This paper considers the sum spectral efficiency (SE) optimization problem in multi-cell Massive MIMO systems with a varying number of active users. This is formulated as a joint pilot and data power control problem. Since the problem is non-convex, we first derive a novel iterative algorithm that obtains a stationary point in polynomial time. To enable real-time implementation, we also develop a deep learning solution. The proposed neural network, PowerNet, only uses the large-scale fading information to predict both the pilot and data powers. The main novelty is that we exploit the problem structure to design a single neural network that can handle a dynamically varying number of active users; hence, PowerNet is simultaneously approximating many different power control functions with varying number inputs and outputs. This is not the case in prior works and thus makes PowerNet an important step towards a practically useful solution. Numerical results demonstrate that PowerNet only loses 2% in sum SE, compared to the iterative algorithm, in a nine-cell system with up to 90 active users per in each coherence interval, and the runtime was only 0.03 ms on a graphics processing unit (GPU). When good data labels are selected for the training phase, PowerNet can yield better sum SE than by solving the optimization problem with one initial point.

中文翻译：

---

具有不同用户活动的蜂窝大规模 MIMO 中的功率控制：深度学习解决方案

本文考虑了具有不同活动用户数量的多小区大规模 MIMO 系统中的总频谱效率 (SE) 优化问题。这被表述为联合导频和数据功率控制问题。由于问题是非凸的，我们首先推导出一种新颖的迭代算法，该算法在多项式时间内获得一个平稳点。为了实现实时实施，我们还开发了深度学习解决方案。所提出的神经网络 PowerNet 仅使用大规模衰落信息来预测导频和数据功率。主要的新颖之处在于我们利用问题结构设计了一个可以处理动态变化的活跃用户数量的单个神经网络；因此，PowerNet 同时使用不同数量的输入和输出来逼近许多不同的功率控制功能。这在之前的工作中并非如此，因此使 PowerNet 成为迈向实用解决方案的重要一步。数值结果表明，与迭代算法相比，PowerNet 在每个相干间隔内最多 90 个活跃用户的 9 单元系统中仅损失 2% 的和 SE，并且在图形处理单元上的运行时间仅为 0.03 ms（ GPU）。当为训练阶段选择好的数据标签时，PowerNet 可以产生比用一个初始点解决优化问题更好的和 SE。03 毫秒在图形处理单元 (GPU) 上。当为训练阶段选择好的数据标签时，PowerNet 可以产生比用一个初始点解决优化问题更好的和 SE。03 毫秒在图形处理单元 (GPU) 上。当为训练阶段选择好的数据标签时，PowerNet 可以产生比用一个初始点解决优化问题更好的和 SE。