Next-generation wireless networks need to handle massive user access effectively. This paper addresses the problem of joint group scheduling and multicast beamforming for downlink transmission with many active user groups. Aiming to maximize the minimum user throughput, we propose a three-phase approach to tackle this difficult joint optimization problem efficiently. In Phase 1, we utilize the optimal multicast beamforming structure obtained recently to find the group-channel directions for all groups. We propose two low-complexity group scheduling algorithms in Phase 2, which determine the subset of groups in each time slot sequentially and the total number of time slots required for all groups. The first algorithm measures the level of spatial separation among groups and selects the dissimilar groups that maximize the minimum user rate into the same time slot. In contrast, the second algorithm first identifies the spatially correlated groups via a learning-based clustering method based on the group-channel directions, and then separates spatially similar groups into different time slots. Finally, the multicast beamformers for the scheduled groups are obtained in each time slot by a computationally efficient method. Simulation results show that our proposed scheduling methods can effectively capture the level of spatial separation among groups to improve the minimum user throughput over the conventional approach that serves all groups in a single time slot or one group per time slot, and can be executed with low computational complexity.

翻译：下一代无线网络需要有效处理海量用户接入。本文研究具有大量活跃用户组的下行链路传输中联合组调度与多播波束成形问题。以最大化最差用户吞吐量为目标，我们提出一种三阶段方法以高效解决这一复杂的联合优化问题。在第一阶段，我们利用近期获得的最优多播波束成形结构来求解所有组的组信道方向。在第二阶段，我们提出两种低复杂度组调度算法，这些算法顺序确定每个时隙中的组子集以及所有组所需的总时隙数。第一种算法度量组间空间分离程度，并将能最大化最差用户速率的不相似组选择至同一时隙。与之相反，第二种算法首先通过基于组信道方向的机器学习聚类方法识别空间相关组，随后将空间相似的组分配至不同时隙。最后，每个时隙中已调度组的多播波束成形器通过一种计算高效的方法获得。仿真结果表明，相较于在单个时隙内服务所有组或每个时隙仅服务一组的传统方案，我们提出的调度方法能有效捕获组间空间分离程度以提升最差用户吞吐量，且能以较低计算复杂度执行。