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Open Access

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

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
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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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Intelligent and Converged Networks
Pages 295-305
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.










Received: 08 June 2021
Revised: 08 September 2021
Accepted: 29 September 2021
Published: 30 December 2021
© All articles included in the journal are copyrighted to the ITU and TUP.

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