  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.
