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.

翻译：我们考虑一个多用户混合波束赋形系统，其中复用增益受限于基站（BS）采用的少量射频链。为了更自由地最大化复用增益，基站最好在每个调度时刻选择并服务部分用户，而非始终服务所有用户。我们采用一种结合毫米波特性的双时间尺度协议：在长时间尺度上为每个用户选择模拟波束，在短时间尺度上基于所选模拟波束进行用户传输选择。用户选择的目标是最大化传统的比例公平（PF）指标。然而，由于选定用户的模拟波束间存在干扰，该最大化问题并非易事。我们首先定义了贪心算法和"top-k"算法，进而提出一种基于机器学习（ML）的用户选择算法，以在PF性能与计算时间之间实现高效折中。通过仿真，我们从多维度分析了基于ML算法的性能，并证明相较于同类算法，该方案在性能层面提供了高效折中。