This paper investigates the joint data and pilot power optimization for
maximum sum spectral efficiency (SE) in multi-cell Massive MIMO systems, which
is a non-convex problem. We first propose a new optimization algorithm,
inspired by the weighted minimum mean square error (MMSE) approach, to obtain a
stationary point in polynomial time. We then use this algorithm together with
deep learning to train a convolutional neural network to perform the joint data
and pilot power control in sub-millisecond runtime, making it suitable for
online optimization in real multi-cell Massive MIMO systems. The numerical
result demonstrates that the solution obtained by the neural network is $1\%$
less than the stationary point for four-cell systems, while the sum SE loss is
$2\%$ in a nine-cell system.

本研究探讨了在多小区 Massive MIMO 系统中最大化总和频谱效率的数据和导频功率优化问题，提出了一种新的基于最小均方误差加权的优化算法，并结合深度学习训练卷积神经网络来实现数据和导频功率控制的联合优化，数值结果表明，在四小区系统中，神经网络得到的解与静态点相比少 1%，将总和频谱效率损失控制在 2% 以内。