Operating Multi-User Massive MIMO Networks: Trade-Off Between Performance and Runtime

While multi-user (MU) massive MIMO is a critical technology for next generation wireless systems, its complexity poses significant operational challenges as it entails several processes. These include user selection, precoding, power distribution among the users, and Modulation and Coding Scheme (MCS) selection. While many studies have been conducted on MU-MIMO, most have made invalid assumptions (e.g., every non-zero signal received at a user yields a non-zero rate) or excluded some essential steps (e.g., MCS selection). We revisit the problem of operating a single-cell massive MIMO network with zero-forcing precoding, and develop real-time network operation algorithms. First, we relax the real-time constraint and perform an offline study to obtain a target performance for online algorithms. The joint problem can be solved exactly offline for small to medium size settings using branch-reduce-and-bound. For larger settings, we note that, given a choice of user selection, the problem reduces to a power distribution problem that can be solved exactly. Thus, the joint problem reduces to a search over user-sets where for each considered user-set, a power distribution problem is solved. We propose various search methods and evaluate their performance. For online operation, we leverage the problem structure to propose an algorithm based on three ideas: i) grouping, 2) MCS-aware power distribution, and 3) an iterative process to remove users that see a zero rate. The algorithm achieves 94% of the performance target set by the offline study results.