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Quantum Deep Unfolding Based Resource Allocation Optimization for Future Wireless Networks
Triwidyastuti Jamaluddin,나로타마 바스카라,신수용 한국통신학회 2023 韓國通信學會論文誌 Vol.48 No.8
This paper introduces Quantum Deep Unfolding (QDU), a technique for optimizing power allocation and transmit precoding in multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) systems. Solving the optimization problem in such systems poses a significant challenge due to its high computational complexity and non-convex nature, which increases the risk of being stuck at a local minimum. In order to address this issue, QDU leverages an iterative algorithm and analytical derivation to enhance the sum rate performance and training processes by optimizing power allocation and transmit precoding. The proposed approach integrates a Quantum Neural Network (QNN) induced by an iterative deep unfolding algorithm with a learning solution inspired by the training process. At each QDU layer, the iterative optimization involving the Projected Gradient Descent (PGD) operator is unfolded to learn the crucial parameters. The objective of QDU is to maximize the achievable sum rate while simultaneously reducing computational complexity.
Statistical Quantum Federated Learning for NOMA Power Allocation
Bhaskara Narottama,Triwidyastuti Jamaluddin,Soo Young Shin 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.6
This study employs the statistical Quantum Federated Learning (sQFL) to optimize NOMA power allocation. Com-pared to the existing Federated Learning (FL), sQFL does not require other edges to perform neural network inferences. The other edge only required to transmit the statistical information to the cloud.
Integration of Quantum Variational Circuit and SVD for Precoding Optimization
Bhaskara Narottama,Triwidyastuti Jamaluddin,Soo Young Shin 한국통신학회 2022 한국통신학회 학술대회논문집 Vol.2022 No.2
In this study, integration of quantum variational circuit and singular-value-decomposition-based precoding (QV-SVD) is presented to optimize MIMO-NOMA precoding. Considering imperfect channel information, the objective of the optimization is to maximize the achievable sum rate.
Statistical Quantum Federated Learning for NOMA Power Allocation
Narottama Bhaskara,Jamaluddin Triwidyastuti,Soo Young Shin 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.6
This study employs the statistical Quantum Federated Learning (sQFL) to optimize NOMA power allocation. Compared to the existing Federated Learning (FL), sQFL does not require other edges to perform neural network inferences. The other edge only required to transmit the statistical information to the cloud.