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한국통신학회논문지

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

Lia Suci Waliani, Soo Young Shin

Published Dec 23, 2024• 1 citation
Publisher

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.

DOI
10.7840/kics.2024.49.12.1739

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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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In this paper, the potential benefit of employing quantum neural networks (QNNs) for cell-free MIMO is explored. In particular, QNN-based scheme are used to optimize transmitter-user assignment in cell-free MIMO. The objective of the optimization is to maximize the minimum achieved sum rate. Although QNN has received increasing research attention owing to the potential benefit of quantum computation, its utilization for multi-transmitter scenario is still limited. As such, in this paper, we consider the QNN-based algorithm for optimal resourcea allocation in cell-free MIMO systems. We also demonstrate the advantage of our proposed QNN-based algorithm through the numerical results.