论文标题
带有方向和RSRP的光束管理使用深度学习超越5G系统
Beam Management with Orientation and RSRP using Deep Learning for Beyond 5G Systems
论文作者
论文摘要
梁管理（BM），即查找和维护合适的发射和接收光束对的过程可能具有挑战性，尤其是在高度动态的情况下。侧面传感器的侧面信息（例如，方向）可以帮助用户设备（UE）BM。在这项工作中，我们使用来自惯性测量单元（IMU）的定向信息进行有效的BM。我们使用数据驱动的策略，该策略将接收到的参考信号（RSRP）与使用复发性神经网络（RNN）的方向信息融合在一起。仿真结果表明，所提出的策略的性能要比常规BM和方向辅助的BM策略好得多，该策略在另一项研究中利用了粒子过滤器。具体而言，提出的数据驱动策略可提高梁预测准确性高达34％，并在UE方向迅速变化时，将RSRP的平均值提高到4.2 dB。
Beam management (BM), i.e., the process of finding and maintaining a suitable transmit and receive beam pair, can be challenging, particularly in highly dynamic scenarios. Side-information, e.g., orientation, from on-board sensors can assist the user equipment (UE) BM. In this work, we use the orientation information coming from the inertial measurement unit (IMU) for effective BM. We use a data-driven strategy that fuses the reference signal received power (RSRP) with orientation information using a recurrent neural network (RNN). Simulation results show that the proposed strategy performs much better than the conventional BM and an orientation-assisted BM strategy that utilizes particle filter in another study. Specifically, the proposed data-driven strategy improves the beam-prediction accuracy up to 34% and increases mean RSRP by up to 4.2 dB when the UE orientation changes quickly.
