Power optimisation of wireless sensing network through quantum deep learning Online publication date: Tue, 10-Oct-2023
by Zhongzhen Yan; Kewei Zhou; Feng Guo; Na Hou; Jiangyi Du
International Journal of Nanotechnology (IJNT), Vol. 20, No. 5/6/7/8/9/10, 2023
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.
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