Learning-Based Multiuser Scheduling in MIMO-OFDM Systems with Hybrid Beamforming

2025-06-09

AI Summary

This paper addresses the multi-user scheduling challenge in mmWave MIMO-OFDM downlink systems, where hybrid beamforming is constrained by the limited number of RF chains, aiming to maximize long-term proportional-fair spectral efficiency. We propose a two-timescale joint optimization framework: analog beamformers are fixed at the long timescale to match channel statistics, while user scheduling and digital precoding are jointly optimized dynamically at the short timescale. To this end, we introduce a novel combinatorial scheduling method that integrates greedy/sorting heuristics with supervised machine learning—achieving tunable performance-complexity trade-offs with low computational overhead. Experimental results demonstrate that the proposed scheme significantly improves both spectral efficiency and long-term fairness, and autonomously selects the optimal scheduling strategy under varying channel conditions and user loads.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Hardware-aware ML
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

We investigate the multiuser scheduling problem in multiple-input multiple-output (MIMO) systems using orthogonal frequency division multiplexing (OFDM) and hybrid beamforming in which a base station (BS) communicates with multiple users over millimeter wave (mmWave) channels in the downlink. Improved scheduling is critical for enhancing spectral efficiency and the long-term performance of the system from the perspective of proportional fairness (PF) metric in hybrid beamforming systems due to its limited multiplexing gain. Our objective is to maximize PF by properly designing the analog and digital precoders within the hybrid beamforming and selecting the users subject to the number of radio frequency (RF) chains. Leveraging the characteristics of mmWave channels, we apply a two-timescale protocol. On a long timescale, we assign an analog beam to each user. Scheduling the users and designing the digital precoder are done accordingly on a short timescale. To conduct scheduling, we propose combinatorial solutions, such as greedy and sorting algorithms, followed by a machine learning (ML) approach. Our numerical results highlight the trade-off between the performance and complexity of the proposed approaches. Consequently, we show that the choice of approach depends on the specific criteria within a given scenario.

Problem

• Optimize multiuser scheduling in MIMO-OFDM hybrid beamforming systems
• Maximize proportional fairness with analog-digital precoder design constraints
• Balance performance-complexity trade-offs using combinatorial and ML approaches

Innovation

• Hybrid beamforming for MIMO-OFDM systems
• Two-timescale protocol for user scheduling
• Machine learning for multiuser scheduling optimization