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

TL;DR

This paper addresses multiuser scheduling in MIMO-OFDM systems with hybrid beamforming at mmWave frequencies, proposing ML-based and combinatorial algorithms to optimize proportional fairness and system performance.

Contribution

It introduces a two-timescale scheduling protocol with ML and combinatorial algorithms for hybrid beamforming in mmWave MIMO-OFDM systems, enhancing spectral efficiency and fairness.

Findings

ML approach improves scheduling efficiency

Greedy algorithms offer a good trade-off between complexity and performance

Performance depends on specific scenario criteria

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.