Fast Group Scheduling for Downlink Large-Scale Multi-Group Multicast Beamforming

TL;DR

Simulation results show that the 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.

Abstract

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.

Figures (5)

• Figure 1: MGMS-GSS: Average number of time slots $T$ vs. semi-orthogonality threshold $\alpha$.
• Figure 3: Convergence behavior of GSC (Algorithm \ref{['alg:GSC']}): Relative difference $\|{\bf c}^{(l+1)}_r - {\bf c}^{(l)}_r\|$ vs. the iterations for cluster $1$ ($\tau=0.7$).
• Figure 5: MGMS-GSC: CDF of the number of groups $G_t$ per time slot ($\tau=0.7$).
• Figure 6: Average minimum user throughput vs. $\alpha$.
• Figure 8: Average minimum user throughput using optimal $\alpha^{\star}$ or $\tau^{\star}$ vs. $N$.

Theorems & Definitions (4)

• Definition 1: Semi-orthogonality
• Remark 1
• Remark 2
• Remark 3