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Deep unfolding based optimization framework of fractional programming for wireless communication systems

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Abstract

Multiple-ratio fractional programming (FP) has been applied to the optimization of wireless communication networks, because of the signal-to-interference-plus-noise ratio terms. However, in order to find solutions, FP requires iterative processes and may include complex operations, such as binary search, eigen-decomposition, and matrix inversion. Thus, the computational complexity and delay may violate the real-time requirements of delay-sensitive applications. To tackle this challenge, we propose a deep unfolding FP (DUFP) optimization framework naturally incorporating expert knowledge and deep neural networks. By unfolding the iterative process to neural network layers, the proposed DUFP framework trains a neural network with a little number of trainable parameters offline and then finds solutions online with reduced computational complexity. In addition, the proposed DUFP approach is applied to downlink beamforming problems to validate its efficiency. Finally, simulation results show that the proposed DUFP approach can achieve a balance between performance and computational complexity.

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Acknowledgements

This work was supported by the National Key R&D Program of China (No.2021ZD0140405), the Jiangsu Natural Science Foundation for Distinguished Young Scholars (No.BK20220054), the National Natural Science Foundation of China (No.62201285), the Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu under Grant (No.BK20212001), the China Postdoctoral Science Foundation (No. 2022M722669), Young Elite Scientists Sponsorship Program by CAST (No. 2022QNRC001), and the Natural Science Research Project of Jiangsu Higher Education Institutions (No.21KJB510027).

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Authors and Affiliations

  1. The Jiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China

    Haitao Zhao, Zhiyuan Chen & Wenchao Xia

  2. The School of Communications and Electronics Engineering, East China Normal University, Shanghai, 200241, China

    Kun Guo

  3. The School of Mathematics and Information Technology, Jiangsu Second Normal University, Nanjing, 210003, China

    Yiyang Ni

  4. Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, 100853, China

    Kunlun He

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  1. Haitao Zhao
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  2. Zhiyuan Chen
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  3. Wenchao Xia
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  4. Kun Guo
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Correspondence to Wenchao Xia or Kunlun He.

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Zhao, H., Chen, Z., Xia, W. et al. Deep unfolding based optimization framework of fractional programming for wireless communication systems. Wireless Netw 29, 2313–2320 (2023). https://doi.org/10.1007/s11276-023-03304-w

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