This letter studies deep learning (DL) approaches to optimize beamforming vectors in downlink multi-user multi-antenna systems that can be universally applied to arbitrarily given transmit power limitation at a base station. We exploit the sum power budget as side information so that deep neural networks (DNNs) can effectively learn the impact of the power constraint in the beamforming optimization. Consequently, a single training process is sufficient for the proposed universal DL approach, whereas conventional methods need to train multiple DNNs for all possible power budget levels. Numerical results demonstrate the effectiveness of the proposed DL methods over existing schemes.

中文翻译：

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通用 MISO 波束成形的深度学习方法

这封信研究了在下行链路多用户多天线系统中优化波束成形矢量的深度学习 (DL) 方法，这些方法可以普遍应用于基站的任意给定发射功率限制。我们利用总功率预算作为辅助信息，以便深度神经网络 (DNN) 可以有效地学习功率约束在波束成形优化中的影响。因此，对于所提出的通用 DL 方法来说，单个训练过程就足够了，而传统方法需要针对所有可能的功率预算水平训练多个 DNN。数值结果证明了所提出的 DL 方法对现有方案的有效性。