This paper demonstrates the use of deep learning and time series data generated from user equipment (UE) beam measurements and positions collected by the base station (BS) to enable handoffs between beams that belong to the same or different BSs. We propose the use of long short-term memory (LSTM) recurrent neural networks with three different approaches and vary the number of lookbacks of the beam measurements to study the performance of the prediction used for the proactive beam handoff. Simulations show that while UE positions can improve the prediction performance, it is only up to a certain point. At a sufficiently large number of lookbacks, the UE positions become irrelevant to the prediction accuracy since the LSTMs are able to learn the optimal beam based on implicitly defined positions from the time-defined trajectories.

翻译：本文展示了利用深度学习技术，结合基站收集的用户设备（UE）波束测量与位置数据生成的时间序列，实现同一或不同基站之间波束的切换。我们提出采用长短期记忆（LSTM）循环神经网络，基于三种不同方法，通过改变波束测量的回溯次数，研究主动式波束切换预测性能。仿真结果表明：UE位置虽能提升预测性能，但存在阈值效应。当回溯次数足够大时，UE位置对预测精度不再产生影响，因为LSTM可通过时间轨迹中隐含的位置特征自主学习最优波束。