Beam alignment is a challenging and time-consuming process for millimeter wave (mmWave) systems, particularly as they trend towards higher carrier frequencies which require ever narrower beams. We propose a beam alignment method that is assisted by machine learning (ML), where we train ML models to predict the optimal access point (AP) and beam – or the best few candidates – for a user equipment (UE) given just its GPS coordinates, which can be fed back by the UE or estimated by the network using emerging localization techniques. We train and evaluate the models with data generated by a state-of-the-art commercial ray-tracing software in a realistic urban topology. Even with dynamic scatterers and imperfect UE coordinates, our proposed method can greatly reduce the search space (number of candidates) for finding the optimal AP and beam. For example, in a 28 GHz scenario with 64 beams, our method reduces the search space by approximately 4x for AP selection and over 10x for beam selection while achieving over 95% accuracy. We provide our dataset and models for ease of reproducing and extending our results, which suggest that UE localization coupled with suitably trained ML models can significantly speed up current beam alignment procedures.

中文翻译：

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毫米波系统的机器学习辅助光束对准


对于毫米波 (mmWave) 系统来说，光束对准是​​一个具有挑战性且耗时的过程，特别是当它们趋向于更高的载波频率时，需要更窄的光束。我们提出了一种由机器学习 (ML) 辅助的波束对准方法，在该方法中，我们训练 ML 模型来预测用户设备 (UE) 的最佳接入点 (AP) 和波束（或最好的几个候选者）（仅给出 GPS）坐标，可以由 UE 反馈或由网络使用新兴的定位技术估计。我们使用最先进的商业光线追踪软件在现实的城市拓扑中生成的数据来训练和评估模型。即使有动态散射体和不完美的 UE 坐标，我们提出的方法也可以大大减少寻找最佳 AP 和波束的搜索空间（候选数量）。例如，在具有 64 个波束的 28 GHz 场景中，我们的方法将 AP 选择的搜索空间减少了大约 4 倍，波束选择的搜索空间减少了 10 倍以上，同时实现了超过 95% 的准确度。我们提供数据集和模型，以便于重现和扩展我们的结果，这表明 UE 定位与经过适当训练的 ML 模型相结合可以显着加快当前的波束对准过程。