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

TL;DR

This paper introduces a machine learning-based user selection method for millimeter wave hybrid beamforming systems, improving multiplexing gain and computational efficiency by balancing performance and complexity.

Contribution

It proposes a novel ML-based user selection algorithm tailored for mmWave systems, outperforming traditional greedy and top-k algorithms in efficiency and fairness.

Findings

ML-based algorithm achieves better performance-complexity trade-off.

The approach enhances multiplexing gain in multi-user mmWave systems.

Simulations confirm improved efficiency over conventional methods.

Abstract

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.