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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2023.8
- 수록면
- 897 - 905 (9page)
- DOI
- 10.7840/kics.2023.48.8.897
이용수
초록· 키워드
오류제보하기
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.
목차
ABSTRACT
Ⅰ. Introduction
Ⅱ. System Model
Ⅲ. Proposed Scheme
Ⅳ. Simulation and Result
Ⅴ. Conclusion
References
참고문헌 (19)
참고문헌 신청
A. Jagannath, J. Jagannath, and T. Melodia, “Redefining wireless communication for 6G: Signal processing meets deep learning with deep unfolding,” IEEE Trans. Artif. Intell., vol. 2, no. 6, pp. 528-536, Dec. 2021. (https://doi.org/10.1109/TAI.2021.3108129
H. Huang, Y. Yang, Z. Ding, H. Wang, H. Sari, and F. Adachi, “Deep learning-based sum data rate and energy efficiency optimization for MIMO-NOMA systems,” IEEE Trans. Wirel. Commun., vol. 19, no. 8, pp. 5373-5388, Aug. 2020. (https://doi.org/10.1109/TWC.2
A. Zappone, M. Di Renzo, and M. Debbah, “Wireless networks design in the era of deep learning: Model-based, AI-based, or both?,” IEEE Trans. Commun., vol. 67, no. 10, pp. 7331-7376, Oct. 2019. (https://doi.org/10.1109/TCOMM.2019.2924010)
B. Narottama and S. Y. Shin, “Quantum neural networks for resource allocation in wireless communications,” IEEE Trans. Wirel. Commun., vol. 21, no. 2, pp. 1103-1116, Feb. 2022. (https://doi.org/10.1109/TWC.2021.3102139)
Q. Hu, Y. Cai, Q. Shi, K. Xu, G. Yu, and Z. Ding, “Iterative algorithm induced deepunfolding neural networks: Precoding design for multiuser MIMO systems,” IEEE Trans. Wirel. Commun., vol. 20, no. 2, pp. 1394-1410, Feb. 2021. (https://doi.org/10.1109/TWC.
H. Huang, Y. Yang, Z. Ding, H. Wang, H. Sari, and F. Adachi, “Deep learning-based sum data rate and energy efficiency optimization for MIMO-NOMA systems,” IEEE Trans. Wirel. Commun., vol. 19, no. 8, pp. 5373-5388, Aug. 2020. (https://doi.org/10.1109/TWC.2
A. Zappone, M. Di Renzo, and M. Debbah, “Wireless networks design in the era of deep learning: Model-based, AI-based, or both?,” IEEE Trans. Commun., vol. 67, no. 10, pp. 7331-7376, Oct. 2019. (https://doi.org/10.1109/TCOMM.2019.2924010)
B. Narottama and S. Y. Shin, “Quantum neural networks for resource allocation in wireless communications,” IEEE Trans. Wirel. Commun., vol. 21, no. 2, pp. 1103-1116, Feb. 2022. (https://doi.org/10.1109/TWC.2021.3102139)
Q. Hu, Y. Cai, Q. Shi, K. Xu, G. Yu, and Z. Ding, “Iterative algorithm induced deepunfolding neural networks: Precoding design for multiuser MIMO systems,” IEEE Trans. Wirel. Commun., vol. 20, no. 2, pp. 1394-1410, Feb. 2021. (https://doi.org/10.1109/TWC.
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