In this paper, a general framework for deep learning-based power control methods for max-min, max-product and max-sum-rate optimization in uplink cell-free massive multiple-input multiple-output (CF mMIMO) systems is proposed. Instead of using supervised learning, the proposed method relies on unsupervised learning, in which optimal power allocations are not required to be known, and thus has low training complexity. More specifically, a deep neural network (DNN) is trained to learn the map between fading coefficients and power coefficients within short time and with low computational complexity. It is interesting to note that the spectral efficiency of CF mMIMO systems with the proposed method outperforms previous optimization methods for max-min optimization and fits well for both max-sum-rate and max-product optimizations.

翻译：本文提出了在无上链细胞大规模多投入多产出(CF MMIMO)系统中,以深层次学习为基础的最大最小、最大产品和最高和最高和最高和最高比率的电源控制方法总体框架。拟议方法不是使用监督的学习,而是依靠未经监督的学习,在这种学习中,不需要知道最佳的电源分配,因此培训的复杂程度较低。更具体地说,一个深层神经网络(DNN)接受了培训,以了解短期内和计算复杂性低的衰减系数和最高功率系数之间的分布图。值得注意的是,CF MMIMO系统的光谱效率与拟议方法的光谱效率相比,已经超越了以往的最大限度优化优化方法,适合最大和最大产品优化。