-learning
Cite this article:
Chandra S, Prateek, Sharma R, et al. QSPCA: A two-stage efficient power control approach in D2D communication for 5G networks. Intelligent and Converged Networks, 2021, 2(4): 295-305. https://doi.org/10.23919/ICN.2021.0021
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Abstract Full text About this article

The existing literature on device-to-device (D2D) architecture suffers from a dearth of analysis under imperfect channel conditions. There is a need for rigorous analyses on the policy improvement and evaluation of network performance. Accordingly, a two-stage transmit power control approach (named QSPCA) is proposed: First, a reinforcement Q-learning based power control technique and; second, a supervised learning based support vector machine (SVM) model. This model replaces the unified communication model of the conventional D2D setup with a distributed one, thereby requiring lower resources, such as D2D throughput, transmit power, and signal-to-interference-plus-noise ratio as compared to existing algorithms. Results confirm that the QSPCA technique is better than existing models by at least 15.31% and 19.5% in terms of throughput as compared to SVM and Q-learning techniques, respectively. The customizability of the QSPCA technique opens up multiple avenues and industrial communication technologies in 5G networks, such as factory automation.


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About this article

QSPCA: A two-stage efficient power control approach in D2D communication for 5G networks

Show Author's information Saurabh Chandra1,  Prateek1, Rohit Sharma1, Rajeev Arya1( ), Korhan Cengiz2
Wireless Sensor Network Lab, the Department of Electronics and Communication Engineering, National Institute of Technology Patna, Ashok Rajpath, Patna 800005, India
Department of Electrical-Electronics Engineering, Trakya University, Edirne 22030, Turkey

Abstract

The existing literature on device-to-device (D2D) architecture suffers from a dearth of analysis under imperfect channel conditions. There is a need for rigorous analyses on the policy improvement and evaluation of network performance. Accordingly, a two-stage transmit power control approach (named QSPCA) is proposed: First, a reinforcement Q-learning based power control technique and; second, a supervised learning based support vector machine (SVM) model. This model replaces the unified communication model of the conventional D2D setup with a distributed one, thereby requiring lower resources, such as D2D throughput, transmit power, and signal-to-interference-plus-noise ratio as compared to existing algorithms. Results confirm that the QSPCA technique is better than existing models by at least 15.31% and 19.5% in terms of throughput as compared to SVM and Q-learning techniques, respectively. The customizability of the QSPCA technique opens up multiple avenues and industrial communication technologies in 5G networks, such as factory automation.

Keywords: machine learning, Internet of Things (IoT), power control, 5G, support vector machine (SVM), interference, device-to-device (D2D), Q-learning

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Publication history

Received: 08 June 2021
Revised: 08 September 2021
Accepted: 29 September 2021
Published: 30 December 2021
Issue date: December 2021

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© All articles included in the journal are copyrighted to the ITU and TUP.

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Acknowledgment

This work was supported by the Science and Engineering Research Board (SERB-DST), Govt. of India (No. EEQ/2019/000010).

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This work is available under the CC BY-NC-ND 3.0 IGO license: https://creativecommons.org/licenses/by-nc-nd/3.0/igo/

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