Study of Multiuser Scheduling Based on User Satisfaction for MU-MIMO Systems

2026-08-15

AI Summary

This study addresses the challenges of high computational complexity and fairness trade-offs in MU-MIMO scheduling by proposing a User Satisfaction-based Scheduling Algorithm (US-SA). The method transforms high-dimensional combinatorial optimization into efficient sub-problems through the construction of low-dimensional subgrouping matrices and a satisfied-user elimination mechanism. Experimental results demonstrate that US-SA achieves performance comparable to optimal exhaustive search while significantly reducing computational overhead. Furthermore, it outperforms existing mainstream schemes in throughput, spectral efficiency, and fairness, effectively balancing system performance with user experience.

Abstract

Scheduling in multiuser multiple input multiple output (MU-MIMO) systems is essential for efficient resource allocation and overall performance enhancement. In this work, a multiuser scheduling problem is formulated to maximize the product of user equipments' (UEs) aggregate satisfactions, which maintains user fairness. Solving such a combinatorial problem using exhaustive search (EX), which requires evaluating all possible multiuser groups within a massive number of resource blocks (RBs), is prohibitive. Instead, we propose an efficient users' satisfaction based scheduling approach (US-SA). In our US-SA, a low dimension sub-grouping matrix is constructed {at each frame}, which is used to schedule the best multiuser group in each time slot; satisfied users are eliminated from the scheduling process. Our US-SA performs close to the optimal EX method in terms of satisfaction, transmitted data amount, spectral efficiency, latency, and fairness with lower computational cost. Moreover, our experiments demonstrate that the proposed scheme outperforms competing techniques.

Problem

• MU-MIMO
• Multiuser Scheduling
• User Satisfaction
• Fairness
• Combinatorial Optimization

Innovation

• User Satisfaction
• MU-MIMO Scheduling
• Low-dimension Sub-grouping Matrix
• Computational Efficiency
• User Fairness