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\begin{document}

\title{Deep Learning Based Beamforming for MISO Systems with Dirty-Paper Coding}



\author[1]{Xingliang Lou}%
\author[1]{Wenchao Xia}%
\author[1]{Zhao Haitao}%
\author[2]{Wanli Wen}%
\author[3]{Xiaohui Li}%
\author[4]{Bin Wang}%
\affil[1]{Nanjing University of Posts and Telecommunications}%
\affil[2]{Chongqing University}%
\affil[3]{Taiyuan University of Technology}%
\affil[4]{Hangzhou Hikvision Digital Technology Co Ltd}%


\vspace{-1em}



  
  \date{June 10, 2024}


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\begin{abstract}
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%
\end{abstract}%



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