论文标题
通用味o束的深度学习方法
Deep Learning Methods for Universal MISO Beamforming
论文作者
论文摘要
这封信研究了深度学习（DL）的方法，以优化下行链路多用户多用户系统中的波束形成向量，这些系统可以普遍应用于基于基站的传输功率限制。我们利用总和力预算为侧面信息，以便深度神经网络（DNN）可以有效地学习功率约束在波束形成优化中的影响。因此，对于拟议的通用DL方法，单个培训过程就足够了，而常规方法需要培训多个DNN以达到所有可能的电力预算水平。数值结果证明了所提出的DL方法对现有方案的有效性。
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.
