毫米波混合波束形成系统中基于学习的自适应用户选择
Learning-Based Adaptive User Selection in Millimeter Wave Hybrid Beamforming Systems
摘要：
我们考虑一个多用户混合波束成形系统，其中的复用增益是有限的在基站（BS）采用的RF链的数量少。为了允许最大化复用增益的更大自由度，如果BS在每个调度时刻选择并服务一些用户，而不是一直服务所有用户，则会更好。我们采用了一个双时标协议，考虑到毫米波的特性，其中在长时标的模拟波束选择每个用户，并在短时标的用户选择传输的基础上选择的模拟波束。用户选择的目标是最大化传统的比例公平（PF）度量。然而，由于所选用户的模拟波束之间的干扰，这种最大化是不平凡的。我们首先定义了一个贪婪算法和一个“top-k”算法，然后提出了一个基于机器学习（ML）的用户选择算法，以提供一个有效的PF性能和计算时间之间的权衡。在整个模拟中，我们分析了各种指标下的ML为基础的算法的性能，并表明，它提供了一个有效的权衡性能相比，同行。
英文摘要：
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.
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