An Optimization-Based User Scheduling Framework for Multiuser MIMO Systems

2026-03-03

AI Summary

Existing user scheduling approaches in multi-user MIMO systems predominantly rely on greedy algorithms, which struggle to achieve global resource optimization and consequently suffer from inadequate interference suppression and limited quality of service. This work proposes a global user scheduling framework based on approximate solutions to non-convex optimization problems. By jointly optimizing the subset of concurrently scheduled users in each time slot, the framework overcomes the limitations of conventional greedy strategies while accommodating diverse objective functions and resource occupancy constraints. The method is applicable to both millimeter-wave and sub-6 GHz cell-free massive MIMO scenarios and demonstrates significantly superior performance compared to existing algorithms, closely approaching the optimal results attainable by exhaustive search.

Technology Category

• Planning, Routing, and Scheduling: Scheduling
• Search and Optimization: Non-convex Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Resource allocation is a key factor in multiuser (MU) multiple-input multiple-output (MIMO) wireless systems to provide high quality of service to all user equipments (UEs). In congested scenarios, UE scheduling enables UEs to be distributed over time, frequency, or space in order to mitigate inter-UE interference. Many existing UE scheduling methods rely on greedy algorithms, which fail at treating the resource-allocation problem globally. In this work, we propose a UE scheduling framework for MU-MIMO wireless systems that approximately solves a nonconvex optimization problem that treats scheduling globally. Our UE scheduling framework determines subsets of UEs that should transmit simultaneously in a given resource slot and is flexible in the sense that it (i) supports a variety of objective functions (e.g., post-equalization mean squared error, capacity, and achievable sum rate) and (ii) enables precise control over the minimum and maximum number of resources the UEs should occupy. We demonstrate the efficacy of our UE scheduling framework for millimeter-wave massive MU-MIMO and sub-6-GHz cell-free massive MU-MIMO systems, and we show that it outperforms existing scheduling algorithms while approaching the performance of an exhaustive search.

Problem

• multiuser MIMO
• user scheduling
• resource allocation
• interference mitigation
• quality of service

Innovation

• user scheduling
• nonconvex optimization
• MU-MIMO
• resource allocation
• massive MIMO