This letter investigates a learning solution for robust beamforming optimization in downlink multi-user systems. A base station (BS) identifies efficient multi-antenna transmission strategies only with imperfect channel state information (CSI) and its stochastic features. To this end, we propose a robust training algorithm where a deep neural network (DNN), which only accepts estimates and statistical knowledge of the perfect CSI, is optimized to fit to real-world propagation environment. Consequently, the trained DNN can provide efficient robust beamforming solutions based only on imperfect observations of the actual CSI. Numerical results validate the advantages of the proposed learning approach compared to conventional schemes.

中文翻译：

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学习 MISO 下行链路系统的鲁棒波束成形

这封信研究了下行链路多用户系统中稳健波束成形优化的学习解决方案。基站 (BS) 仅利用不完善的信道状态信息 (CSI) 及其随机特征来识别有效的多天线传输策略。为此，我们提出了一种稳健的训练算法，其中对仅接受完美 CSI 的估计和统计知识的深度神经网络 (DNN) 进行了优化，以适应现实世界的传播环境。因此，经过训练的 DNN 可以仅基于对实际 CSI 的不完美观察提供有效的稳健波束成形解决方案。与传统方案相比，数值结果验证了所提出的学习方法的优势。