Article: Power optimisation of wireless sensing network through quantum deep learning Journal: International Journal of Nanotechnology (IJNT) 2023 Vol.20 No.5/6/7/8/9/10 pp.523 - 535 Abstract: In current research, secure wireless communication system with high data rate is obtained by quantum computing (QC). Main requirement of using wireless infrastructure is to reduce cost and transmit faster in network. To speed up the data rate of transfer, QC has been combined with machine learning algorithms. Then the large volume of data that are transferred, stored and processed in wireless systems leads high energy of the system. This article proposes a power minimisation approach with QC based approach called improved sequential parametric convex approximation (ISPCA) trained by deep learning algorithm called graph convolutional neural network (GCNN). The proposed approach has been used to minimise the power and enhance the energy efficiency of the wireless communication systems such as LTE/5G. Evaluated result shows that our proposed consumes only 44.82J energy for computing 1000 samples. Proposed technique outperforms existing by consuming much less energy. Inderscience Publishers - linking academia, business and industry through research

Title: Power optimisation of wireless sensing network through quantum deep learning

Authors: Zhongzhen Yan; Kewei Zhou; Feng Guo; Na Hou; Jiangyi Du

Addresses: School of Computer Science and Technology, Hubei University of Technology, No. 28 Nanli Road, Hongshan District, Wuhan City, Hubei Province, 430068, China ' School of Computer Science and Technology, Hubei University of Technology, No. 28 Nanli Road, Hongshan District, Wuhan City, Hubei Province, 430068, China ' China Railway Seventh Bureau Group Electrical Engineering Co. Ltd., 226 Jinshui Road, Jinshui District, Zhengzhou City, Henan Province, 450052, China ' China Railway Seventh Bureau Group Electrical Engineering Co. Ltd., 226 Jinshui Road, Jinshui District, Zhengzhou City, Henan Province, 450052, China ' School of Computer Science and Technology, Hubei University of Technology, No. 28 Nanli Road, Hongshan District, Wuhan City, Hubei Province, 430068, China

Abstract: In current research, secure wireless communication system with high data rate is obtained by quantum computing (QC). Main requirement of using wireless infrastructure is to reduce cost and transmit faster in network. To speed up the data rate of transfer, QC has been combined with machine learning algorithms. Then the large volume of data that are transferred, stored and processed in wireless systems leads high energy of the system. This article proposes a power minimisation approach with QC based approach called improved sequential parametric convex approximation (ISPCA) trained by deep learning algorithm called graph convolutional neural network (GCNN). The proposed approach has been used to minimise the power and enhance the energy efficiency of the wireless communication systems such as LTE/5G. Evaluated result shows that our proposed consumes only 44.82J energy for computing 1000 samples. Proposed technique outperforms existing by consuming much less energy.

Keywords: wireless; 5G; SPCA; convolution network; deep learning; energy efficiency; power optimisation.

DOI: 10.1504/IJNT.2023.134009

International Journal of Nanotechnology, 2023 Vol.20 No.5/6/7/8/9/10, pp.523 - 535

Received: 20 May 2021
Accepted: 04 Aug 2021
Published online: 10 Oct 2023 *

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Title: Power optimisation of wireless sensing network through quantum deep learning

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