Digital Precoder-Combiner and Power Allocation Optimization in MU MIMO-NOMA: A Quantum Neural Network Approach 


Vol. 49,  No. 12, pp. 1739-1746, Dec.  2024
10.7840/kics.2024.49.12.1739


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  Abstract

This paper proposes a quantum neural network (QNN) to address joint optimization in wireless communication. By utilizing the advantages of quantum entanglement and superposition in quantum computing and machine learning, QNN can solve optimization problems with lower complexity than classical neural networks, due to its parallel processing capabilities. Specifically, this study applies QNN to jointly optimize digital precoder-combiner and power allocation in a multi-user multiple-input multiple-output non-orthogonal multiple access (MU MIMO-NOMA) system. The performance of the QNN is analyzed and presented.

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[IEEE Style]

L. S. Waliani and S. Y. Shin, "Digital Precoder-Combiner and Power Allocation Optimization in MU MIMO-NOMA: A Quantum Neural Network Approach," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 12, pp. 1739-1746, 2024. DOI: 10.7840/kics.2024.49.12.1739.

[ACM Style]

Lia Suci Waliani and Soo Young Shin. 2024. Digital Precoder-Combiner and Power Allocation Optimization in MU MIMO-NOMA: A Quantum Neural Network Approach. The Journal of Korean Institute of Communications and Information Sciences, 49, 12, (2024), 1739-1746. DOI: 10.7840/kics.2024.49.12.1739.

[KICS Style]

Lia Suci Waliani and Soo Young Shin, "Digital Precoder-Combiner and Power Allocation Optimization in MU MIMO-NOMA: A Quantum Neural Network Approach," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 12, pp. 1739-1746, 12. 2024. (https://doi.org/10.7840/kics.2024.49.12.1739)
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