论文标题
学习辅助灵活的梯度下降方法，用于误差波束成形
A Learning Aided Flexible Gradient Descent Approach to MISO Beamforming
论文作者
论文摘要
本文提出了学习辅助梯度下降（lagd）算法，以解决多输入单输出（MISO）波束成形的加权总和（WSR）最大化问题。所提出的LAGD算法通过基于隐式梯度下降的迭代直接优化了Transmit预编码器，在每个迭代中，优化策略由神经网络确定，因此是动态和适应性的。在问题的每个实例中，该网络是随机初始化的，并在整个迭代解决方案过程中进行了更新。因此，可以以任何信号噪声比（SNR）实现LAGD算法，并且对于任意天线/用户数量，在部署之前不需要标记的数据或培训。数值结果表明，LAGD算法的表现可以优于众所周知的WMMSE算法以及其他具有适度计算复杂性的基于学习的解决方案。我们的代码可在https://github.com/xiagroup/lagd上找到。
This paper proposes a learning aided gradient descent (LAGD) algorithm to solve the weighted sum rate (WSR) maximization problem for multiple-input single-output (MISO) beamforming. The proposed LAGD algorithm directly optimizes the transmit precoder through implicit gradient descent based iterations, at each of which the optimization strategy is determined by a neural network, and thus, is dynamic and adaptive. At each instance of the problem, this network is initialized randomly, and updated throughout the iterative solution process. Therefore, the LAGD algorithm can be implemented at any signal-to-noise ratio (SNR) and for arbitrary antenna/user numbers, does not require labelled data or training prior to deployment. Numerical results show that the LAGD algorithm can outperform of the well-known WMMSE algorithm as well as other learning-based solutions with a modest computational complexity. Our code is available at https://github.com/XiaGroup/LAGD.
