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Deep Learning Based Beamforming for MISO Systems with Dirty-Paper Coding
  • +3
  • Xingliang Lou,
  • Wenchao Xia,
  • Zhao Haitao,
  • Wanli Wen,
  • Xiaohui Li,
  • Bin Wang
Xingliang Lou
Nanjing University of Posts and Telecommunications

Corresponding Author:2022010109@njupt.edu.cn

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Wenchao Xia
Nanjing University of Posts and Telecommunications
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Zhao Haitao
Nanjing University of Posts and Telecommunications
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Wanli Wen
Chongqing University
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Xiaohui Li
Taiyuan University of Technology
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Bin Wang
Hangzhou Hikvision Digital Technology Co Ltd
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Beamforming technique can effectively improve the spectrum utilization of multi-antenna systems, while the dirty-paper coding (DPC) technique can reduce inter-user interference. In this letter, we aim to maximize the weighted sum-rate under power constraint in a multiple-input-single-output (MISO) system with the DPC. However, the existing methods of beamforming optimization mainly rely on customized iterative algorithms, which have high computational complexity. To address this issue, by utilizing the deep learning technique and the uplink-downlink duality, and carefully exploring the optimal solution structure, we devise a beamforming neural network (BFNNet), which includes a deep neural network module and a signal processing module. Besides, we use the modulus of the channel coefficients as the input of deep neural network, which reduces the input size. Simulation results show that a well-trained BFNNet can achieve near-optimal solutions, while significantly reducing computational complexity
20 Oct 2022Submitted to Electronics Letters
20 Oct 2022Submission Checks Completed
20 Oct 2022Assigned to Editor
22 Oct 2022Reviewer(s) Assigned
03 Nov 2022Review(s) Completed, Editorial Evaluation Pending
04 Dec 2022Editorial Decision: Revise Minor
11 Dec 20221st Revision Received
12 Dec 2022Submission Checks Completed
12 Dec 2022Assigned to Editor
12 Dec 2022Review(s) Completed, Editorial Evaluation Pending
20 Dec 2022Editorial Decision: Accept