In this paper, we investigate a user scheduling algorithm for massive multiple-input multiple-output (MIMO) systems over more general correlated Rician fading channels. To achieve low latency and high throughput, a new user scheduling algorithm based on deep learning (DL) is proposed, which exploits only statistical channel state information. The proposed scheduling network is trained to grasp the mapping from the statistical signal and interference pattern to the user scheduling decision through supervised learning. It can predict the optimal scheduling scheme from statistical CSI without iterative calculation after offline training. Simulation results demonstrate the superior performance of the proposed algorithm in terms of calculation time, and it achieves almost the same throughput as the optimal scheduling algorithm which is obtained through exhaustive search. Furthermore, with the normalization of the input data, the proposed scheduling network is robust to the change of the channel environment and the number of transmit antennas.

在本文中，我们研究了一种用于大规模多输入多输出 (MIMO) 系统在更一般的相关里斯衰落信道上的用户调度算法。为了实现低延迟和高吞吐量，提出了一种基于深度学习（DL）的新用户调度算法，该算法仅利用统计信道状态信息。所提出的调度网络经过训练，可以通过监督学习掌握从统计信号和干扰模式到用户调度决策的映射。离线训练后无需迭代计算，即可从统计CSI预测最优调度方案。仿真结果证明了该算法在计算时间方面的优越性能，并且实现了与穷举搜索得到的最优调度算法几乎相同的吞吐量。此外，随着输入数据的归一化，所提出的调度网络对信道环境和发射天线数量的变化具有鲁棒性。