Towards Transparent Deep Learning: Neural Precoder Design for Downlink RSMA
The non-convexity of rate-splitting precoder design precludes the direct use of efficient convex optimization algorithms. Instead, successive convex approximation (SCA)-based methods have emerged as a promising approach for precoder design in rate-splitting multiple access (RSMA) systems. Although S...
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| Published in: | IEEE transactions on communications p. 1 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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2025
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| ISSN: | 0090-6778, 1558-0857 |
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| Abstract | The non-convexity of rate-splitting precoder design precludes the direct use of efficient convex optimization algorithms. Instead, successive convex approximation (SCA)-based methods have emerged as a promising approach for precoder design in rate-splitting multiple access (RSMA) systems. Although SCA-based algorithms deliver satisfactory performance, their lengthy optimization process and high computational complexity-due to repeatedly solving approximate problems-hinder real-time implementation. To address this challenge, we propose an alternating direction method of multipliers (ADMM)-induced deep learning network (AIDLN)-based precoder design for multiuser downlink communications, leveraging the strengths of both model-based optimization and data-driven deep learning. Specifically, we first develop an iterative precoding algorithm based on ADMM, where each subproblem admits a closed-form solution. Next, we unfold the iterations of this ADMM-based algorithm into the layers of a neural network, constructing a deep-unfolding architecture. To accelerate convergence, we introduce trainable parameters alongside the ADMM hyperparameters. Numerical results demonstrate that our AIDLN-based precoder achieves superior performance compared to traditional model-based optimization while maintaining low computational complexity, making it suitable for real-time applications. |
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| AbstractList | The non-convexity of rate-splitting precoder design precludes the direct use of efficient convex optimization algorithms. Instead, successive convex approximation (SCA)-based methods have emerged as a promising approach for precoder design in rate-splitting multiple access (RSMA) systems. Although SCA-based algorithms deliver satisfactory performance, their lengthy optimization process and high computational complexity-due to repeatedly solving approximate problems-hinder real-time implementation. To address this challenge, we propose an alternating direction method of multipliers (ADMM)-induced deep learning network (AIDLN)-based precoder design for multiuser downlink communications, leveraging the strengths of both model-based optimization and data-driven deep learning. Specifically, we first develop an iterative precoding algorithm based on ADMM, where each subproblem admits a closed-form solution. Next, we unfold the iterations of this ADMM-based algorithm into the layers of a neural network, constructing a deep-unfolding architecture. To accelerate convergence, we introduce trainable parameters alongside the ADMM hyperparameters. Numerical results demonstrate that our AIDLN-based precoder achieves superior performance compared to traditional model-based optimization while maintaining low computational complexity, making it suitable for real-time applications. |
| Author | Jin, Liang Li, Guo Wang, Chao Li, Zan Zhang, Haibin |
| Author_xml | – sequence: 1 givenname: Chao orcidid: 0000-0003-0187-6453 surname: Wang fullname: Wang, Chao email: drchaowang@126.com organization: Integrated Service Networks Lab, Xidian University, Xi'an, China – sequence: 2 givenname: Guo orcidid: 0000-0001-6273-9028 surname: Li fullname: Li, Guo email: liguo@stu.xidian.edu.cn organization: School of Cyber Engineering, Xidian University, Xi'an, China – sequence: 3 givenname: Zan orcidid: 0000-0002-5207-6504 surname: Li fullname: Li, Zan organization: Integrated Service Networks Lab, Xidian University, Xi'an, China – sequence: 4 givenname: Haibin orcidid: 0000-0002-0080-6341 surname: Zhang fullname: Zhang, Haibin email: hbzhang@mail.xidian.edu.cn organization: School of Cyber Engineering, Xidian University, Xi'an, China – sequence: 5 givenname: Liang orcidid: 0000-0001-6464-6263 surname: Jin fullname: Jin, Liang email: liangjin@263.net organization: Information Engineering University, Zhengzhou, China |
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| Snippet | The non-convexity of rate-splitting precoder design precludes the direct use of efficient convex optimization algorithms. Instead, successive convex... |
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| SubjectTerms | alternating direction method of multipliers Approximation algorithms Computational complexity Computational modeling Covariance matrices Deep learning deep unfolding Downlink Iterative algorithms Mathematical models Optimization precoder design rate splitting multiple access Spectral efficiency |
| Title | Towards Transparent Deep Learning: Neural Precoder Design for Downlink RSMA |
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