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 multicast with many active 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 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 approaches can effectively capture the level of spatial separation among groups for scheduling 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大规模用户接入。本文针对具有多个活跃组的下行多播场景，研究了联合分组调度与多播波束赋形的优化问题。以最大化用户最小吞吐量为目标，我们提出了一种三阶段方法以高效求解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赋形结构，为所有组获取组-信道方向。在第二阶段，我们提出两种低复杂度调度算法：该算法依次确定每个时隙中的组子集以及所有组所需的总时隙数。第一种算法通过衡量组间空间分离程度，将能够最大化最小用户速率的非相似组调度至同一时隙；而第二种算法则首先基于组-信道方向，通过基于学习的聚类方法识别空间相关组，再将空间相似组分配至不同时隙。最后，第三阶段通过计算高效的方法，为每个时隙中的已调度组生成多播波束赋形器。仿真结果表明，与所有组调度至单一或单个时隙的传统方法相比，所提方法能有效捕捉组间空间分离程度以优化调度策略，从而显著提升用户最小吞吐量，且具有较低的计算复杂度。