# Learning-Based Adaptive User Selection in Millimeter Wave Hybrid Beamforming Systems

arXiv: https://arxiv.org/abs/2302.08240 · HTML: https://mlsanity.com/paper/2302.08240/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 13 references that are in the map.
- First posted on arXiv 16 Feb 2023.

## Builds on (most cited first, 1 of 1)

- [Millimeter Wave Channel Modeling and Cellular Capacity Evaluation](https://mlsanity.com/paper/1312.4921/) (2013)

## Built on by (most cited first, 2 of 2)

- [Learning-Based Multiuser Scheduling in MIMO-OFDM Systems with Hybrid Beamforming](https://mlsanity.com/paper/2506.08263/) (2025)
- [Enhancing User Throughput in Multi-panel mmWave Radio Access Networks for Beam-based MU-MIMO Using a DRL Method](https://mlsanity.com/paper/2603.02745/) (2026)

## Abstract (arXiv)

We consider a multi-user hybrid beamforming system, where the multiplexing gain is limited by the small number of RF chains employed at the base station (BS). To allow greater freedom for maximizing the multiplexing gain, it is better if the BS selects and serves some of the users at each scheduling instant, rather than serving all the users all the time. We adopt a two-timescale protocol that takes into account the mmWave characteristics, where at the long timescale an analog beam is chosen for each user, and at the short timescale users are selected for transmission based on the chosen analog beams. The goal of the user selection is to maximize the traditional Proportional Fair (PF) metric. However, this maximization is non-trivial due to interference between the analog beams for selected users. We first define a greedy algorithm and a "top-k" algorithm, and then propose a machine learning (ML)-based user selection algorithm to provide an efficient trade-off between the PF performance and the computation time. Throughout simulations, we analyze the performance of the ML-based algorithms under various metrics, and show that it gives an efficient trade-off in performance as compared to counterparts.